Differential early warning pushing method and system based on types of chemical reagents

Abstract

The invention discloses a differentiated early warning pushing method and system based on chemical reagent types. The method comprises the following steps: acquiring attribute data and state data of reagents; basic management categories are determined according to attribute data classification, and high-risk reagents are dynamically updated and identified based on state data; analyzing the metamorphism trend of the high-risk reagent, and predicting the residual validity period range; the remaining validity period is compared with the inventory use period, and an early warning signal is generated when the remaining validity period is smaller than the inventory use period; determining a pushing priority according to the reagent characteristics and the real-time state, generating a pushing sequence and sending a notification; and continuously monitoring the reagent state, and re-executing the steps when the key parameters change. According to the invention, classification identification, trend prediction and priority pushing can be carried out in combination with the attribute data and the dynamic state data of the reagents, precise and dynamic management of different reagents is realized, targeted early warning is given out before the reagents fail actually, the reliability of experimental results is guaranteed, resource configuration is optimized, and safety risks are reduced.

Description

Technical Field

[0001] This invention relates to the field of laboratory chemical reagent management technology, and in particular to a differentiated early warning push method and system based on the type of chemical reagent. Background Technology

[0002] Chemical reagent management is a core aspect of laboratory safety and research efficiency, and its management level directly affects the accuracy of experimental results and the personal safety of operators. Currently, the most prominent problem in the industry is that different types of reagents vary significantly in chemical properties, storage requirements, and usage characteristics, while traditional management models often employ uniform standards. This "one-size-fits-all" approach is ill-suited to the complex and diverse needs of reagent management. Specifically, some temperature-sensitive reagents prematurely expire due to fluctuations in storage environment; highly volatile reagents deteriorate rapidly after opening but are not replaced in time; and some reagents with poor stability continue to be used even as their performance deteriorates near their expiration date. These problems not only waste reagent resources but can also lead to experimental failures or data distortion due to the use of expired reagents, and in severe cases, even safety accidents.

[0003] To address the aforementioned issues, existing technologies have proposed several solutions. A common approach is to manage reagents through a laboratory information management system. This system records basic reagent information and expiration dates, and issues reminders when reagents are nearing their expiration date. Some advanced systems have also incorporated barcode or RFID technology to automate the recording of reagent entry and exit, monitor inventory levels, and issue alerts when inventory falls below a set threshold. Furthermore, some solutions attempt to monitor and trigger alarms for environmental anomalies by installing temperature and humidity sensors in storage devices. These technological approaches have, to some extent, improved the standardization and efficiency of reagent management.

[0004] However, existing technologies still have significant shortcomings in addressing the aforementioned problems. First, traditional expiration date reminders are based solely on fixed production dates or shelf lives, failing to reflect actual changes in the reagent's condition after opening. For many reagents whose shelf life is significantly shortened after opening, such static reminders are often too late. Second, environmental monitoring and reagent management are usually separate; even if environmental anomalies are detected, it is difficult to accurately determine the actual impact of these anomalies on a specific reagent. More critically, existing technologies lack the ability to differentiate between the characteristics of different reagents, failing to dynamically adjust management strategies based on the reagent's specific chemical properties, storage requirements, and real-time status, resulting in severely insufficient accuracy and specificity of warnings. For example, for volatile organic solvents, if reminders are only issued based on a fixed expiration date without considering the evaporation rate and storage environment after opening, warnings may not be triggered even when the reagent no longer meets usage requirements. Summary of the Invention

[0005] Therefore, the technical problem to be solved by this invention is to overcome the shortcomings of existing technologies, such as the lack of differentiated processing of reagent characteristics, static reminders failing to reflect the actual state of reagents, and the separation of environmental monitoring and reagent management, which result in insufficient accuracy and poor timeliness of early warnings and difficulty in effectively addressing the risk of reagent failure. This invention provides a differentiated early warning push method based on the type of chemical reagent, which can classify, identify, predict trends, and prioritize pushes by combining reagent attribute data and dynamic status data, thereby achieving precise and dynamic management of different reagents and issuing targeted early warnings before the actual failure of reagents, thus ensuring the reliability of experimental results, optimizing resource allocation, and reducing safety risks.

