Weizhiheng Hazardous Chemicals Intelligent Weighing Big Data Comparison and Early Warning System

Abstract

This invention relates to the field of hazardous chemical management technology, and provides a Weizhiheng intelligent weighing and big data comparison and early warning system for hazardous chemicals. The system includes modules for multi-dimensional time-series data acquisition, nonlinear physical loss compensation, dynamic tolerance generation based on operator profiles, and multi-dimensional verification and interlocking early warning. By collecting ambient temperature and humidity data and time spent away from the container, combined with saturated vapor pressure and viscosity coefficient, the system calculates and separates dynamic volatilization and wall residue losses to obtain the compensated actual operational consumption. A deep learning model adaptively adjusts the allowable consumption range based on the operator's historical variance characteristics. When consumption exceeds the limit, a visual perception front-end is activated to identify physical spillage characteristics. This invention eliminates the interference of environmental and physical factors on weight calculation, achieves accurate identification of non-malicious losses and illegal interception, and significantly improves the accuracy of safety control over the flow of hazardous chemicals.

Description

Technical Field

[0001] This invention relates to the field of hazardous chemical management technology, specifically to the Weizhiheng Intelligent Weighing System for Hazardous Chemicals, which features big data comparison and early warning. Background Technology

[0002] Hazardous chemicals are chemicals that possess dangerous properties such as toxicity, corrosiveness, explosiveness, and flammability, and may pose a threat to human health, facilities, and the environment. The use, storage, and transportation of hazardous chemicals all present high safety risks; improper operation or inadequate supervision can easily lead to safety accidents.

[0003] To improve the safety management of hazardous chemicals, existing hazardous chemical management systems are gradually moving towards intelligent systems. For example, existing intelligent hazardous chemical management cabinets are typically equipped with biometric cameras, fingerprint readers, and other identity verification devices, as well as weighing sensors and intelligent system panels. In practical applications, operators can open the storage box and retrieve hazardous chemicals after authorization. The system uses weighing sensors to monitor the weight changes of chemicals in real time during storage and triggers warnings when there are abnormal weight changes or when chemicals are depleted.

[0004] However, existing systems can only record and statically compare the physical consumption of hazardous chemicals, lacking a mechanism to determine the reasonableness of the consumption. The weight change data acquired by the system through weighing sensors only reflects the reduction in chemical quantity; this underlying hardware data is not linked to the specific production tasks and experimental formulation guidelines at the upper level of the management system. Therefore, the system cannot determine whether the actual consumption matches the theoretical requirements of the current application.

[0005] In existing technologies, the compliance of chemical requisition quantities relies heavily on manual declaration. If operators cause abnormal reagent loss due to experimental errors, or declare and retain chemicals in excess for illegal purposes, the existing system assumes the consumption process is compliant after completing identity verification and weight recording, making it difficult to effectively prevent the covert leakage of hazardous chemicals.

[0006] The actual reasonable consumption of hazardous chemicals is affected by multiple variables, including the type of experiment, production process batch, and environmental temperature and humidity. Current technologies lack in-depth analysis of historical and operational data and the training of algorithmic models, failing to establish dynamic consumption benchmarks for different task scenarios. This results in early warning mechanisms being largely limited to fixed threshold alarms such as leaks or long-term non-return, leading to delayed warnings and hindering proactive prevention and dynamic verification.

[0007] In summary, this invention provides a smart weighing and big data comparison and early warning system for hazardous chemicals to solve the above problems. Summary of the Invention

[0008] This invention provides a Weizhiheng intelligent weighing data comparison and early warning system for hazardous chemicals. It solves the technical problems in existing technologies that hazardous chemical management can only passively record physical weight changes, lacks an intelligent judgment mechanism for the rationality of actual consumption, and is difficult to distinguish between non-malicious business losses and illegal interception. This is achieved through multimodal underlying data fusion, nonlinear physical loss algorithm compensation, and AI dynamic tolerance comparison based on personnel operation profiles.

[0009] The specific technical solution of this invention is as follows:

[0010] The Weizhiheng Hazardous Chemicals Intelligent Weighing Major Data Comparison and Early Warning System includes:

[0011] The multi-dimensional time-series data acquisition module is used to acquire the time-series characteristics of hazardous chemicals from the time of their release to their return, and to collect the initial weight of the chemicals released, the weight of the chemicals returned, the ambient temperature data, and the ambient humidity data within the period.

[0012] The nonlinear physical loss compensation module, connected to the multidimensional time-series data acquisition module, is used to extract the saturated vapor pressure and viscosity coefficient of the target chemical. Combined with the ambient temperature data, ambient humidity data, and exposure time from outbound to return, it calculates the dynamic volatilization loss of the target chemical due to natural environmental factors. It also calculates the container wall residue loss due to physical adhesion by combining the viscosity coefficient with the theoretical number of pouring and transferring operations recorded in the business application work order. Based on the difference between the initial outbound weight and the inbound return weight, the total physical loss is obtained, and the dynamic volatilization loss and the container wall residue loss are deducted from the total physical loss to obtain the compensated actual business consumption.

