A vacuum distillation safety monitoring system based on Internet of Things

Abstract

This invention discloses an IoT-based vacuum distillation safety monitoring system, relating to the field of vacuum distillation monitoring. It includes: a data acquisition module for real-time acquisition of pressure, temperature, solvent evaporation concentration, and sealing status parameters during the vacuum distillation process, converting each parameter into a digital electrical signal; and a communication module for bidirectional transmission of the digital electrical signal to a pre-defined remote data processing platform located far from the distillation site using a pre-defined standardized digital communication protocol. This invention captures key operational data of the distillation process in real-time, suppresses fluctuation interference through preprocessing, ensures data accuracy and reliability through dynamic self-calibration and intelligent self-testing, automatically switches to a backup scheme to maintain continuous acquisition in case of failure, dynamically adjusts data transmission to adapt to link status, ensuring stable long-distance transmission, and comprehensively evaluates operational compliance based on multiple parameters, dynamically optimizing the adjustment logic to fit the operating conditions, and quickly guiding the distillation system towards a steady state.

Description

Technical Field

[0001] This invention relates to the field of vacuum distillation monitoring technology, specifically to a vacuum distillation safety monitoring system based on the Internet of Things. Background Technology

[0002] In the field of cleaning production parts in the printing, packaging, and painting industries, the mainstream technologies currently include open ethyl acetate solvent tank immersion cleaning and ultrasonic cleaning. More than 70% of companies rely on manual immersion cleaning, while less than 30% use ultrasonic equipment. Solvent purification generally employs a semi-open distillation tank heating process, using bottom heating to achieve solvent evaporation, collection, and cooling purification. Although ultrasonic cleaning equipment is relatively advanced, it still uses an open-box design, relying on solvent immersion and ultrasonic vibration to remove contaminants.

[0003] Based on the above, the existing technology has clear and critical problems: The open design of the cleaning process leads to significant safety and environmental risks, serious solvent waste, unstable cleaning efficiency and quality, and failure to guarantee workers' occupational health. The semi-open process of distillation has a lot of fugitive emissions and also faces safety and environmental hazards. The overall technology is difficult to meet the industry's requirements for automation, environmental compliance and safety management.

[0004] To address this, we propose an IoT-based safety monitoring system for vacuum distillation. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a vacuum distillation safety monitoring system based on the Internet of Things, which can effectively solve the problems of the existing technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses an IoT-based vacuum distillation safety monitoring system, comprising: The system comprises the following modules: a data acquisition module for real-time acquisition of pressure, temperature, solvent evaporation concentration, and sealing status parameters during vacuum distillation, converting these physical parameters into digital electrical signals; a communication module for bidirectional transmission of these digital electrical signals to a remote data processing platform located far from the distillation site using a pre-defined standardized digital communication protocol; an analysis module for receiving the digital data transmitted from the communication module on the remote data processing platform, performing analysis, calculations, and safety threshold comparisons to determine whether the distillation system is operating in compliance with regulations; a control module for automatically adjusting the heating power, vacuum level, and sealing mechanism of the distillation system based on the judgment and comparison results from the remote data processing platform, maintaining steady-state operation of the distillation system; an early warning module for sending early warning commands to authorized terminals via a digital communication link and triggering local warning devices when the remote data processing platform detects parameters exceeding preset safety ranges; and a management module for synchronously storing all distillation process parameter data and control operation records on the remote data processing platform, allowing authorized terminals to query and trace historical operation data via the digital communication link. The data acquisition module is interconnected with a communication module via a wireless network. The communication module is interconnected with an analysis module via a wireless network. The analysis module is interconnected with a control module via a wireless network. The control module is interconnected with an early warning module and a management module via a wireless network.

[0007] Furthermore, the acquisition module is integrated by a multi-sensor array, which acquires pressure, temperature, solvent evaporation concentration, and sealing status parameters based on a synchronous acquisition mechanism. The multi-sensor array includes a piezoresistive sensor, a platinum resistance sensor, a photoionization sensor, and a capacitive sealing sensor. The sampling clock of each sensor is synchronized via GPS, and the sampling time deviation does not exceed a preset time threshold. Furthermore, the collected raw parameter data underwent adaptive fluctuation suppression preprocessing: ; In the formula: These are the preprocessed calibration parameter values; These are the raw parameter values ​​collected by the sensor; This is the fluctuation suppression coefficient; The standard deviation of the fluctuation of the original parameter values ​​within the preset time window; This represents the duration of the statistical time window for fluctuations.

