A Smart Chip Barrel Contents Status Monitoring System and Method Based on Intelligent Sensing

Abstract

This invention discloses a smart-chip bucket contents status monitoring system and method based on intelligent sensing, belonging to the field of intelligent sensing technology. The method includes: deploying a sensor array in the smart-chip bucket; when the rate of change of liquid height, the rate of change of pressure at the bottom of the bucket, or the rate of change of liquid mass exceeds a preset trigger threshold, collecting monitoring data from each sensor and transmitting it to an edge computing terminal; verifying the accuracy of the data based on physical correlations; when the verification result shows inaccurate data, dynamically adjusting the working weight of each sensor according to environmental parameters; when the physical rationality verification result does not meet preset requirements, automatically performing sensor optimization configuration; and classifying the data according to the final verification result based on its credibility level, and processing it accordingly. This invention improves the credibility of the monitoring data by using a cross-validation mechanism combined with physical correlations to verify data consistency.

Description

Technical Field

[0001] This invention relates to the field of intelligent sensing technology, specifically to a smart chip bucket content status monitoring system and method based on intelligent sensing. Background Technology

[0002] In the fields of industrial logistics and hazardous chemical transportation, monitoring the contents of intelligent transport containers is crucial for ensuring transportation safety. Traditional monitoring systems mainly rely on a single sensor (such as a level gauge or pressure sensor) for independent measurement, which has drawbacks such as low data reliability and insufficient compensation for environmental interference.

[0003] While existing technologies employ multi-sensor data fusion algorithms to improve monitoring accuracy, they rely on complex machine learning models, resulting in high computational resource consumption and poor model interpretability. Weighted average-based data verification methods fail to consider the coupling effects of sensor dynamic performance changes and environmental parameters, leading to significantly increased errors under complex conditions such as vibration and tilt. Furthermore, existing systems generally lack effective self-verification mechanisms, making it difficult to promptly identify and correct erroneous data when sensors drift or malfunction. Especially in cases of liquid density changes or phase transitions, traditional volume-mass conversion methods introduce systematic errors. Although attempts have been made to introduce environmental compensation coefficients, their fixed weight allocation mode cannot adapt to dynamic environmental changes during transportation. These technical deficiencies result in existing monitoring systems still exhibiting high false alarm rates and insufficient reliability in the transportation of sensitive materials such as chemical raw materials and liquid food. Summary of the Invention

[0004] The purpose of this invention is to provide a smart chip bucket contents status monitoring system and method based on intelligent sensing, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the state of contents in a smart bucket based on intelligent sensing, the method comprising:

[0006] A sensor array is deployed in the smart tank; the sensor array includes a tank bottom pressure sensor, an environmental monitoring sensor, a mass sensor, and a liquid level sensor;

[0007] When the rate of change of liquid height, the rate of change of pressure at the bottom of the tank, or the rate of change of liquid mass exceeds the preset trigger threshold, the monitoring data of each sensor is collected and transmitted to the edge computing terminal.

[0008] Based on the bottom pressure data, liquid mass data, liquid height data, and pre-stored liquid density parameters and the bottom area parameters of the smart tank, the accuracy of the bottom pressure data, liquid mass data, and liquid height data is verified through physical correlation.

[0009] When the verification results show that the data is inaccurate, the working weight of each sensor is dynamically adjusted according to the environmental parameters, the data is re-collected and physical correlation verification is performed; when the physical rationality verification results do not meet the preset requirements, sensor optimization configuration is automatically executed.

[0010] Based on the final verification results, the data is classified into confidence levels, and then further processed according to the confidence level to generate a data package containing sensor combination information, environmental compensation parameters, and data quality identifiers.

[0011] According to the above scheme, the pressure sensor collects the pressure data at the bottom of the tank and the rate of change of the pressure at the bottom of the tank; the mass sensor collects the liquid mass data and the rate of change of the liquid mass; the liquid height sensor collects the liquid height data and the rate of change of the liquid height; the environmental monitoring sensors include a temperature sensor, a vibration sensor and a tilt sensor, the temperature sensor collects the ambient temperature data, the vibration sensor collects the vibration intensity data, and the tilt sensor collects the tilt angle data.

[0012] When the rate of change of liquid height, the rate of change of pressure at the bottom of the container, or the rate of change of liquid mass exceeds a preset trigger threshold, a data acquisition command is automatically initiated. This command is simultaneously sent to the pressure sensor, environmental monitoring sensor, mass sensor, and liquid height sensor. The pressure sensor responds to the command by outputting the current and historical bottom pressure data; the mass sensor responds by outputting the current and historical liquid mass data; the liquid height sensor responds by outputting the current and historical liquid height data; and the environmental monitoring sensor responds by outputting the current ambient temperature, vibration intensity, and tilt angle data.

[0013] Each sensor encapsulates the collected monitoring data according to the data frame format of the preset communication protocol and transmits it to the edge computing terminal; after receiving the data, the edge computing terminal performs data integrity verification and timestamp alignment.

[0014] According to the above scheme, the accuracy of the data on the pressure at the bottom of the tank, the liquid mass data, and the liquid height data is verified through physical correlation, including:

[0015] Based on the liquid height data h and the liquid density ρ, the theoretical pressure value at the bottom of the container is calculated using the following formula:

[0016] P l =ρ×g×h;

[0017] Among them, P l The value is expressed as the theoretical pressure at the bottom of the bucket; g represents the acceleration due to gravity.