[0013] The dynamic tolerance generation module based on the operation profile is used to retrieve the historical business declaration and actual consumption comparison records of the current operator within the evaluation period, perform dispersion analysis on the comparison records, extract the historical operation variance features that reflect the stability of the operator's usage, and input the theoretical usage of the current declaration into the deep learning model. Combined with the historical operation variance features, it outputs the upper and lower limits of the adaptive baseline consumption allowable range.

[0014] The multi-dimensional verification and interlocking early warning module is used to determine whether the actual business consumption after compensation falls between the upper limit and the lower limit. When it is determined that it does not fall between the upper limit and the lower limit, an abnormal early warning signal is generated and a physical interlocking blocking command is triggered.

[0015] As an improvement of the present invention, the multi-dimensional time-series data acquisition module is communicatively connected to a weighing sensing matrix and a micro-meteorological monitoring node deployed at the hazardous chemical storage node; the weighing sensing matrix is ​​used to capture weight data when the target chemical is released from the warehouse and returned to generate the initial weight of the release and the return weight; the micro-meteorological monitoring node is used to collect and transmit the ambient temperature data and ambient humidity data in real time.

[0016] As an improvement of the present invention, the nonlinear physical loss compensation module is provided with an environmental volatilization analysis subunit; the environmental volatilization analysis subunit has built-in volatilization rate calculation logic with the saturated vapor pressure, the ambient temperature data and the ambient humidity data as independent variables. The environmental volatilization analysis subunit performs a time-dimensional cumulative calculation of the volatilization rate in conjunction with the exposure duration to output the dynamic volatilization loss.

[0017] As an improvement of the present invention, the nonlinear physical loss compensation module is provided with a fluid adhesion analysis subunit; the fluid adhesion analysis subunit stores a mapping relationship table between the viscosity characteristics of chemicals and the standard residual amount of a single transfer. The fluid adhesion analysis subunit obtains the corresponding standard residual amount of a single transfer based on the viscosity coefficient of the target chemical in the mapping relationship table, and performs superposition calculation in combination with the theoretical number of pouring transfers to output the container wall residue loss amount.

[0018] As an improvement of the present invention, the dynamic tolerance generation module based on the operation profile includes a variance feature extractor; the variance feature extractor obtains the deviation difference between the actual business consumption and the theoretical consumption of the operator in multiple times within the evaluation period, performs a quantitative evaluation based on the deviation of the deviation difference group from the benchmark mean, and uses the dispersion index obtained from the evaluation as the historical operation variance feature.

[0019] As an improvement of the present invention, the deep learning model is a multilayer perceptron network pre-installed on the control host; the input layer feature vector of the multilayer perceptron network is fixed and includes: the hazard level weight of the target chemical, the process complexity rating recorded in the business declaration work order, and the historical operation variance feature.

[0020] As an improvement of the present invention, the multilayer perceptron network integrates a variance feedback regulator, and the control logic of the variance feedback regulator is as follows:

[0021] When the historical operational variance characteristics reflect that the operator's usage stability is higher than the built-in safety benchmark level, the variance feedback regulator controls the upper and lower limits to shrink and approach the theoretical usage.

[0022] When the reflected dosage stability is lower than the safety benchmark level, the variance feedback regulator controls the upper and lower limits to expand outward from the theoretical dosage.

[0023] As an improvement of the present invention, the system further includes a visual perception front-end that is communicatively connected to the multidimensional verification and interlocking early warning module; the visual perception front-end is deployed in the flow and return channel of hazardous chemicals and the lower overflow prevention area, and is used to collect container appearance image data and ground spillage monitoring image data when the target chemical is returned.

[0024] As an improvement of the present invention, the multi-dimensional verification and interlocking early warning module is pre-configured with a visual cross-obstacle clearing strategy, the execution logic of which is as follows:

[0025] When the actual business consumption after compensation is greater than the upper limit of the baseline consumption allowable range, the multi-dimensional verification and interlocking early warning module activates the visual perception front end to capture images.

[0026] Machine vision algorithms are used to determine whether there are edge features of liquid stains in the container appearance image data, and anomalies such as stain area or liquid level increase are compared and judged in the ground spill monitoring image data.

[0027] If the edge features of the liquid stain are detected or it is determined that there is abnormal spillage in the ground spill monitoring image data, the abnormal warning signal is marked as an accidental spillage loss type, and the physical interlock blocking command is released.

[0028] If the edge features of the liquid stain are not identified and the ground spill monitoring image data is determined to be normal, the abnormal warning signal will be marked as a suspected illegal interception type, and the physical interlock blocking command will be maintained.