[0008] Furthermore, the fluctuation suppression coefficient ; In the formula: The preset fluctuation suppression level; This represents the saturated vapor pressure of the medium at the current distillation temperature T. The dynamic viscosity of the medium at the current distillation temperature T and system pressure P; This represents the actual operating load of the distillation system. This is the system's rated operating load. Calibration constants for sensor type; During the preprocessing process, if the original parameter value exceeds the preset proportional threshold of the sensor range for no less than a preset number of sampling periods, the sensor self-test process is immediately triggered. The acquisition module sends a preset standard excitation signal that matches the sensor type to the sensor, including voltage excitation and current excitation; After receiving the excitation signal, the sensor feeds back response data, and the acquisition module extracts the amplitude, phase, or frequency characteristic parameters from the response data. The extracted feature parameters are compared with the preset standard response feature range. If the feature parameters are within the standard range, the sensor is determined to be fault-free, the over-range data is recorded as abnormal operating condition data, and the acquisition process continues. If the feature parameters exceed the standard range, the sensor is determined to be faulty, the current acquired data is marked as invalid data, and the fault information, including sensor ID, fault type, self-test feature parameters, and over-range raw data, is reported to the remote data processing platform. In the event of a fault, the acquisition module activates the preset redundant acquisition channel corresponding to the sensor, or temporarily replaces it with historical trend fitting values ​​until the fault is resolved or the sensor is replaced.

[0009] Furthermore, the acquisition module incorporates periodic self-calibration logic. The calibration process is completed using standard reference values ​​issued by a remote data processing platform, and its calibration logic formula is as follows: ; In the formula: For calibration coefficients; These are standard reference parameter values ​​for synchronization with remote data processing platforms. The parameter value currently being measured by the acquisition module. To calibrate the sensitivity coefficient; After calibration, subsequent data acquisition is automatically multiplied by the calibration coefficient. If the coefficient deviation after two consecutive calibrations exceeds the preset calibration accuracy threshold, a sensor replacement prompt will be triggered.

[0010] Furthermore, the communication module adopts an improved MQTT communication protocol, adding a real-time link health assessment field to the standard protocol. ; In the formula: R is the data reception success rate within the most recent complete preset communication cycle; C is the real-time available capacity of the current communication channel; This represents the average delay for bidirectional data transmission. This is a preset minimum constant; The communication module dynamically adjusts the data transmission packet size based on the link health level H. When H is lower than the preset health level threshold, it automatically switches to the backup communication channel and reports the link switching log to the remote data processing platform.

[0011] Furthermore, in the parsing and calculation stage of the analysis module, the pressure value P, temperature value T, solvent evaporation concentration value C, and sealing status value S are extracted from the digital data. The relative deviations of each parameter from the corresponding preset safety thresholds are calculated, and then the overall compliance index of the distillation system is calculated. ; In the formula: For compliance index; , , , These include relative deviations in pressure, temperature, solvent evaporation concentration, and sealing condition. , , , Preset pressure safety threshold, preset temperature safety threshold, preset solvent evaporation concentration safety threshold, and preset sealed state safety threshold; Among them, when If the value is below the preset compliance threshold, the operation is deemed non-compliant.

[0012] Furthermore, the adjustment process of the control module applies a compliance index. And the rate of change of parameter deviation, the adjustment logic is expressed as: ; In the formula: To control the output, adjust the corresponding heating power, vacuum level, or sealing mechanism signals; This is the proportionality coefficient; This represents the deviation between the target parameter and the actual parameter. The integral coefficient; These are the differential coefficients; For compliance correction functions; To adjust the duration; , , Preset low, medium, and high compliance thresholds.

[0013] Furthermore, the warning level in the warning module is based on a compliance index. Division; Set the Level 1 warning threshold as follows: The level 2 warning threshold is ,and < < ; when ∈[ , When this occurs, a Level 1 warning is triggered, and the local warning device emits a preset low-frequency audible and visual signal; when ∈[ , When a level 2 warning is triggered, the local warning device emits a preset high-frequency audible and visual signal, and the authorized system terminal receives a push notification containing abnormal source tracing data. when < When the alarm is triggered, a Level 3 warning is sent. Based on the Level 2 warning, an emergency intervention trigger signal is sent to the control module, and the emergency shutdown device of the distillation system is activated.