[0018] The pressure data P at the bottom of the bucket collected by the pressure sensor sCompared with the theoretical pressure value P at the bottom of the bucket l Perform a comparison;

[0019] Based on the liquid height data h, the internal bottom area S of the smart tank, and the liquid density ρ, the theoretical mass value of the liquid is calculated using the following formula:

[0020] m l =ρ×S×h;

[0021] Where, m l Expressed as the theoretical mass value of the liquid;

[0022] The liquid mass data m collected by the mass sensor s With respect to the theoretical mass value m of the liquid l Perform a comparison;

[0023] Based on liquid mass data m s The theoretical height of the liquid is calculated using the following formula: ρ is the liquid density, and S is the internal bottom area of ​​the smart core tank.

[0024] h l =m s / (ρ×S);

[0025] Among them, h l Represented as the theoretical height of the liquid;

[0026] The liquid height data h collected by the liquid height sensor s With respect to the theoretical height value h of the liquid l Compare them.

[0027] Based on the above scheme, the deviation rate δP = |P s -P l | / P l ×100%, Deviation rate of liquid quality data δm=|m s -m l | / m l ×100%, Deviation rate of liquid height data δh=|h s -h l | / h l ×100%;

[0028] Preset pressure deviation threshold δP0, mass deviation threshold δm0, and height deviation threshold δh0;

[0029] Pressure, mass, and height parameters are set independently. The pressure deviation threshold δP0 is determined based on the rated working pressure range of the smart tank and the measurement accuracy level of the pressure sensor. The mass deviation threshold δm0 is determined based on the rated mass range of the contents and the range error range of the mass sensor. The height deviation threshold δh0 is determined based on the internal height dimensions of the smart tank and the resolution parameters of the liquid height sensor.

[0030] When δP≤δP0, δm≤δm0 and δh≤δh0, the data on the bottom pressure, liquid mass, and liquid height are considered accurate; when any deviation rate exceeds the corresponding threshold, the corresponding data is considered inaccurate.

[0031] According to the above scheme, the environmental parameters include ambient temperature data, vibration intensity data, and tilt angle data; the real-time reliability coefficient of each sensor is calculated based on the environmental parameters, and a sensor weight allocation model is established.

[0032] Dynamic weight values ​​were assigned to pressure sensors, mass sensors, and liquid height sensors based on the reliability coefficient, with sensors that are more affected by the environment being assigned lower weights.

[0033] The adjusted duty cycle of the pressure sensor is calculated using the following formula:

[0034] Wp=Wp0×(1-Kt p ×Dt-Kv p ×Dv-Kθ p ×Dθ);

[0035] Where Wp0 represents the initial working weight of the pressure sensor; Kt p The influence coefficient of ambient temperature on the pressure sensor is represented by Dt; the deviation of ambient temperature is represented by Kv. p The coefficient representing the influence of vibration intensity on the pressure sensor is denoted as Dv; the deviation of vibration intensity is denoted as Kθ. p The coefficient representing the influence of the tilt angle on the pressure sensor; Dθ represents the tilt angle deviation.

[0036] The adjusted working weight of the mass sensor is calculated using the following formula:

[0037] Wm=Wm0×(1-Kt m ×Dt-Kv m ×Dv-Kθ m ×Dθ);

[0038] Where Wm0 represents the initial working weight of the mass sensor; Kt m This is expressed as the influence coefficient of ambient temperature on the mass sensor; Kv m Kθ represents the influence coefficient of vibration intensity on the mass sensor.m This is expressed as the influence coefficient of the tilt angle on the mass sensor;

[0039] The adjusted duty cycle of the liquid level sensor is calculated using the following formula:

[0040] Wh = Wh0 × (1 - Kt) h ×Dt-Kv h ×Dv-Kθ h ×Dθ);

[0041] Where Wh0 represents the initial working weight of the liquid height sensor; Kt h This is expressed as the influence coefficient of ambient temperature on the liquid height sensor; Kv h This is expressed as the influence coefficient of vibration intensity on the liquid height sensor; Kθ h This is expressed as the influence coefficient of the tilt angle on the liquid height sensor;

[0042] The adjusted work weights must satisfy Wp≥0.1, Wm≥0.1, and Wh≥0.1; if the calculation result is lower than the lower limit, the corresponding lower limit value shall be used.

[0043] The ambient temperature reference value is the rated temperature for normal operation of the Smart Core Tank, determined based on the physicochemical properties of the contents; the vibration intensity reference value is the inherent vibration intensity of the Smart Core Tank's installation environment, determined based on the equipment's operational stability requirements; the tilt angle reference value is the allowable horizontal installation error of the Smart Core Tank, determined based on the influence coefficient of the container structure on liquid level measurement.

[0044] The edge computing terminal sends acquisition commands to the corresponding sensors according to the adjusted workload weights. The pressure sensor responds to the command and outputs the bottom pressure data, the mass sensor responds to the command and outputs the liquid mass data, and the liquid height sensor responds to the command and outputs the liquid height data.

[0045] According to the above scheme, the edge computing terminal extracts the response characteristic parameters of each sensor during the physical rationality verification process. The response characteristic parameters include the stability of the data acquisition cycle, the signal transmission delay duration, and the fluctuation range of the detection value.

[0046] Based on response characteristic parameters, abnormal sensor conditions are identified, including acquisition accuracy deviating from the standard range, response speed exceeding the preset threshold, and anti-interference capability being lower than the benchmark level.

[0047] Based on the abnormal situation of the sensor, the edge computing terminal sends parameter adjustment instructions to the corresponding sensor. The parameter adjustment instructions include adjusting the sensor sampling frequency, modifying the sensor working mode, updating the sensor calibration parameters, switching the sensor data transmission channel, and reconfiguring the sensor networking mode.

[0048] After the parameters are adjusted, the edge computing terminal controls each sensor to restart the data acquisition process and simultaneously starts the environmental monitoring sensor to continuously monitor the adjusted working environment to ensure that the working environment of the sensor under the new parameter settings meets the preset conditions.