[0029] As an improvement of the present invention, the system further includes a model evolution and self-learning subsystem connected to the dynamic tolerance generation module based on the operation profile; the model evolution and self-learning subsystem extracts the actual business consumption, the real physical loss reasons and the operator's identity as label data after manual security review, and iteratively trains the network weight parameters of the deep learning model through machine learning algorithms to achieve adaptive dynamic calibration of the upper and lower limits.

[0030] In this invention, the multi-dimensional time-series data acquisition module relies on the underlying weighing sensor and can interface with existing hazardous chemical storage equipment for hardware and data exchange. The microclimate monitoring node is used to acquire real-time temperature and humidity changes in the local environment, providing a data foundation for subsequently removing environmental factors from the chemical weight and ensuring the comprehensiveness and objectivity of the underlying input data.

[0031] In this invention, the nonlinear physical loss compensation module combines underlying hardware sensor data with upper-layer business management logic. By extracting the saturated vapor pressure and viscosity coefficient of the target chemical, it calculates the natural volatilization amount and the amount of residue adhering to the wall during dumping and transfer. By deducting the aforementioned natural volatilization amount and wall residue amount from the total weight difference, the actual business consumption amount used for the declared task can be restored, thereby reducing the false alarm rate of warnings for highly volatile and high-viscosity chemicals during use.

[0032] In this invention, the dynamic tolerance generation module based on the operator profile employs a dynamic threshold determination mechanism. The deep learning model transforms the operator's historical operation records into quantified historical operation variance features. For operators with small deviations in historical usage, the system adaptively narrows the upper and lower limits of the baseline consumption allowable range; for operators with large deviations in historical usage, the system appropriately widens the upper and lower limits of the baseline consumption allowable range. This enables differentiated calculations for operators with different skill levels, reducing invalid warnings caused by fixed thresholds.

[0033] In this invention, the multi-dimensional verification and interlocking early warning module, combined with a visual perception front-end, performs cross-troubleshooting verification. When actual business consumption exceeds the allowable range, the system uses machine vision algorithms to identify the edge features of accidentally spilled liquid stains on the outer wall of the container or in the storage area. This determines whether the excessive consumption is due to physical spillage or suspected illegal interception, and issues a cleaning review work order or maintains the blocking alarm instruction based on the judgment result. Furthermore, the model evolution and self-learning subsystem iteratively trains the deep learning model based on real usage data after manual review, to continuously calibrate the early warning judgment logic.

[0034] In this invention, the various computing modules and deep learning models of the system can be deployed on a local industrial control host or on a cloud server, and interact with the lower-level sensing hardware through the Internet of Things communication protocol.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. This invention constructs a nonlinear physical loss compensation mechanism by introducing micrometeorological environmental data and chemical physical properties, namely saturated vapor pressure and viscosity coefficient. Natural volatilization loss and wall residue loss are deducted from the total weight difference between inbound and outbound storage, overcoming the shortcomings of existing technologies that directly equate physical weight difference with actual operational consumption. This eliminates the interference of environmental and physical factors on weight calculation, effectively improving the accuracy of calculating the actual operational consumption of hazardous chemicals.

[0037] 2. This invention achieves multi-dimensional, linked early warning judgment by combining a dynamic tolerance generation module based on operator profiles with a visual cross-checking strategy. A deep learning model generates an adaptive consumption allowance range based on the historical operational variance characteristics of operators, while the visual perception front end is used to eliminate false alarms caused by physical spills. This mechanism can accurately distinguish between non-malicious business losses and suspected illegal losses, reducing the cost of manual on-site verification, improving the safety control capabilities of hazardous chemical transfer processes, and enabling dynamic self-learning and iterative optimization of the system's early warning judgment parameters. Attached Figure Description

[0038] Figure 1 This is the main flowchart of the overall system operation of the present invention.

[0039] Figure 2 This is a flowchart of the algorithm for nonlinear physical loss compensation calculation of the present invention.

[0040] Figure 3 This is a flowchart of the visual cross-obstacle clearing and interlocking early warning branch of the present invention. Detailed Implementation

[0041] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0042] like Figure 1-3 As shown, this invention provides a smart weighing data comparison and early warning system for hazardous chemicals, the system comprising:

[0043] The multi-dimensional time-series data acquisition module is used to acquire the time-series characteristics of hazardous chemicals from the time of their release to their return, and to collect data on the initial weight of the chemicals released, the weight of the chemicals returned, the ambient temperature, and the ambient humidity during the period.

[0044] The nonlinear physical loss compensation module, connected to the multidimensional time-series data acquisition module, is used to extract the saturated vapor pressure and viscosity coefficient of the target chemical. Combined with environmental temperature data, environmental humidity data, and the exposure time from outbound to return, it calculates the dynamic volatilization loss of the target chemical caused by natural environmental factors. It also calculates the container wall residue loss caused by physical adhesion by combining the viscosity coefficient with the theoretical number of pouring and transferring times recorded in the business application work order. The total physical loss is obtained based on the difference between the initial outbound weight and the inbound return weight. The dynamic volatilization loss and the container wall residue loss are then deducted from the total physical loss to obtain the compensated actual business consumption.