[0014] Furthermore, the storage logic in the management module includes blockchain distributed storage and local encrypted backup; Blockchain distributed storage is used to record immutable critical data, including compliance judgment results, control operation instructions, and early warning event records. Each data block contains a timestamp, operation node identifier, and hash verification value. Local encrypted backups are used to store complete end-to-end parameter data. The application time-series database stores data in segments according to the acquisition time, and data compression uses an adaptive compression algorithm based on the parameter change rate.

[0015] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention captures key operational data of the distillation process in real time, suppresses fluctuation interference through preprocessing, ensures data accuracy and reliability through dynamic self-calibration and intelligent self-testing, automatically switches to backup schemes to maintain continuous acquisition in case of failure, dynamically adjusts data transmission to adapt to link status to ensure stable long-distance transmission, comprehensively evaluates operational compliance based on multiple parameters, dynamically optimizes adjustment logic to fit operating conditions, quickly guides the system to a steady state, triggers early warnings according to operational status, and intervenes in emergencies through multiple safety checks to effectively avoid risks. In addition, the data is stored securely, ensuring both immutability and complete backup, and supports historical traceability, comprehensively improving the safety, stability, and traceability of the distillation process, and reducing the cost of manual intervention. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 This is a schematic diagram of a vacuum distillation safety monitoring system based on the Internet of Things. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] The present invention will be further described below with reference to embodiments.