[0049] If the physical rationality verification results fail to meet the preset requirements for three consecutive times, the backup sensor switching mechanism is triggered; if there is no backup sensor to switch to, a fault alarm message is generated, which includes the abnormal sensor ID, historical adjustment records and current environmental parameters.

[0050] The edge computing terminal stores the sensor optimization configuration process information in the configuration log. The process information includes the response characteristic parameters before parameter adjustment, the parameter adjustment command content, the response status of each sensor to the adjustment command, and the adjusted working environment parameters recorded by the environmental monitoring sensor.

[0051] Based on the above scheme, a credibility grading model is constructed using the physical rationality verification results, parameter adjustment records after sensor optimization, and environmental monitoring data. The credibility grading model includes data consistency indicators, sensor stability indicators, and environmental interference indicators. The comprehensive credibility score of the data is calculated through a weighted summation, as shown in the following formula:

[0052] C = w1 × A + w2 × B + w3 × E;

[0053] Where C represents the overall credibility score; w1 represents the weight coefficient of the data consistency index, w2 represents the weight coefficient of the sensor stability index, w3 represents the weight coefficient of the environmental interference index; A represents the data consistency score, which is determined based on the comparison result of the data deviation rate and the preset threshold; B represents the sensor stability score, which is determined based on the fluctuation range of the sensor response characteristic parameters; and E represents the environmental interference score, which is determined based on the degree of deviation of the environmental parameters from the benchmark value.

[0054] For example: Temperature reference value: clearly stated based on the boiling point / freezing point of the contents or industry standards, such as the requirement of 20±5℃ for chemical storage tanks; Vibration reference value: referencing equipment installation specifications, such as the limits on mechanical vibration levels in ISO 10816-3; Inclination reference value: indicating the permissible engineering error for horizontal installation of the container, such as ±0.5°;

[0055] w1 is determined based on the weight of the impact of data deviation on subsequent applications; w2 is determined based on the contribution of the sensor's long-term working stability to the system reliability; w3 is determined based on the proportion of the impact of environmental interference on measurement error; and w1+w2+w3=1;

[0056] Based on the range of the comprehensive credibility score, the data credibility is divided into three levels: when the comprehensive credibility score is in the first preset range, it is judged as Level 1 credibility; when the comprehensive credibility score is in the second preset range, it is judged as Level 2 credibility; and when the comprehensive credibility score is in the third preset range, it is judged as Level 3 credibility.

[0057] The first preset range is determined based on industry-level high-precision monitoring standards and the maximum permissible error of the content measurement; the second preset range is determined based on industrial-level routine monitoring requirements and the acceptable error range of the content application; the third preset range is determined based on the minimum precision requirements of the basic monitoring scenario.

[0058] Level 1 reliability indicates that the data is highly consistent with the theoretical value, the sensor is operating stably, and the impact of environmental interference is minimal; Level 2 reliability indicates that the data is basically consistent with the theoretical value, the sensor's operating state fluctuates slightly, and the impact of environmental interference is within an acceptable range; Level 3 reliability indicates that the data has a identifiable deviation from the theoretical value, the sensor's operating stability is average, and the impact of environmental interference needs to be closely monitored.

[0059] According to the above scheme, for Level 1 reliability, the original collected values ​​are directly retained and marked as baseline data; for Level 2 reliability, the original data are corrected according to the environmental compensation algorithm, and the correction process references the temperature, vibration and tilt parameters recorded by the environmental monitoring sensors; for Level 3 reliability, deviation explanations and correction suggestions are added, and the applicable scope of the data is marked.

[0060] According to the above scheme, the sensor combination information includes the model identification, deployment location code, and working mode parameters of the pressure sensor, mass sensor, and liquid height sensor that are currently in an active state; the environmental compensation parameters include the temperature compensation coefficient, vibration attenuation coefficient, and tilt correction value; the data quality identification includes the reliability level identification, comprehensive reliability score, data acquisition period, and sensor status code, and the sensor status code is used to indicate the working stability of each sensor during the data acquisition process.

[0061] A smart chip bucket contents status monitoring system based on intelligent sensing, the system includes a data acquisition module, a data preprocessing module, a data verification module, a credibility grading module and a data output module;

[0062] The data acquisition module includes a pressure sensor module, a mass sensor module, a liquid level sensor module, and an environmental monitoring sensor module; the pressure sensor module is used to collect bottom pressure data and pressure change rate data; the mass sensor module is used to collect liquid mass data and mass change rate data; and the liquid level sensor module is used to collect liquid height data and height change rate data.

[0063] The data preprocessing module performs format verification on the raw data transmitted by the data acquisition module and aligns the synchronous data collected by different sensors according to a unified time reference.

[0064] The data verification module includes a cross-validation module, a weight adjustment module, and a configuration optimization module. The cross-validation module verifies the accuracy of the data collected by the data acquisition module through physical correlation. The weight adjustment module calculates the real-time reliability coefficient of each sensor based on the data collected by the environmental sensing module and dynamically adjusts the working weight of the sensor.

[0065] The credibility grading module evaluates the overall credibility score based on data verification results, sensor optimization records, and environmental data, divides the data into different credibility levels, and performs differentiated processing on data of different credibility levels.