[0045] The dynamic tolerance generation module based on the operation profile is used to retrieve the historical business declaration and actual consumption comparison records of the current operator within the evaluation period, perform dispersion analysis on the comparison records, extract the historical operation variance features that reflect the stability of the operator's usage, and input the theoretical usage of the current declaration into the deep learning model. Combined with the historical operation variance features, it outputs the upper and lower limits of the adaptive baseline consumption allowable range.

[0046] The multi-dimensional verification and interlocking early warning module is used to determine whether the actual business consumption after compensation falls between the upper and lower limits. When it is determined that it does not fall between the upper and lower limits, an abnormal early warning signal is generated and a physical interlocking blocking command is triggered.

[0047] The multidimensional time-series data acquisition module is connected to a weighing sensing matrix and a micro-meteorological monitoring node deployed at the hazardous chemical storage node. The weighing sensing matrix is ​​used to capture weight data when the target chemical is released from the warehouse and returned to generate the initial weight for release and the weight for return. The micro-meteorological monitoring node is used to collect and transmit ambient temperature data and ambient humidity data in real time.

[0048] The nonlinear physical loss compensation module includes an environmental volatile analysis subunit. This subunit has built-in logic for calculating the volatile rate using saturated vapor pressure, ambient temperature, and ambient humidity as independent variables. It combines the exposure duration to perform a time-dimensional cumulative calculation of the volatile rate, thereby outputting the dynamic volatile loss.

[0049] The nonlinear physical loss compensation module includes a fluid adhesion analysis subunit. This subunit stores a mapping relationship table between the viscosity characteristics of chemicals and the standard residual amount for a single transfer. Based on the viscosity coefficient of the target chemical, the subunit matches and obtains the corresponding standard residual amount for a single transfer in the mapping relationship table, and combines it with the theoretical number of pouring transfers to perform superposition calculations to output the container wall residue loss.

[0050] The dynamic tolerance generation module based on the operation profile includes a variance feature extractor. The variance feature extractor obtains the deviation difference between the actual business consumption and the theoretical consumption of the operator multiple times within the evaluation period. It performs a quantitative evaluation based on the deviation of the deviation difference group from the benchmark mean and uses the dispersion index obtained from the evaluation as the historical operation variance feature.

[0051] The deep learning model is a multilayer perceptron network pre-installed on the control host; the input layer feature vector of the multilayer perceptron network is fixed and includes: the hazard level weight of the target chemical, the process complexity rating recorded in the business declaration work order, and the historical operation variance features.

[0052] The multilayer perceptron network integrates a variance feedback regulator, and the control logic of the variance feedback regulator is as follows:

[0053] When the historical operational variance characteristics reflect that the operator's usage stability is higher than the built-in safety benchmark level, the variance feedback regulator controls the upper and lower limits to shrink towards the theoretical usage.

[0054] When the stability of the reflected dosage is lower than the safety benchmark level, the variance feedback regulator controls the upper and lower limits of the dosage to be amplified outward from the theoretical dosage.

[0055] The system also includes a visual perception front-end that is connected to the multi-dimensional verification and interlocking early warning module. The visual perception front-end is deployed in the flow and return channels of hazardous chemicals and the lower overflow prevention area to collect container appearance image data and ground spill monitoring image data when the target chemicals are returned.

[0056] The multi-dimensional verification and interlocking early warning module has a pre-set visual cross-obstacle clearing strategy. The execution logic of this strategy is as follows:

[0057] When the actual business consumption after compensation exceeds the upper limit of the baseline consumption allowable range, the multi-dimensional verification and interlocking early warning module activates the visual perception front end to capture images.

[0058] Machine vision algorithms are used to determine whether there are edge features of liquid stains in container appearance image data, and to make anomaly judgments by comparing the stain area or liquid level rise in ground spill monitoring image data.

[0059] If the edge features of the liquid stain are identified or it is determined that there is abnormal spillage in the ground spill monitoring image data, the abnormal warning signal will be marked as an accidental spillage loss type, and the physical interlock blocking command will be released.

[0060] If no edge features of the liquid stain are identified and the ground spill monitoring image data is determined to be normal, the abnormal warning signal will be marked as a suspected illegal interception type, and the physical interlock blocking command will be maintained.

[0061] The system also includes a model evolution and self-learning subsystem connected to the dynamic tolerance generation module based on the operation profile. The model evolution and self-learning subsystem extracts the actual business consumption, the real physical loss reasons, and the operator's identity as label data after manual security review. Iteratively trains the network weight parameters of the deep learning model through machine learning algorithms to achieve adaptive dynamic calibration of the upper and lower limits.