[0020] Example: This embodiment presents an IoT-based vacuum distillation safety monitoring system, such as... Figure 1 As shown, it includes: The data acquisition module is used to collect pressure, temperature, solvent evaporation concentration, and sealing status parameters in real time during the vacuum distillation process, and convert each parameter physical quantity into a digital electrical signal. The acquisition module is integrated by a multi-sensor array. It collects pressure, temperature, solvent evaporation concentration, and sealing status parameters based on a synchronous acquisition mechanism. The multi-sensor array includes a piezoresistive sensor, a platinum resistance sensor, a photoionization sensor, and a capacitive sealing sensor. The sampling clock of each sensor is synchronized via GPS, and the sampling time deviation does not exceed a preset time threshold. Furthermore, the collected raw parameter data underwent adaptive fluctuation suppression preprocessing: ; In the formula: These are the preprocessed calibration parameter values; These are the raw parameter values ​​collected by the sensor; This is the fluctuation suppression coefficient; The standard deviation of the fluctuation of the original parameter values ​​within the preset time window; This refers to the duration of the fluctuation statistics time window; The above formula is based on the raw parameters collected by the sensor. It combines the ratio of the standard deviation of the parameter fluctuation within the preset time window to the window duration and uses the fluctuation suppression coefficient to dynamically correct the raw data. This can effectively filter the interference signals caused by the fluctuation of the operating conditions during the distillation process, and can flexibly adjust the suppression intensity according to the actual operating conditions to ensure that the pre-processed data is more in line with the real operating conditions. Fluctuation suppression coefficient ; In the formula: The preset fluctuation suppression level; This represents the saturated vapor pressure of the medium at the current distillation temperature T. The dynamic viscosity of the medium at the current distillation temperature T and system pressure P; This represents the actual operating load of the distillation system. This is the system's rated operating load. Calibration constants for sensor type; The above formula takes into account the safety requirements of the distillation process to set the fluctuation suppression level. It combines the saturated vapor pressure of the medium at the current temperature, the dynamic viscosity of the medium under the combined influence of temperature and pressure, and the ratio of the actual operating load of the system to the rated load. It also incorporates the calibration constant of the corresponding sensor type, so that the fluctuation suppression coefficient can dynamically match the changes in medium characteristics, operating load and sensor characteristics, thereby achieving targeted fluctuation suppression and improving the stability and accuracy of parameter acquisition under different operating conditions. in, The value range is 1-5, set according to the safety requirements of the distillation process; the higher the level, the greater the inhibition strength. According to the sensor model, the values ​​were calibrated through experiments. The values ​​ranged from 0.8 to 1.2 for the piezoresistive pressure sensor, from 1.1 to 1.5 for the platinum resistance temperature sensor, from 0.9 to 1.3 for the photoionization concentration sensor, and from 1.0 to 1.4 for the capacitive sealing sensor. During preprocessing, if the original parameter value exceeds the preset proportional threshold of the sensor range for no less than the preset number of sampling periods, the sensor self-test process is immediately triggered. The acquisition module sends a preset standard excitation signal that matches the sensor type to the sensor, including voltage excitation and current excitation; After receiving the excitation signal, the sensor feeds back response data, and the acquisition module extracts the amplitude, phase, or frequency characteristic parameters from the response data. The extracted feature parameters are compared with the preset standard response feature range. If the feature parameters are within the standard range, the sensor is determined to be fault-free, the over-range data is recorded as abnormal operating condition data, and the acquisition process continues. If the feature parameters exceed the standard range, the sensor is determined to be faulty, the current acquired data is marked as invalid data, and the fault information, including sensor ID, fault type, self-test feature parameters, and over-range raw data, is reported to the remote data processing platform. In the event of a fault, the acquisition module activates the preset redundant acquisition channel corresponding to the sensor, or temporarily replaces it with historical trend fitting values ​​until the fault is cleared or the sensor is replaced. The acquisition module has built-in periodic self-calibration logic. The self-calibration cycle is dynamically adjusted according to the sensor's usage time. The calibration process is completed using standard reference values ​​issued by a remote data processing platform. The calibration logic formula is as follows: ; In the formula: For calibration coefficients; These are standard reference parameter values ​​for synchronization with remote data processing platforms. The parameter value currently being measured by the acquisition module. To calibrate the sensitivity coefficient; The above formula is based on the deviation between the standard reference parameter value synchronized by the remote data processing platform and the current measurement value of the acquisition module. It introduces a calibration sensitivity coefficient that is dynamically adjusted with the cumulative usage time of the sensor and the historical calibration deviation. By reasonably quantifying the impact of the deviation on the calibration result, it achieves accurate compensation for sensor drift error. At the same time, it allows the calibration process to adapt to the performance changes of the sensor at different stages of use, effectively maintains the accuracy of long-term data acquisition, and thus reduces unnecessary sensor replacement costs. After calibration, subsequent data acquisition is automatically multiplied by the calibration coefficient to eliminate errors caused by sensor drift. If the coefficient deviation after two consecutive calibrations exceeds the preset calibration accuracy threshold, a sensor replacement prompt will be triggered. in, The preset value range is [0.3, 1.8]. The longer the cumulative usage time of the sensor, the greater the historical calibration deviation. The larger the value, the shorter the cumulative usage time of the sensor and the smaller the historical calibration deviation. The smaller the value; The communication module is used to transmit digital electrical signals bidirectionally to a pre-set remote data processing platform far away from the distillation site using a pre-set standardized digital communication protocol. The communication module adopts an improved MQTT communication protocol, adding a real-time link health assessment field to the standard protocol. ; In the formula: R is the data reception success rate within the most recent complete preset communication cycle; C is the real-time available capacity of the current communication channel; This represents the average delay for bidirectional data transmission. This is a preset minimum constant; The above formula comprehensively quantifies the transmission performance of the communication link by multiplying the data reception success rate in the most recent complete preset communication cycle by the real-time available capacity of the current communication channel, and dividing by the sum of the average delay of bidirectional data transmission and the preset minimum constant. It takes into account the reliability of data transmission and the channel carrying capacity, avoids the extreme impact of delay factors on the evaluation results, and provides support for the communication module to dynamically adjust the transmission packet size and switch backup channels. The communication module dynamically adjusts the data transmission packet size based on the link health H. When H is lower than the preset health threshold, it automatically switches to the backup