[0066] The data output module integrates sensor combination information, environmental compensation parameters, and data quality identifiers to generate standardized data packets.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] 1. This invention solves the problem of measurement deviation by a single sensor by using a cross-validation mechanism of pressure, mass and liquid level sensors, combined with physical correlation to verify data consistency, thereby improving the reliability of monitoring data;

[0069] 2. This invention dynamically adjusts the sensor's working weight based on environmental parameters, reducing the impact of environmental interference on measurements and ensuring data stability under complex working conditions;

[0070] 3. This invention identifies anomalies and automatically adjusts the configuration by using sensor response characteristic parameters, which can optimize the sensor without manual intervention and improve maintenance efficiency. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating the steps of a smart chip bucket content status monitoring method based on intelligent sensing according to the present invention.

[0072] Figure 2 This is a schematic diagram of the structure of a smart chip bucket content status monitoring system based on intelligent sensing according to the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Example: Figures 1-2 As shown, the present invention provides a technical solution: a method for monitoring the state of contents in a smart bucket based on intelligent sensing, the method comprising:

[0075] A sensor array is deployed in the smart tank; the sensor array includes a tank bottom pressure sensor, an environmental monitoring sensor, a mass sensor, and a liquid level sensor;

[0076] Specifically, the pressure sensor collects data on the pressure at the bottom of the container and the rate of change of the pressure at the bottom of the container; the mass sensor collects data on the mass of the liquid and the rate of change of the mass of the liquid; the liquid height sensor collects data on the liquid height and the rate of change of the liquid height; the environmental monitoring sensors include a temperature sensor, a vibration sensor, and a tilt sensor, with the temperature sensor collecting ambient temperature data, the vibration sensor collecting vibration intensity data, and the tilt sensor collecting tilt angle data.

[0077] For example: Bottom pressure data: 22.5 kPa; Bottom pressure change rate data: 0.25 kPa / s;

[0078] Liquid mass data: 420 kg; Liquid mass change rate data: 4 kg / s;

[0079] Liquid height data: 0.84m; Liquid height change rate data: 0.014m / s;

[0080] Ambient temperature data: 32℃; Vibration intensity data: 0.2g; Tilt angle data: 1.2°;

[0081] This is just an example and is not intended to be restrictive;

[0082] When the rate of change of liquid height, the rate of change of pressure at the bottom of the tank, or the rate of change of liquid mass exceeds the preset trigger threshold, the monitoring data of each sensor is collected and transmitted to the edge computing terminal.

[0083] Specifically, when the rate of change of liquid height, the rate of change of pressure at the bottom of the container, or the rate of change of liquid mass exceeds a preset trigger threshold, a data acquisition command is automatically initiated. This command is simultaneously sent to the pressure sensor, environmental monitoring sensor, mass sensor, and liquid height sensor. The pressure sensor responds to the command, outputting the current and historical bottom pressure data; the mass sensor responds, outputting the current and historical liquid mass data; the liquid height sensor responds, outputting the current and historical liquid height data; and the environmental monitoring sensor responds, outputting the current ambient temperature, vibration intensity, and tilt angle data. Each sensor encapsulates the collected monitoring data according to a preset communication protocol data frame format and transmits it to the edge computing terminal. Upon receiving the data, the edge computing terminal performs data integrity verification and timestamp alignment.

[0084] Based on the bottom pressure data, liquid mass data, liquid height data, and pre-stored liquid density parameters and the bottom area parameters of the smart tank, the accuracy of the bottom pressure data, liquid mass data, and liquid height data is verified through physical correlation.

[0085] Specifically, based on the liquid height data h and the liquid density ρ, the theoretical pressure value at the bottom of the container is calculated using the following formula:

[0086] P l =ρ×g×h;

[0087] Among them, P l The value is expressed as the theoretical pressure at the bottom of the bucket; g represents the acceleration due to gravity.

[0088] The pressure data P at the bottom of the bucket collected by the pressure sensor s Compared with the theoretical pressure value P at the bottom of the bucket l Perform a comparison;

[0089] Based on the liquid height data h, the internal bottom area S of the smart tank, and the liquid density ρ, the theoretical mass value of the liquid is calculated using the following formula:

[0090] m l =ρ×S×h;

[0091] Where, m l Expressed as the theoretical mass value of the liquid;

[0092] The liquid mass data m collected by the mass sensor s With respect to the theoretical mass value m of the liquid l Perform a comparison;

[0093] Based on liquid mass data m s The theoretical height of the liquid is calculated using the following formula: ρ is the liquid density, and S is the internal bottom area of ​​the smart core tank.

[0094] h l =m s / (ρ×S);

[0095] Among them, h l Represented as the theoretical height of the liquid;

[0096] The liquid height data h collected by the liquid height sensor s With respect to the theoretical height value h of the liquid l Compare them.

[0097] For example: the storage density of the smart chip bucket is 1000 kg / m³. 3 The liquid inside the smart chip tank has a bottom area of ​​0.5m². 2 Rated working pressure range: 0-30 kPa; maximum flow rate: 0.3 m³ / s. 3 / min;

[0098] Theoretical pressure value at the bottom of the bucket: P l =ρ×g×h=1000×9.8×0.84=8.232kPa;

[0099] Theoretical mass of liquid: m l =ρ×S×h=1000×0.5×0.84=420kg;

[0100] Theoretical height of liquid: h l =m s / (ρ×S);=420 / (1000×0.5)=0.84m;

[0101] Furthermore, the deviation rate δP = |P_bottom pressure data is calculated. s -P l | / P l ×100%, Deviation rate of liquid quality data δm=|m s -m l | / m l ×100%, Deviation rate of liquid height data δh=|h s -h l | / h l×100%; Preset pressure deviation thresholds δP0, mass deviation thresholds δm0, and height deviation thresholds δh0; Pressure, mass, and height parameters are set independently. The pressure deviation threshold δP0 is determined based on the rated working pressure range of the smart tank and the measurement accuracy level of the pressure sensor; the mass deviation threshold δm0 is determined based on the rated mass range of the contents and the range error range of the mass sensor; the height deviation threshold δh0 is determined based on the internal height dimensions of the smart tank and the resolution parameters of the liquid height sensor. When δP≤δP0, δm≤δm0, and δh≤δh0, the tank bottom pressure data, liquid mass data, and liquid height data are considered accurate. When any deviation rate exceeds the corresponding threshold, the corresponding data is considered inaccurate.