[0062] Example 1: In actual operation, when a senior R&D personnel needs to use a highly volatile organic solvent (such as an ethyl acetate-based complex solvent) to perform a synthesis task, the operator first registers their identity through the intelligent system panel on one side of the management cabinet and completes permission matching verification through the biometric camera installed on the top of the management cabinet. The system simultaneously analyzes the business application form submitted by the operator, accurately determining that the theoretical usage of the organic solvent in this synthesis task is 100.00 grams, and that due to experimental requirements, the theoretical number of pouring and transferring operations is 3. At the same time, the system's internal nonlinear physical loss compensation module automatically extracts the saturated vapor pressure parameter of this specific organic solvent under standard atmospheric pressure and the viscosity coefficient reflecting fluid characteristics from the local physical property database.

[0063] After acquiring basic business parameters, the system immediately activates the dynamic tolerance generation module based on the operator profile for personalized evaluation. The module's variance feature extractor automatically retrieves 52 records of hazardous chemical requisition and return within the past three-month evaluation period, extracting the deviations between actual and theoretical consumption amounts and quantifying the deviations from the benchmark mean. The evaluation revealed that the experienced operator's historical deviations were extremely small, with a historical operational variance index of only 0.024, reflecting a significantly higher usage stability than the system's built-in safety benchmark. Subsequently, the system inputs the theoretical consumption of 100.00 grams, along with the hazard level weight of the organic solvent, the process complexity rating of the submitted work order, and the extremely low historical operational variance, into a pre-built multilayer perceptron network. Based on the aforementioned high stability characteristics, the variance feedback regulator within the multilayer perceptron network controls the upper and lower limits of the benchmark consumption allowable range to shrink towards the theoretical consumption, ultimately outputting an adaptive benchmark consumption allowable range of 98.42 grams to 101.58 grams.

[0064] After establishing the dynamic tolerance range, the operator opened the corresponding storage box to retrieve the organic solvent. At this time, the multi-dimensional time-series data acquisition module, through a weighing sensing matrix deployed inside the storage box, captured the initial weight of the reagent bottle upon release as 1000.52 grams and started timing. During the 114 minutes of exposure of the organic solvent after removal from the cabinet and the execution of the experiment, the microclimate monitoring node deployed at the hazardous chemical storage point continuously collected local environmental data, transmitting and recording the average environmental temperature as 24.3 degrees Celsius and the average environmental humidity as 58.2% during this period. This high-frequency multi-dimensional time-series data acquisition provides an objective and continuous data foundation for subsequent removal of environmental interference.

[0065] After the experiment, the operator returned the organic solvent reagent bottle to the storage box. The weighing sensing matrix performed a precise weighing again, capturing the returned weight as 884.28 grams. The system's underlying calculation showed that the total physical loss due to outbound and inbound shipments was 116.24 grams. Next, the nonlinear physical loss compensation module initiated in-depth calculations. First, the environmental volatile analysis subunit substituted the ambient temperature data of 24.3 degrees Celsius, the ambient humidity data of 58.2%, and the saturated vapor pressure of the organic solvent into the volatile rate calculation logic, estimating the volatile rate under the current environment to be approximately 0.052 grams per minute. Combined with the 114-minute exposure time, a cumulative calculation was performed over time, outputting the dynamic volatile loss of the target chemical due to natural environmental factors as 5.93 grams. Secondly, the fluid adhesion analysis subunit, based on the viscosity coefficient of the organic solvent, matched and obtained its single-transfer standard residual amount of 0.18 grams from the mapping and correlation table. Combined with the three theoretical pouring and transfer times recorded in the work order, the subunit calculated and output the container wall residue loss due to physical adhesion as 0.54 grams. From the total physical loss of 116.24 grams, the system accurately deducted 5.93 grams of dynamic evaporation loss and 0.54 grams of container wall residue loss, ultimately calculating the actual business consumption after removing non-human business factors to be 109.77 grams.

[0066] After completing nonlinear compensation, the multidimensional verification and interlocking early warning module compared the actual business consumption of 109.77 grams with the previously generated baseline consumption allowable range of 98.42 grams to 101.58 grams. The system determined that the actual business consumption did not fall within this range and was significantly greater than the upper limit of the allowable range. At this point, the system did not immediately trigger the highest-level physical interlocking blocking command for illegal interception, but automatically activated the visual cross-obstacle clearing strategy. The multidimensional verification and interlocking early warning module quickly called the visual perception front end to perform high-frequency image capture of the flow channel and the lower anti-overflow area when the target chemical was returned. The machine vision algorithm identified the edge features of a liquid stain with an area of ​​approximately 5.24 square centimeters on the outer wall of the reagent bottle by performing edge detection on the container appearance image data, and determined that a physical spill had occurred during the pouring process. Based on this visual verification result, the system clearly marked this abnormal warning signal as an accidental spillage loss type, then released the originally triggered physical interlocking blocking command, and automatically issued a cleaning and safety review work order to the laboratory management terminal. This process fully demonstrates that while ensuring the safe transfer of hazardous chemicals, the present invention can intelligently identify non-malicious losses, significantly reducing the system's ineffective blocking rate and the on-site troubleshooting costs for management personnel.