communication channel and reports the link switching log to the remote data processing platform. The analysis module is used to receive digital data transmitted by the communication module on the remote data processing platform, parse, calculate and compare the digital data with security thresholds, and determine whether the distillation system is operating in compliance with regulations. In the parsing and calculation phase of the analysis module, the pressure value P, temperature value T, solvent evaporation concentration value C, and sealing status value S are extracted from the digital data. The relative deviations of each parameter from the corresponding preset safety thresholds are calculated, and then the overall compliance index of the distillation system is calculated. ; In the formula: For compliance index; , , , These include relative deviations in pressure, temperature, solvent evaporation concentration, and sealing condition. , , , Preset pressure safety threshold, preset temperature safety threshold, preset solvent evaporation concentration safety threshold, and preset sealed state safety threshold; The above formula calculates the relative deviations of each parameter, such as pressure, temperature, solvent evaporation concentration, and sealing status, from the corresponding preset safety thresholds. It integrates the influence of the deviations of each parameter by using the sum of squares and the square root method, and then obtains the overall system compliance index through reasonable numerical conversion. It comprehensively considers the deviation of key safety parameters in the distillation process and transforms multi-dimensional deviation information into an intuitive single index, which facilitates quick determination of whether the system operation is compliant. Among them, when If the value is below the preset compliance threshold, the operation is deemed non-compliant. The control module is used to automatically adjust the heating power, vacuum degree and sealing mechanism of the distillation system based on the judgment and comparison results in the remote data processing platform, so as to maintain the steady-state operation of the distillation system. The control module's adjustment process applies a compliance index. And the rate of change of parameter deviation, the adjustment logic is expressed as: ; In the formula: To control the output, adjust the corresponding heating power, vacuum level, or sealing mechanism signals; This is the proportionality coefficient; This represents the deviation between the target parameter and the actual parameter. The integral coefficient; These are the differential coefficients; For compliance correction functions; To adjust the duration; , , Preset low, medium, and high compliance thresholds; The above formula combines proportional, integral, and derivative control with a correction function based on the compliance index. It sets corresponding correction coefficients according to different intervals of the compliance index, and dynamically adjusts the proportional, integral, and derivative coefficients according to the magnitude of parameter deviation, the duration of deviation, the rate of change of deviation, and the level of the compliance index. This allows the control output to accurately adapt to different operating compliance states of the system, ensuring rapid adjustment capability when parameter deviation is large, while also taking into account the adjustment accuracy when the system tends to stabilize, thus assisting the distillation system to quickly converge to steady-state operation. Among them, the proportionality coefficient ∈[0.1, 2.5], when the parameter deviation Larger and compliance index When the value is large, the parameter deviation is large. Smaller and compliance index When the value is small; the integral coefficient ∈[0.01,0.5], when the parameter deviation Persistent and Compliance Index When the value is large, the parameter deviation is large. The value is small when the system decreases rapidly or when it tends to converge; the differential coefficient ∈[0.05,1.8], when the rate of change of parameter deviation Larger and compliance index When the value is large, the rate of change of parameter deviation Smaller and compliance index When the value is relatively small; During the adjustment process, the control module collects the parameter feedback values ​​after adjustment in real time and makes dynamic corrections. , , The coefficient ensures that the distillation system converges rapidly to a steady state; The early warning module is used to send an early warning command to the authorized terminal through a digital communication link and trigger the local warning device when the remote data processing platform detects that the parameters exceed the preset safety range. The warning level in the warning module is based on the compliance index. Division; Set the Level 1 warning threshold as follows: The level 2 warning threshold is ,and < < ; when ∈[ , When this occurs, a Level 1 warning is triggered, and the local warning device emits a preset low-frequency audible and visual signal; when ∈[ , When a level 2 warning is triggered, the local warning device emits a preset high-frequency audible and visual signal, and the authorized system terminal receives a push notification containing abnormal source tracing data. when < When this occurs, a Level 3 warning is triggered. Based on the Level 2 warning, an emergency intervention trigger signal is sent to the control module, and the emergency shutdown device of the distillation system is activated simultaneously. The emergency intervention trigger signal includes the following: Abnormal parameter identifiers and real-time values, compliance index Specific values, warning level codes, signal transmission timestamps, preset emergency control parameter thresholds, corresponding heating power upper limit, vacuum safety range, and sealing mechanism locking force threshold; The emergency shutdown device of the linked distillation system must be executed after safety logic verification. Safety logic verification refers to the control module performing triple verification after receiving the emergency intervention trigger signal: Verify whether the real-time collected data of abnormal parameters are consistent with the abnormal values ​​carried in the signal (the deviation does not exceed the preset verification threshold); verify whether the current operating status of the distillation system is under a safe condition that can be intervened in an emergency (no mechanical jamming of equipment, no other critical control commands being executed); verify whether the authorization identifier of the signal matches the preset authorization code; After all three checks pass, the control module immediately performs an emergency shutdown operation, shutting down the heating unit, adjusting the vacuum valve to a safe pressure relief state, and locking the sealing mechanism. At the same time, the check results and execution status are fed back to the remote data processing platform and authorized terminal. The management module is used to synchronously store the entire distillation process parameter data and control operation records on the remote data processing platform, and supports authorized terminals to query and trace historical operation data through digital communication links; The storage logic in the management module includes blockchain distributed storage and local encrypted backup; Blockchain distributed storage is used to record immutable critical data, including compliance judgment results, control operation instructions, and early warning event records. Each data block contains a timestamp, operation node identifier, and hash verification value. Local encrypted backups are used to store complete end-to-end parameter data. The application time-series database stores data in segments according to the acquisition time. Data compression uses an adaptive compression algorithm based on the parameter change rate. When the parameter change rate is lower than the preset value, a high compression ratio is used, and when it is higher than the preset value, a low compression ratio is used. The data acquisition module is interconnected with a communication module via a wireless network. The communication module is interconnected with an analysis module via a wireless network. The analysis module is interconnected with a control module via a wireless network. The control module is interconnected with an early warning module and a management module via a wireless network.