[0102] For example: Deviation thresholds: δP0 = 0.5% (based on rated pressure 30kPa × 0.5%, i.e. 0.15kPa); δm0 = 0.3% (based on maximum capacity 500kg × 0.3%, i.e. 1.5kg); δh0 = 0.2% (based on maximum height 1.2m × 0.2%, i.e. 0.24cm);

[0103] Pressure deviation rate δP = |22.5 - 8.232| / 8.232 × 100% ≈ 173%;

[0104] Quality deviation rate δm = |420 - 420| / 420 × 100% = 0%;

[0105] The height deviation rate δh = |0.84 - 0.84| / 0.84 × 100% = 0%;

[0106] δP (173%) > δP0 (0.5%), indicating that the pressure data is inaccurate;

[0107] When the verification results show that the data is inaccurate, the working weight of each sensor is dynamically adjusted according to the environmental parameters, the data is re-collected and physical correlation verification is performed; when the physical rationality verification results do not meet the preset requirements, sensor optimization configuration is automatically executed.

[0108] Specifically, environmental parameters include ambient temperature data, vibration intensity data, and tilt angle data; based on the environmental parameters, the real-time reliability coefficient of each sensor is calculated, and a sensor weight allocation model is established.

[0109] Dynamic weight values ​​were assigned to pressure sensors, mass sensors, and liquid height sensors based on the reliability coefficient, with sensors that are more affected by the environment being assigned lower weights.

[0110] The adjusted duty cycle of the pressure sensor is calculated using the following formula:

[0111] Wp=Wp0×(1-Kt p×Dt-Kv p ×Dv-Kθ p ×Dθ);

[0112] Where Wp0 represents the initial working weight of the pressure sensor; Kt p The influence coefficient of ambient temperature on the pressure sensor is represented by Dt; the deviation of ambient temperature is represented by Kv. p The coefficient representing the influence of vibration intensity on the pressure sensor is denoted as Dv; the deviation of vibration intensity is denoted as Kθ. p The coefficient representing the influence of the tilt angle on the pressure sensor; Dθ represents the tilt angle deviation.

[0113] The adjusted working weight of the mass sensor is calculated using the following formula:

[0114] Wm=Wm0×(1-Kt m ×Dt-Kv m ×Dv-Kθ m ×Dθ);

[0115] Where Wm0 represents the initial working weight of the mass sensor; Kt m This is expressed as the influence coefficient of ambient temperature on the mass sensor; Kv m Kθ represents the influence coefficient of vibration intensity on the mass sensor. m This is expressed as the influence coefficient of the tilt angle on the mass sensor;

[0116] The adjusted duty cycle of the liquid level sensor is calculated using the following formula:

[0117] Wh = Wh0 × (1 - Kt) h ×Dt-Kv h ×Dv-Kθ h ×Dθ);

[0118] Where Wh0 represents the initial working weight of the liquid height sensor; Kt h This is expressed as the influence coefficient of ambient temperature on the liquid height sensor; Kv h This is expressed as the influence coefficient of vibration intensity on the liquid height sensor; Kθ h This is expressed as the influence coefficient of the tilt angle on the liquid height sensor;

[0119] The ambient temperature reference value is the rated temperature for normal operation of the Smart Core Tank, determined based on the physicochemical properties of the contents; the vibration intensity reference value is the inherent vibration intensity of the Smart Core Tank's installation environment, determined based on the equipment's operational stability requirements; the tilt angle reference value is the allowable horizontal installation error of the Smart Core Tank, determined based on the influence coefficient of the container structure on liquid level measurement.

[0120] For example: the ambient temperature baseline is 25℃, the current temperature is 32℃, Dt=|32-25| / 25=0.28;

[0121] The vibration intensity benchmark value is 0.1g, the current value is 0.2g, and Dv = |0.2 - 0.1| / 0.1 = 1;

[0122] The baseline tilt angle is 1°, currently 1.2°, Dθ = |1.2 - 1| / 1 = 0.2;

[0123] The influence coefficients and weights are adjusted, with the initial weights preset as Wp0=0.4, Wm0=0.3, and Wh0=0.3.

[0124] Adjusted duty weight of the pressure sensor:

[0125] Wp=0.4×(1-0.3×0.28-0.4×1-0.2×0.2)=0.4×0.46=0.184;

[0126] Kt p =0.3, Kv p =0.4, Kθ p =0.2;

[0127] Adjusted working weights for the mass sensor:

[0128] Wm=0.3×(1-0.1×0.28-0.2×1-0.1×0.2)=0.3×0.75=0.225;

[0129] Kt m =0.1, Kv m =0.2, Kθ m =0.1;

[0130] Adjusted duty weights for the liquid height sensor:

[0131] Wh=0.3×(1-0.2×0.28-0.3×1-0.5×0.2)=0.3×0.59=0.177;

[0132] Kt h =0.2, Kv h =0.3, Kθ h =0.5;

[0133] After normalization: Wp'=0.184 / 0.586≈0.314; Wm'≈0.384; Wh'≈0.302;

[0134] The edge computing terminal sends acquisition commands to the corresponding sensors according to the adjusted workload weights. The pressure sensor responds to the command and outputs the bottom pressure data, the mass sensor responds to the command and outputs the liquid mass data, and the liquid height sensor responds to the command and outputs the liquid height data.