[0006] To address the aforementioned technical problems, this invention provides a differentiated early warning push method based on the type of chemical reagent, comprising the following steps: Acquire attribute and status data for various reagents. Attribute data includes chemical property data and storage environment requirements data, while status data includes real-time monitoring of the storage environment and usage records. The reagents are classified based on attribute data to determine the basic management category of each reagent, and the basic management category is dynamically updated based on status data to identify reagents in a high-risk state. For reagents in high-risk states, analyze their deterioration trends by combining their attribute data and state data, and predict the remaining shelf life of the reagents. The remaining shelf life range is compared with the preset inventory usage period. When the remaining shelf life is less than the inventory usage period, an early warning signal matching the reagent characteristics is generated. Based on the warning signal, the push priority is determined by combining the characteristics of the reagent and real-time status data, and an optimized push sequence is generated; Send notification messages containing reagent identification and warning information to user terminals according to the push sequence; Continuously monitor changes in reagent status data. When a change is detected in a key parameter affecting the reagent's shelf life, repeat the above steps to update the alert notification.

[0007] In one embodiment of the present invention, the chemical property data includes the reagent's volatility parameters, hygroscopic parameters, thermal stability parameters, photosensitivity parameters, and safety attribute parameters; the storage environment requirement data includes at least one of the reagent's suitable temperature range, suitable humidity range, and light protection requirements; the real-time monitored storage environment data is obtained by collecting data at a preset frequency through temperature sensors, humidity sensors, and light sensors installed in the storage device; the usage process record data includes the reagent's entry and exit time, opening time, remaining quantity information, and user information.

[0008] In one embodiment of the present invention, reagents are classified according to attribute data to determine the basic management category of each reagent, specifically including: Based on their volatility parameters, reagents are classified into volatile, moderately volatile, and non-volatile categories. Reagents are classified into deliquescent, hygroscopic, and hydrophobic categories based on their hygroscopicity parameters. Reagents are classified into thermosensitive, temperature-sensitive, and heat-resistant types based on their thermal stability parameters; and into photosensitivity and light-resistant types based on their photosensitivity parameters. Based on the flash point, corrosiveness level, and toxicity level in the safety attribute parameters, they are classified into flammable, corrosive, and toxic categories, respectively. Based on the temperature range, humidity range, and light protection requirements in the storage environment requirements data, storage conditions are categorized, and a basic management file containing classification labels is established for each reagent in the database.

[0009] In one embodiment of the present invention, the basic management category is dynamically updated based on status data to identify reagents in a high-risk state, specifically including: The real-time monitoring data of the storage environment is compared with the storage environment requirements of the reagents. When the temperature, humidity or light intensity exceeds the suitable range, the reagents are marked as high-risk reagents with abnormal environment. Based on the opening time and the expiration date after opening in the reagent attribute data, if the current time exceeds the expiration date after opening, the reagent will be marked as a high-risk expired reagent.

[0010] In one embodiment of the present invention, for reagents in a high-risk state, the deterioration trend is analyzed by combining their attribute data and state data to predict the remaining shelf life of the reagents, specifically including: The dominant deterioration factor of the reagent is determined based on its chemical property data. The dominant deterioration factor includes one of the following: volatility, hygroscopicity, thermal decomposition, or photodecomposition. Based on the dominant deterioration factors, the basic deterioration rate parameters of the reagent under standard conditions are obtained from the reagent property data; Extract environmental factor data related to the dominant deterioration factors from real-time monitored storage environment data. The environmental factor data includes at least one of temperature, humidity, or light intensity. The basic deterioration rate parameters were corrected based on environmental factor data to obtain the actual deterioration rate of the reagent under the current storage environment. The current remaining amount of reagent is obtained based on the data recorded during the usage process, and the theoretical remaining time of the reagent in the current state is calculated. Based on historical fluctuation data of the storage environment, the range of environmental fluctuations is set, the most unfavorable and most favorable conditions within the range of environmental fluctuations are simulated, and the corresponding remaining time is calculated to obtain the remaining validity period range including the lower limit and the upper limit.