[0021] In this embodiment, the acquisition module collects pressure, temperature, solvent evaporation concentration, and sealing status parameters in real time during the vacuum distillation process, converting each physical parameter into a digital electrical signal. The communication module then uses a preset standardized digital communication protocol to transmit the digital electrical signal bidirectionally to a preset remote data processing platform far from the distillation site. The analysis module receives the digital data transmitted by the communication module on the remote data processing platform, performs analysis, calculations, and safety threshold comparisons to determine whether the distillation system is operating in compliance with regulations. The control module simultaneously adjusts the heating power, vacuum level, and sealing mechanism of the distillation system based on the judgment and comparison results from the remote data processing platform to maintain steady-state operation of the distillation system. The early warning module further sends an early warning command to the authorized terminal through the digital communication link and triggers a local warning device when the remote data processing platform detects that the parameters exceed the preset safety range. Finally, the management module synchronously stores the entire distillation process parameter data and control operation records on the remote data processing platform, allowing authorized terminals to query and trace historical operation data through the digital communication link.

[0022] In the above embodiments, the system can capture and accurately process key parameters of the distillation process in real time, dynamically adjust the operating status to maintain steady state, provide tiered early warnings and rapid intervention in case of anomalies, and ensure operational safety. Simultaneously, it completely retains operational data, supports traceability and querying, reduces the impact of faults, improves operational reliability and compliance, effectively reduces safety risks, and optimizes distillation efficiency.

[0023] Application example: XX Printing and Packaging Company deployed this system in the ethyl acetate solvent purification process after parts cleaning for the safety management of the heating and distillation process in semi-open distillation tanks.

[0024] During the distillation and purification process, the system's data acquisition module uses a multi-sensor array consisting of a piezoresistive pressure sensor, a platinum resistance temperature sensor, a photoelectric ionization sensor, and a capacitive sealing sensor to synchronously acquire various key parameters via GPS timing. After adaptive fluctuation suppression preprocessing, the raw data yields a pressure calibration value of 0.068 MPa, a temperature calibration value of 65.3 degrees Celsius, an ethyl acetate volatile concentration calibration value of 21 g / m³, and a sealing status calibration value of 0.95. No continuous over-range conditions occurred during the acquisition period. After periodic self-calibration, the calibration coefficient was determined to be 1.02, and subsequent data acquisition automatically applies this coefficient to correct sensor drift errors.

[0025] The communication module uses an improved MQTT protocol to transmit data. The real-time link health score is 0.89, which is higher than the preset health threshold. The current channel is maintained for stable transmission, and no backup channel switching is triggered.

[0026] The remote data processing platform's analysis module extracts parameter data, calculates the relative deviation of each parameter from the preset safety threshold, and obtains an overall system compliance index of 0.85, thus determining that the distillation system is operating in compliance.

[0027] Based on this, the control module sets the proportional coefficient to 1.0, the integral coefficient to 0.07, and the derivative coefficient to 0.38, and outputs control signals to adjust the heating power of the distillation tank to 25 kW, stabilize the vacuum degree at 0.068 MPa, and set the locking force of the sealing mechanism to 90% of the rated value, thus maintaining steady-state operation of the solvent purification process.

[0028] During the distillation process, residual oil on some components caused a sudden change in the solvent evaporation rate, resulting in an abnormally high ethyl acetate evaporation concentration. The analysis module recalculated the compliance index to 0.29, which falls within the level-two warning range. The warning module immediately triggered a local high-frequency audible and visual alert and simultaneously sent a notification to authorized terminals containing traceability data including the abnormal parameter, compliance index, and warning level. The control module dynamically adjusted the control coefficients, appropriately reduced the heating power, and strengthened the sealing mechanism. Within a short time, the solvent evaporation concentration returned to normal, and the compliance index rebounded to 0.83.