[0135] According to the above scheme, the edge computing terminal extracts the response characteristic parameters of each sensor during the physical rationality verification process. The response characteristic parameters include the stability of the data acquisition cycle, the signal transmission delay duration, and the fluctuation range of the detection value.

[0136] Based on response characteristic parameters, abnormal sensor conditions are identified, including acquisition accuracy deviating from the standard range, response speed exceeding the preset threshold, and anti-interference capability being lower than the benchmark level.

[0137] Based on the abnormal situation of the sensor, the edge computing terminal sends parameter adjustment instructions to the corresponding sensor. The parameter adjustment instructions include adjusting the sensor sampling frequency, modifying the sensor working mode, updating the sensor calibration parameters, switching the sensor data transmission channel, and reconfiguring the sensor networking mode.

[0138] After the parameters are adjusted, the edge computing terminal controls each sensor to restart the data acquisition process and simultaneously starts the environmental monitoring sensor to continuously monitor the adjusted working environment to ensure that the working environment of the sensor under the new parameter settings meets the preset conditions.

[0139] The edge computing terminal stores the sensor optimization configuration process information in the configuration log. The process information includes the response characteristic parameters before parameter adjustment, the parameter adjustment command content, the response status of each sensor to the adjustment command, and the adjusted working environment parameters recorded by the environmental monitoring sensor.

[0140] Based on the final verification results, the data is classified into confidence levels, and then further processed according to the confidence level to generate a data package containing sensor combination information, environmental compensation parameters, and data quality identifiers.

[0141] Specifically, a credibility grading model is constructed based on the physical rationality verification results, parameter adjustment records after sensor optimization, and environmental monitoring data. The credibility grading model includes data consistency indicators, sensor stability indicators, and environmental interference indicators. The comprehensive credibility score of the data is calculated through a weighted summation, as shown in the following formula:

[0142] C = w1 × A + w2 × B + w3 × E;

[0143] Where C represents the overall credibility score; w1 represents the weight coefficient of the data consistency index, w2 represents the weight coefficient of the sensor stability index, w3 represents the weight coefficient of the environmental interference index; A represents the data consistency score, which is determined based on the comparison result of the data deviation rate and the preset threshold; B represents the sensor stability score, which is determined based on the fluctuation range of the sensor response characteristic parameters; and E represents the environmental interference score, which is determined based on the degree of deviation of the environmental parameters from the benchmark value.

[0144] w1 is determined based on the weight of the impact of data deviation on subsequent applications; w2 is determined based on the contribution of the sensor's long-term working stability to the system reliability; w3 is determined based on the proportion of the impact of environmental interference on measurement error; and w1+w2+w3=1;

[0145] For example: Data consistency score A=0.8; sensor stability score B=0.7; environmental interference score E=0.6; weighting coefficients w1=0.5, w2=0.3, w3=0.2, C=0.5×0.8+0.3×0.7+0.2×0.6=0.73; it falls within the second preset range (0.6-0.8], and is judged as Level 2 reliable.

[0146] Furthermore, based on the range of the comprehensive credibility score, the data credibility is divided into three levels: when the comprehensive credibility score is in the first preset range, it is judged as Level 1 credibility; when the comprehensive credibility score is in the second preset range, it is judged as Level 2 credibility; and when the comprehensive credibility score is in the third preset range, it is judged as Level 3 credibility. The first preset range is determined based on industry-level high-precision monitoring standards and the maximum permissible error of content measurement; the second preset range is determined based on industrial-level routine monitoring requirements and the acceptable error range of content application; and the third preset range is determined based on the minimum accuracy requirements of basic monitoring scenarios.

[0147] For example: First preset interval: (0.8, 1.0]; Second preset interval: (0.6-0.8]; Third preset interval: (0.3-0.6];

[0148] Level 1 reliability indicates that the data is highly consistent with the theoretical value, the sensor is operating stably, and the impact of environmental interference is minimal; Level 2 reliability indicates that the data is basically consistent with the theoretical value, the sensor's operating state fluctuates slightly, and the impact of environmental interference is within an acceptable range; Level 3 reliability indicates that the data has a identifiable deviation from the theoretical value, the sensor's operating stability is average, and the impact of environmental interference needs to be closely monitored.

[0149] For Level 1 reliability, the original collected values ​​are directly retained and marked as baseline data; for Level 2 reliability, the original data are corrected according to the environmental compensation algorithm, and the correction process references the temperature, vibration and tilt parameters recorded by the environmental monitoring sensors; for Level 3 reliability, additional deviation explanations and correction suggestions are provided, and the applicable scope of the data is indicated.

[0150] The formula for temperature compensation of pressure data is as follows:

[0151] P corrected =P s / (1-α(TT j ));

[0152] Among them, P corrected This represents the pressure data after temperature compensation; α represents the temperature coefficient of the pressure sensor; T represents the current temperature. j Represented as a temperature reference value;

[0153] The formula for tilt compensation of height data is as follows:

[0154] h corrected =h s ×cosθ;

[0155] Among them, h corrected This represents the compensation for height caused by tilt; h s θ represents the height measured by the sensor, and θ represents the tilt angle.

[0156] For example: Pressure data: corrected to 8.3 kPa after temperature compensation (original 22.5 kPa discarded); Mass and height data retain the original values ​​of 420 kg and 0.84 m;

[0157] The sensor combination information includes the model identification, deployment location code, and operating mode parameters of the pressure sensor, mass sensor, and liquid level sensor that are currently active; environmental compensation parameters include temperature compensation coefficient, vibration attenuation coefficient, and tilt correction value; data quality identification includes reliability level identification, comprehensive reliability score, data acquisition period, and sensor status code, which is used to indicate the operational stability of each sensor during the data acquisition process.