[0067] Example 2: In actual operation, when a newly hired operator (or a trainee researcher) needs to obtain a moderately volatile and highly viscous chemical reagent (such as a solvent for preparing a certain polymer adhesive) to perform a routine mixing task, the operator first registers their identity and undergoes biometric verification at the hazardous chemicals storage point. The system simultaneously analyzes the work order submitted by the new operator, accurately determining the theoretical usage of the reagent in this mixing task to be 50.00 grams, and that due to process requirements, it is added in batches, with a theoretical number of pouring and transferring operations of 2. The system's internal nonlinear physical loss compensation module automatically extracts the saturated vapor pressure parameter and viscosity coefficient reflecting the high viscosity characteristics of this specific reagent from the local physical property database.

[0068] After acquiring basic business parameters, the dynamic tolerance generation module based on the operational profile quickly performs a personalized assessment of the personnel. The module's variance feature extractor retrieves 25 records of hazardous chemical requisition and return since the operator joined the company. Quantitative assessment reveals that, as the personnel are in the skill development phase, there are significant deviations between actual and theoretical consumption amounts, resulting in a historical operational variance index as high as 0.158, indicating that their consumption stability is lower than the system's built-in safety benchmark. The system inputs the theoretical consumption of 50.00 grams, along with the reagent's hazard level weight, the process complexity rating of the submitted work order, and the high level of historical operational variance, into a pre-built multilayer perceptron network. Based on the aforementioned low stability characteristics, the variance feedback regulator within the multilayer perceptron network significantly expands the upper and lower limits of the baseline consumption allowable range outwards from the theoretical consumption, ultimately outputting a relatively lenient adaptive baseline consumption allowable range of 46.85 grams to 53.15 grams.

[0069] After establishing the dynamic tolerance range, the operator successfully opened the storage box to retrieve the reagent. The multi-dimensional time-series data acquisition module, through the underlying weighing sensing matrix, captured the initial weight of the reagent bottle upon release as 1500.35 grams and initiated full-cycle time-series recording. During the 48 minutes of the reagent's removal from the cabinet for dispensing, the microclimate monitoring node continuously collected local environmental data from the laboratory, recording the average ambient temperature as 22.4 degrees Celsius and the average ambient humidity as 62.1% during this period.

[0070] After the operation was completed, the novice operator returned the reagent bottle to the storage box. The weighing sensing matrix accurately weighed again, capturing the returned weight as 1446.12 grams. The system's underlying calculation showed that the total physical loss due to outbound and inbound shipments was 54.23 grams. Subsequently, the nonlinear physical loss compensation module initiated calculations. The environmental volatile analysis subunit substituted the temperature and humidity data of 22.4 degrees Celsius and 62.1%, along with the reagent's saturated vapor pressure, into the volatile rate calculation logic, estimating the volatile rate under the current environment to be approximately 0.012 grams per minute. Combined with the 48-minute exposure duration, the dynamic volatile loss was calculated to be 0.58 grams. Simultaneously, the fluid adhesion analysis subunit, based on the reagent's high viscosity coefficient, matched and obtained its single-transfer standard residue of up to 0.35 grams from the mapping association table. Combining this with the two theoretical pouring transfers, the container wall residue loss was calculated to be 0.70 grams. The system accurately deducts 0.58 grams of dynamic evaporation loss and 0.70 grams of container wall residue loss from the total physical loss of 54.23 grams, and finally calculates the actual business consumption after removing non-human business factors to be 52.95 grams.

[0071] During the multi-dimensional verification phase, the multi-dimensional verification and interlocking early warning module compared the actual business consumption of 52.95 grams with the previously generated baseline consumption allowable range of 46.85 grams to 53.15 grams. The system determined that the actual business consumption successfully fell within the allowable range. Although the actual consumption of this operation (52.95 grams) was nearly 3 grams higher than the theoretical consumption (50.00 grams), under the strict tolerance standard of experienced operators in Example 1, this value would definitely trigger an interlocking alarm. However, thanks to the dynamic tolerance model automatically amplified for novice operators, the system intelligently determined that the loss was a reasonable, non-malicious error caused by skill unfamiliarity. Therefore, the system directly determined the consumption record as compliant, updated the inventory ledger normally, and released the operation without triggering any invalid physical interlocking or visual cross-troubleshooting instructions. At the same time, the model evolution and self-learning subsystem returned the compliant actual business consumption and personnel tag as incremental data for continuous updates to the operator's proficiency profile, fully demonstrating the intelligence and flexibility of this invention in actual management.

[0072] Example 3: In actual operation, to verify the system's robust security and prevention capabilities against malicious leakage of hazardous chemicals, an example is taken where a routine operator uses a strictly controlled highly toxic or precursor chemical reagent (such as a controlled precursor solvent of a specific concentration) to perform a trace reaction task. The operator undergoes identity registration and biometric verification at the storage node. The system simultaneously analyzes the business declaration work order, obtaining the theoretical usage of the controlled reagent in this task as 20.00 grams and the theoretical number of pouring / transfer operations as 1. The system's internal nonlinear physical loss compensation module automatically extracts the saturated vapor pressure parameter and low-viscosity fluid coefficient of the controlled reagent under the current state.