[0029] Throughout the solvent purification process, the management module records key data such as compliance judgment results, control operation instructions, and early warning events through blockchain distributed storage to ensure immutability. At the same time, it stores all parameter data locally through encrypted backup, uses a time-series database to store data in segments according to the collection time, and adaptively compresses the data according to the parameter change rate to achieve secure storage and convenient traceability of the entire solvent distillation and purification process data.

[0030] In summary, the system in the above embodiments captures key operational data of the distillation process in real time, suppresses fluctuation interference through preprocessing, ensures data accuracy and reliability through dynamic self-calibration and intelligent self-testing, automatically switches to backup schemes to maintain continuous acquisition in case of failure, dynamically adjusts data transmission to adapt to link status to ensure stable long-distance transmission, comprehensively evaluates operational compliance based on multiple parameters, dynamically optimizes adjustment logic to fit operating conditions, quickly guides the system towards a steady state, triggers early warnings according to operational status, and intervenes in emergencies after multiple security checks, effectively avoiding risks. In addition, the data adopts a secure storage method that balances immutability and complete backup, supports historical traceability, comprehensively improves the safety, stability and traceability of the distillation process, and reduces the cost of manual intervention.

[0031] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An Internet of Things based vacuum distillation safety monitoring system characterized by, The application relates to a vacuum distillation system parameter real-time monitoring and control system. The system comprises: a collection module for collecting pressure, temperature, solvent evaporation concentration and sealing state parameters in a vacuum distillation process in real time, and converting the physical parameters into digital electrical signals; a communication module for applying a preset standardized digital communication protocol to bidirectionally transmit the digital electrical signals to a preset remote data processing platform far away from a distillation site; an analysis module for receiving the digital data transmitted by the communication module on the remote data processing platform, analyzing, operating and comparing the digital data with safety thresholds, and determining whether the distillation system operation is compliant; a control module for automatically adjusting the heating power, vacuum degree and sealing mechanism of the distillation system according to the determination and comparison results in the remote data processing platform, and maintaining the steady operation of the distillation system; a pre-warning module for sending a pre-warning instruction to an authorized terminal and triggering a local warning device through a digital communication link when the remote data processing platform detects that the parameters exceed a preset safety range; 2. The vacuum distillation safety monitoring system based on the Internet of Things according to claim 1, characterized in that, and a management module for synchronously storing distillation parameter data and control operation records in the remote data processing platform, and supporting the authorized terminal to query and trace historical operation data through the digital communication link. The collection module is integrated with a multi-sensor array, and the pressure, temperature, solvent evaporation concentration and sealing state parameters are collected based on a synchronous collection mechanism; the multi-sensor array comprises a piezoresistive sensor, a platinum resistance sensor, a photoionization sensor and a capacitive sealing sensor, the sampling clock of each sensor is synchronized through GPS time service, and the sampling time deviation is not more than a preset time threshold value; ； In the formula, is a pre-processed calibration parameter value; is an original parameter value collected by the sensor; is a fluctuation suppression coefficient; is a fluctuation standard deviation of the original parameter value within a preset time window; is a fluctuation statistical time window duration.

3. The vacuum distillation safety monitoring system based on the Internet of Things according to claim 2, characterized in that, the fluctuation suppression coefficient ; wherein: is a preset fluctuation suppression level; is the saturated vapor pressure of the medium at the current distillation temperature T; is the dynamic viscosity of the medium at the current distillation temperature T, system pressure P; is the actual operating load of the distillation system; is the rated operating load of the system; is a sensor type calibration constant; and the collected original parameter data is pretreated through adaptive fluctuation suppression. In the pretreatment process, if the original parameter value continuously exceeds a preset proportion threshold value of the sensor range for not less than a preset sampling period, a sensor self-checking process is immediately triggered. The collection module sends a preset standard excitation signal matched with the sensor type to the sensor, including a voltage excitation and a current excitation; the sensor feeds back response data after receiving the excitation signal, and the collection module extracts amplitude, phase or frequency characteristic parameters in the response data; the extracted characteristic parameters are compared with a preset standard response characteristic range, if the characteristic parameters are within the standard range, it is determined that the sensor is fault-free, the current over-range is recorded as abnormal working condition data, and the collection process is continuously executed; if the characteristic parameters exceed the standard range, it is determined that the sensor has a fault, the current collection data is marked as invalid data, and fault information, including a sensor ID, a fault type, self-checking characteristic parameters and over-range original data, is reported to the remote data processing platform; 4. The vacuum distillation safety monitoring system based on the Internet of Things according to claim 2, characterized in that, in the fault state, the collection module starts a preset redundant collection channel corresponding to the sensor, or temporarily replaces the sensor with a historical trend fitting value until the fault is eliminated or the sensor is replaced. ； In the formula: is a calibration coefficient; is a standard reference parameter value for synchronization of the remote data processing platform, is a parameter value currently measured by the acquisition module, is a calibration sensitivity coefficient; The collection module is internally provided with a periodic self-calibration logic, and the calibration process is completed through a standard reference value issued by the remote data processing platform; the calibration logic formula is:

5. The vacuum distillation safety monitoring system based on the Internet of Things according to claim 1, characterized in that, The communication module adopts an improved MQTT communication protocol, and a link health degree real-time evaluation field is added on the basis of a standard protocol ; after calibration, the subsequent collection data is automatically multiplied by the calibration coefficient, if the coefficient deviation after continuous two times of calibration exceeds a preset calibration precision threshold value, a sensor replacement prompt is triggered. In the formula, R is a data reception success rate in a latest complete preset communication period. C is the real-time available capacity of the current communication channel; is the average delay for data bi-directional transmission; is a preset minimum constant; The communication module dynamically adjusts the data transmission packet size based on the link health level H. When H is lower than the preset health level threshold, it automatically switches to the backup communication channel and reports the link switching log to the remote data processing platform.

6. The vacuum distillation safety monitoring system based on the Internet of Things according to claim 1, characterized in that, In the parsing and calculation phase of the analysis module, the pressure value P, temperature value T, solvent evaporation concentration value C, and sealing status value S are extracted from the digital data. The relative deviations of each parameter from the corresponding preset safety thresholds are calculated, and then the overall compliance index of the distillation system is calculated. ； wherein: is a compliance index; , , , is a relative deviation of pressure, a relative deviation of temperature, a relative deviation of solvent evaporation concentration, a relative deviation of sealing state; , , , is a preset pressure safety threshold, a preset temperature safety threshold, a preset solvent evaporation concentration safety threshold, a preset sealing state safety threshold; wherein, when a lower than the preset compliance threshold is determined as a non-compliant operation. 7.The vacuum distillation safety monitoring system based on Internet of Things according to claim 1, wherein, The control module's regulation process applies a compliance index and a parameter deviation rate of change, the regulation logic being expressed as: ； In the formula: is a control output, corresponding to a heating power, a vacuum degree, or a sealing mechanism adjustment signal; is a proportional coefficient; is a deviation value of a target parameter and an actual parameter; is an integral coefficient; is a differential coefficient; is a compliance correction function; is an adjustment duration; , , are preset low, medium, and high compliance threshold values. 8.The vacuum distillation safety monitoring system based on the Internet of Things according to claim 1, wherein, The pre-warning module, wherein the pre-warning level is according to the compliance index Partitioning; The first level early warning threshold is set as , the second level early warning threshold is set as , and < < ; When ∈[ , ) a first-level early warning is triggered, and the local warning device sends a preset low-frequency sound and light signal. When ∈[ , ) a secondary early warning is triggered, the local warning device sends a preset high-frequency sound and light signal, and the authorization system terminal receives a push notification containing abnormal trace data. When < a third level warning is triggered, an emergency intervention trigger signal is sent to the control module on the basis of the second level warning, and the emergency shutdown device of the distillation system is linked. 9.The vacuum distillation safety monitoring system based on the Internet of Things according to claim 1, wherein, The storage logic in the management module includes blockchain distributed storage and local encrypted backup; Blockchain distributed storage is used to record immutable critical data, including compliance judgment results, control operation instructions, and early warning event records. Each data block contains a timestamp, operation node identifier, and hash verification value. Local encrypted backups are used to store complete end-to-end parameter data. The application time-series database stores data in segments according to the acquisition time, and data compression uses an adaptive compression algorithm based on the parameter change rate. 10.The vacuum distillation safety monitoring system based on the Internet of Things according to claim 1, wherein, The data acquisition module is interconnected with a communication module via a wireless network. The communication module is interconnected with an analysis module via a wireless network. The analysis module is interconnected with a control module via a wireless network. The control module is interconnected with an early warning module and a management module via a wireless network.

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