[0158] This invention provides a technical solution: a smart chip bucket content status monitoring system based on intelligent sensing. The system includes a data acquisition module, a data preprocessing module, a data verification module, a reliability grading module, and a data output module.

[0159] The data acquisition module includes a pressure sensor module, a mass sensor module, a liquid level sensor module, and an environmental monitoring sensor module; the pressure sensor module is used to collect bottom pressure data and pressure change rate data; the mass sensor module is used to collect liquid mass data and mass change rate data; and the liquid level sensor module is used to collect liquid height data and height change rate data.

[0160] The data preprocessing module performs format verification on the raw data transmitted by the data acquisition module and aligns the synchronous data collected by different sensors according to a unified time reference.

[0161] The data verification module includes a cross-validation module, a weight adjustment module, and a configuration optimization module. The cross-validation module verifies the accuracy of the data collected by the data acquisition module through physical correlation. The weight adjustment module calculates the real-time reliability coefficient of each sensor based on the data collected by the environmental sensing module and dynamically adjusts the working weight of the sensor.

[0162] The credibility grading module evaluates the overall credibility score based on data verification results, sensor optimization records, and environmental data, divides the data into different credibility levels, and performs differentiated processing on data of different credibility levels.

[0163] The data output module integrates sensor combination information, environmental compensation parameters, and data quality identifiers to generate standardized data packets.

[0164] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for monitoring the state of contents in a smart bucket based on intelligent sensing, characterized in that: The method includes: A sensor array is deployed in the smart tank; the sensor array includes a tank bottom pressure sensor, an environmental monitoring sensor, a mass sensor, and a liquid height sensor; When the rate of change of liquid height, the rate of change of pressure at the bottom of the tank, or the rate of change of liquid mass exceeds the preset trigger threshold, the monitoring data of each sensor is collected and transmitted to the edge computing terminal. Based on the bottom pressure data, liquid mass data, liquid height data, and pre-stored liquid density parameters and the bottom area parameters of the smart tank, the accuracy of the bottom pressure data, liquid mass data, and liquid height data is verified through physical correlation. When the verification results show that the data is inaccurate, the working weight of each sensor is dynamically adjusted according to the environmental parameters, the data is re-collected and physical correlation verification is performed; when the physical rationality verification results do not meet the preset requirements, sensor optimization configuration is automatically executed. Based on the final verification results, the data is classified into confidence levels, and then further processed according to the confidence level to generate a data package containing sensor combination information, environmental compensation parameters, and data quality identifiers.

2. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 1, characterized in that: The pressure sensor collects bottom pressure data and bottom pressure change rate data; the mass sensor collects liquid mass data and liquid mass change rate data; the liquid height sensor collects liquid height data and liquid height change rate data; the environmental monitoring sensor includes a temperature sensor, a vibration sensor, and a tilt sensor, wherein the temperature sensor collects ambient temperature data, the vibration sensor collects vibration intensity data, and the tilt sensor collects tilt angle data. When the rate of change of liquid height, the rate of change of pressure at the bottom of the container, or the rate of change of liquid mass exceeds a preset trigger threshold, a data acquisition command is automatically initiated. This command is simultaneously sent to the pressure sensor, environmental monitoring sensor, mass sensor, and liquid height sensor. The pressure sensor responds to the command by outputting the current and historical bottom pressure data. The mass sensor responds by outputting the current and historical liquid mass data. The liquid height sensor responds by outputting the current and historical liquid height data. The environmental monitoring sensor responds by outputting the current ambient temperature, vibration intensity, and tilt angle data. Each sensor encapsulates the collected monitoring data according to the data frame format of a preset communication protocol and transmits it to the edge computing terminal; After receiving the data, the edge computing terminal performs data integrity verification and timestamp alignment.

3. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 1, characterized in that: The method of verifying the accuracy of the tank bottom pressure data, liquid mass data, and liquid height data through physical correlation includes: Based on the liquid height data h and the liquid density ρ, the theoretical pressure value at the bottom of the container is calculated using the following formula: P l =ρ×g×h; Among them, P l The value is expressed as the theoretical pressure at the bottom of the bucket; g represents the acceleration due to gravity. The pressure data P at the bottom of the bucket collected by the pressure sensor s Compared with the theoretical pressure value P at the bottom of the bucket l Perform a comparison; Based on the liquid height data h, the internal bottom area S of the smart tank, and the liquid density ρ, the theoretical mass value of the liquid is calculated using the following formula: m l =ρ×S×h; Where, m l Expressed as the theoretical mass value of the liquid; The liquid mass data m collected by the mass sensor s With respect to the theoretical mass value m of the liquid l Perform a comparison; Based on liquid mass data m s The theoretical height of the liquid is calculated using the following formula: ρ is the liquid density, and S is the internal bottom area of ​​the smart core tank. h l =m s / (ρ×S); Among them, h l Represented as the theoretical height of the liquid; The liquid height data h collected by the liquid height sensor s With respect to the theoretical height value h of the liquid l Compare them.

4. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 3, characterized in that: Calculate the deviation rate of the bottom pressure data δP=|P s -P l | / P l ×100%, Deviation rate of liquid quality data δm=|m s -m l | / m l ×100%, Deviation rate of liquid height data δh=|h s -h l | / h l ×100%; Preset pressure deviation threshold δP0, mass deviation threshold δm0, and height deviation threshold δh0; When δP≤δP0, δm≤δm0 and δh≤δh0, the data on the bottom pressure, liquid mass, and liquid height are considered accurate; when any deviation rate exceeds the corresponding threshold, the corresponding data is considered inaccurate.

5. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 1, characterized in that: The dynamic adjustment of the working weight of each sensor based on environmental parameters includes: The environmental parameters include ambient temperature data, vibration intensity data, and tilt angle data; the real-time reliability coefficient of each sensor is calculated based on the environmental parameters, and a sensor weight allocation model is established. Based on the reliability coefficient, dynamic weight values ​​are assigned to the pressure sensor, mass sensor and liquid height sensor respectively, with the sensor that is more affected by the environment being assigned a lower weight. The edge computing terminal sends acquisition commands to the corresponding sensors according to the adjusted workload weights. The pressure sensor responds to the command and outputs the bottom pressure data, the mass sensor responds to the command and outputs the liquid mass data, and the liquid height sensor responds to the command and outputs the liquid height data.

6. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 1, characterized in that: The sensor configuration is optimized as follows: The edge computing terminal extracts the response characteristic parameters of each sensor during the physical rationality verification process. The response characteristic parameters include data acquisition cycle stability, signal transmission delay duration, and detection value fluctuation amplitude. Based on response characteristic parameters, abnormal sensor conditions are identified, including acquisition accuracy deviating from the standard range, response speed exceeding a preset threshold, and anti-interference capability falling below the benchmark level. Based on the abnormal situation of the sensor, the edge computing terminal sends parameter adjustment instructions to the corresponding sensor. The parameter adjustment instructions include adjusting the sensor sampling frequency, modifying the sensor working mode, updating the sensor calibration parameters, switching the sensor data transmission channel, and reconfiguring the sensor networking mode. After the parameters are adjusted, the edge computing terminal controls each sensor to restart the data acquisition process and simultaneously starts the environmental monitoring sensor to continuously monitor the adjusted working environment to ensure that the working environment of the sensor under the new parameter settings meets the preset conditions. The edge computing terminal stores the sensor optimization configuration process information in the configuration log. The process information includes the response characteristic parameter record before parameter adjustment, the parameter adjustment instruction content, the response status of each sensor to the adjustment instruction, and the adjusted working environment parameters recorded by the environmental monitoring sensor.

7. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 6, characterized in that: The process of classifying the data based on the final verification results includes: Based on the physical rationality verification results, parameter adjustment records after sensor optimization, and environmental monitoring data, a credibility grading model is constructed. This model includes data consistency indicators, sensor stability indicators, and environmental interference indicators. The comprehensive credibility score is calculated through a weighted summation, as shown in the following formula: C = w1 × A + w2 × B + w3 × E; Where C represents the overall credibility score; w1 represents the weight coefficient of the data consistency index, w2 represents the weight coefficient of the sensor stability index, w3 represents the weight coefficient of the environmental interference index; A represents the data consistency score, which is determined based on the comparison result of the data deviation rate and the preset threshold; B represents the sensor stability score, which is determined based on the fluctuation range of the sensor response characteristic parameters; and E represents the environmental interference score, which is determined based on the degree of deviation of the environmental parameters from the benchmark value. Based on the range of the comprehensive credibility score, the data credibility is divided into three levels: when the comprehensive credibility score is in the first preset range, it is judged as Level 1 credibility; when the comprehensive credibility score is in the second preset range, it is judged as Level 2 credibility; and when the comprehensive credibility score is in the third preset range, it is judged as Level 3 credibility.

8. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 7, characterized in that: The grading process based on credibility level includes: For Level 1 reliability, the original collected values ​​are directly retained and marked as baseline data; for Level 2 reliability, the original data are corrected according to the environmental compensation algorithm, and the correction process references the temperature, vibration and tilt parameters recorded by the environmental monitoring sensors; for Level 3 reliability, deviation explanations and correction suggestions are added, and the applicable scope of the data is marked.

9. The method for monitoring the state of contents of a smart bucket based on intelligent sensing according to claim 1, characterized in that: The sensor combination information includes the model identification, deployment location code, and operating mode parameters of the pressure sensor, mass sensor, and liquid level sensor currently in active state; the environmental compensation parameters include temperature compensation coefficient, vibration attenuation coefficient, and tilt correction value; the data quality identifier includes reliability level identifier, comprehensive reliability score, data acquisition period, and sensor status code, the sensor status code being used to indicate the operational stability of each sensor during the data acquisition process.

10. A smart sensor-based intelligent chip bucket contents status monitoring system, applied to the intelligent sensor-based intelligent chip bucket contents status monitoring method according to any one of claims 1-9, characterized in that: The system includes a data acquisition module, a data preprocessing module, a data verification module, a credibility grading module, and a data output module; The data acquisition module includes a pressure sensor module, a mass sensor module, a liquid level sensor module, and an environmental monitoring sensor module; the pressure sensor module is used to acquire bottom pressure data and pressure change rate data; the mass sensor module is used to acquire liquid mass data and mass change rate data; and the liquid level sensor module is used to acquire liquid height data and height change rate data. The data preprocessing module performs format verification on the raw data transmitted by the data acquisition module and aligns the synchronous data collected by different sensors according to a unified time reference. The data verification module includes a cross-validation module, a weight adjustment module, and a configuration optimization module. The cross-validation module verifies the accuracy of the data collected by the data acquisition module through physical correlation. The weight adjustment module calculates the real-time reliability coefficient of each sensor based on the data collected by the environmental sensing module and dynamically adjusts the working weight of the sensors. The credibility grading module evaluates the comprehensive credibility score based on data verification results, sensor optimization records, and environmental data, divides the data into different credibility levels, and performs differentiated processing on data of different credibility levels. The data output module integrates sensor combination information, environmental compensation parameters, and data quality identifiers to generate standardized data packets.

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