[0073] The system then activates the dynamic tolerance generation module based on the operational profile. The variance feature extractor retrieves 30 requisition records from the individual's recent assessment period. After quantitative evaluation, the historical operational variance index is 0.045, which is within the normal range of the system's built-in safety benchmark level. The system inputs the theoretical usage of 20.00 grams, the extremely high risk level weight of the reagent, and the aforementioned normal historical operational variance features into the multilayer perceptron network. The system integrates the extremely high risk weight and outputs a relatively tight adaptive benchmark consumption allowable range, namely 19.25 grams to 20.75 grams.

[0074] The operator opened the storage box to retrieve the controlled reagent, and the weighing sensing matrix recorded an initial weight of 500.00 grams upon release. During the 60 minutes of the experiment outside the cabinet, the microclimate monitoring node recorded an average ambient temperature of 20.5 degrees Celsius and an average ambient humidity of 45.0%.

[0075] After the experiment, the operator returned the reagent bottle. The weighing sensing matrix detected a return weight of 450.00 grams, and the system's underlying calculation showed a total physical loss of 50.00 grams. The nonlinear physical loss compensation module quickly initiated calculations: the environmental volatile analysis subunit, based on a temperature and humidity of 20.5 degrees Celsius and 45.0% and saturated vapor pressure, calculated the dynamic volatile loss during that period to be 0.60 grams; the fluid adhesion analysis subunit, based on the reagent's low viscosity characteristics and single-transfer measurements, determined the container wall residue loss to be 0.10 grams. After accurately deducting the 0.60 grams of dynamic volatile loss and the 0.10 grams of container wall residue loss from the total physical loss of 50.00 grams, the system calculated the actual business consumption to be 49.30 grams.

[0076] The multi-dimensional verification and interlocking early warning module compared the actual consumption of 49.30 grams with the allowable range of 19.25 grams to 20.75 grams, determining that the actual consumption significantly exceeded the upper limit. The system immediately activated the visual perception front-end to perform high-frequency image capture of the reagent bottle's appearance and the lower anti-overflow area. After depth detection by the machine vision algorithm, no liquid stain edge features were detected on the outer wall of the container, and the ground spill monitoring image data also showed no abnormal liquid level rise or stain area. Based on this visual verification result of no physical spillage, the system completely ruled out the possibility of reagent spillage due to operator error and decisively classified this abnormal warning signal as a suspected illegal interception type.

[0077] After establishing this high-risk warning type, the system not only refuses to update the normal consumption log for the reagent, but also strictly maintains the physical interlocking blockade command, automatically locking the storage box and associated cabinet doors where the reagent is located. Simultaneously, it directly freezes the operator's subsequent access to hazardous chemicals in the background system. The system then pushes the highest-level emergency alarm signal to the safety and environmental management terminal through the network layer and generates a targeted tracking work order with accompanying time-series data, visual screenshots, and a deviation calculation list. This embodiment fully demonstrates that, after eliminating environmental and physical errors, the present invention, through rigorous data logic and visual troubleshooting mechanisms, can accurately pinpoint abnormal losses and achieve robust and effective safety safeguards.

[0078] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A dangerous chemical intelligent weighing and significant data comparison early warning system, characterized in that, The system includes: The multi-dimensional time-series data acquisition module is used to acquire the time-series characteristics of hazardous chemicals from the time of their release to their return, and to collect the initial weight of the chemicals released, the weight of the chemicals returned, the ambient temperature data, and the ambient humidity data within the period. The nonlinear physical loss compensation module, connected to the multidimensional time-series data acquisition module, is used to extract the saturated vapor pressure and viscosity coefficient of the target chemical. Combined with the ambient temperature data, ambient humidity data, and exposure time from outbound to return, it calculates the dynamic volatilization loss of the target chemical due to natural environmental factors. It also calculates the container wall residue loss due to physical adhesion by combining the viscosity coefficient with the theoretical number of pouring and transferring operations recorded in the business application work order. Based on the difference between the initial outbound weight and the inbound return weight, the total physical loss is obtained, and the dynamic volatilization loss and the container wall residue loss are deducted from the total physical loss to obtain the compensated actual business consumption. The dynamic tolerance generation module based on the operation profile is used to retrieve the historical business declaration and actual consumption comparison records of the current operator within the evaluation period, perform dispersion analysis on the comparison records, extract the historical operation variance features that reflect the stability of the operator's usage, and input the theoretical usage of the current declaration into the deep learning model. Combined with the historical operation variance features, it outputs the upper and lower limits of the adaptive baseline consumption allowable range. The multi-dimensional verification and interlocking early warning module is used to determine whether the actual business consumption after compensation falls between the upper limit and the lower limit. When it is determined that it does not fall between the upper limit and the lower limit, an abnormal early warning signal is generated and a physical interlocking blocking command is triggered.

2. The intelligent weighing and early warning system for hazardous chemicals according to claim 1, characterized in that, The multidimensional time-series data acquisition module is communicatively connected to a weighing sensing matrix and a micro-meteorological monitoring node deployed at the hazardous chemical storage node. The weighing sensing matrix is ​​used to capture weight data when the target chemical is released from the warehouse and returned to generate the initial weight of the release and the return weight. The micro-meteorological monitoring node is used to collect and transmit the ambient temperature data and ambient humidity data in real time.

3. The intelligent weighing and data comparison early warning system for hazardous chemicals according to claim 1, characterized in that, The nonlinear physical loss compensation module includes an environmental volatile analysis subunit. This subunit has built-in volatile rate calculation logic that uses the saturated vapor pressure, ambient temperature data, and ambient humidity data as independent variables. The environmental volatile analysis subunit performs a time-dimensional cumulative calculation of the volatile rate in conjunction with the exposure duration to output the dynamic volatile loss.

4. The intelligent weighing and early warning system for hazardous chemicals according to claim 1, characterized in that, The nonlinear physical loss compensation module includes a fluid adhesion analysis subunit. The fluid adhesion analysis subunit stores a mapping relationship table between the viscosity characteristics of chemicals and the standard residual amount for a single transfer. Based on the viscosity coefficient of the target chemical, the fluid adhesion analysis subunit matches and obtains the corresponding standard residual amount for a single transfer in the mapping relationship table, and combines it with the theoretical number of pouring and transferring times to perform superposition calculation to output the container wall residue loss amount.

5. The intelligent weighing and data comparison early warning system for hazardous chemicals according to claim 1, characterized in that, The dynamic tolerance generation module based on the operation profile includes a variance feature extractor; the variance feature extractor obtains the deviation difference between the actual business consumption and the theoretical consumption of the operator in multiple times within the evaluation period, performs quantitative evaluation based on the deviation of the deviation difference group from the benchmark mean, and uses the dispersion index obtained from the evaluation as the historical operation variance feature.

6. The intelligent weighing and early warning system for hazardous chemicals according to claim 1, characterized in that, The deep learning model is a multilayer perceptron network pre-installed on the control host; the input layer feature vector of the multilayer perceptron network is fixed and includes: the hazard level weight of the target chemical, the process complexity rating recorded in the business declaration work order, and the historical operation variance features.

7. The intelligent weighing and early warning system for hazardous chemicals according to claim 6, characterized in that, The multilayer perceptron network integrates a variance feedback regulator, and the control logic of the variance feedback regulator is as follows: When the historical operational variance characteristics reflect that the operator's usage stability is higher than the built-in safety benchmark level, the variance feedback regulator controls the upper and lower limits to shrink and approach the theoretical usage. When the reflected dosage stability is lower than the safety benchmark level, the variance feedback regulator controls the upper and lower limits to expand outward from the theoretical dosage.

8. The intelligent weighing and data comparison early warning system for hazardous chemicals according to claim 1, characterized in that, The system also includes a visual perception front-end that is communicatively connected to the multidimensional verification and interlocking early warning module; the visual perception front-end is deployed in the flow and return channel of hazardous chemicals and the lower overflow prevention area, and is used to collect container appearance image data and ground spill monitoring image data when the target chemical is returned.

9. The intelligent weighing and data comparison early warning system for hazardous chemicals according to claim 8, characterized in that, The multi-dimensional verification and interlocking early warning module has a pre-set visual cross-obstacle clearing strategy, the execution logic of which is as follows: When the actual business consumption after compensation is greater than the upper limit of the baseline consumption allowable range, the multi-dimensional verification and interlocking early warning module activates the visual perception front end to capture images. Machine vision algorithms are used to determine whether there are edge features of liquid stains in the container appearance image data, and anomalies such as stain area or liquid level increase are compared and judged in the ground spill monitoring image data. If the edge features of the liquid stain are detected or it is determined that there is abnormal spillage in the ground spill monitoring image data, the abnormal warning signal is marked as an accidental spillage loss type, and the physical interlock blocking command is released. If the edge features of the liquid stain are not identified and the ground spill monitoring image data is determined to be normal, the abnormal warning signal will be marked as a suspected illegal interception type, and the physical interlock blocking command will be maintained.

10. The intelligent weighing and early warning system for hazardous chemicals according to claim 1, characterized in that, The system also includes a model evolution and self-learning subsystem connected to the dynamic tolerance generation module based on the operation profile; the model evolution and self-learning subsystem extracts the actual business consumption, the real physical loss reasons and the operator's identity as label data after manual security review, and iteratively trains the network weight parameters of the deep learning model through machine learning algorithms to achieve adaptive dynamic calibration of the upper and lower limits.

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