[0011] In one embodiment of the present invention, when the dominant metamorphic factor is volatile matter, the basic metamorphic rate parameter is corrected based on environmental factor data to obtain the actual metamorphic rate, specifically including: Obtain the baseline evaporation rate of the reagent under standard temperature conditions from the reagent property data; Obtain the current temperature value from real-time monitored storage environment data; Based on the preset temperature-evaporation rate correspondence, the evaporation rate correction coefficient corresponding to the current temperature value is determined. The correspondence is obtained by experimentally measuring the evaporation rate of the same type of reagent at different temperatures and fitting the data, reflecting the physical law that the increase in temperature leads to the intensification of molecular motion and thus accelerates evaporation. Based on the baseline evaporation rate and the evaporation rate correction factor, the real-time evaporation rate of the reagent under the current temperature conditions is determined; For volatile reagents marked as humidity-sensitive in the attribute data, the critical humidity value and humidity influence coefficient of the reagent are obtained from the reagent attribute data. The critical humidity value refers to the lowest ambient humidity at which the evaporation rate of the reagent begins to change significantly. The humidity influence coefficient is obtained by experimentally measuring the change range of the evaporation rate of the same type of reagent under different humidity conditions and statistically analyzing it. The current humidity value is obtained from the real-time monitored storage environment data. The current humidity value is compared with the critical humidity value. If the current humidity value exceeds the critical humidity value, the humidity correction coefficient is determined according to the humidity influence coefficient. The final actual evaporation rate is determined based on the real-time evaporation rate and the humidity correction factor.

[0012] In one embodiment of the present invention, when the dominant metamorphic factor is hygroscopicity, the basic metamorphic rate parameter is corrected based on environmental factor data to obtain the actual metamorphic rate, specifically including: Obtain the baseline moisture absorption rate of the reagent under standard humidity conditions from the reagent property data; Obtain the current humidity value from real-time monitored storage environment data; Based on the preset humidity-moisture absorption rate correspondence, determine the moisture absorption rate correction coefficient corresponding to the current humidity value. The correspondence reflects the non-linear growth law of the moisture absorption rate when the ambient humidity exceeds the critical humidity of the reagent. Multiply the baseline moisture absorption rate by the moisture absorption rate correction factor to obtain the actual moisture absorption rate of the reagent under the current humidity conditions. For hygroscopic reagents marked as temperature-sensitive in the attribute data, the temperature influence coefficient of the reagent is obtained from the reagent attribute data. The temperature influence coefficient is obtained by experimentally measuring the change range of the moisture absorption rate of the same type of reagent under different temperature conditions and statistically analyzing it. The current temperature value is obtained from the real-time monitored storage environment data. Based on the actual moisture absorption rate and the temperature influence coefficient, the final actual moisture absorption rate is determined.

[0013] In one embodiment of the present invention, when the dominant metamorphic factor is thermal decomposition, the basic metamorphic rate parameter is corrected based on environmental factor data to obtain the actual metamorphic rate, specifically including: The thermal decomposition kinetic parameters of the reagent are obtained from the reagent property data. These parameters include activation energy, pre-exponential factor, and reaction order. The activation energy represents the energy barrier that the decomposition reaction needs to overcome, and the pre-exponential factor represents the frequency of effective intermolecular collisions. The activation energy and pre-exponential factor are obtained by performing thermogravimetric analysis experiments on the reagent at different heating rates, recording the mass change curves with temperature and time, and fitting these curves. The reaction order represents the dependence of the decomposition rate on the remaining amount and is determined by fitting isothermal thermogravimetric experimental data. Obtain the current temperature value from real-time monitored storage environment data; Based on the activation energy parameter, pre-exponential factor parameter, and current temperature value, the thermal decomposition rate constant under the current temperature condition is determined by an exponential function relationship. The exponential function relationship reflects that as the temperature increases, the molecular thermal motion intensifies, causing the proportion of molecules with energy exceeding the activation energy to increase exponentially. Based on the thermal decomposition rate constant and reaction order, determine the actual decomposition rate of the reagent under the current temperature conditions. The actual decomposition rate represents the attenuation ratio of the effective component of the reagent per unit time. For thermally decomposition reagents marked as humidity-sensitive in the attribute data, the humidity acceleration coefficient of the reagent is obtained from the reagent attribute data. The humidity acceleration coefficient is obtained by experimentally measuring the thermal decomposition rate of the same type of reagent under different humidity conditions and comparing it with the rate under standard humidity conditions. The current humidity value is obtained from the real-time monitored storage environment data. Based on the actual decomposition rate and the humidity acceleration coefficient, the corrected actual decomposition rate is determined.

[0014] In one embodiment of the present invention, when the dominant metamorphic factor is photodecomposition, the basic metamorphic rate parameter is corrected based on environmental factor data to obtain the actual metamorphic rate, specifically including: The photolysis efficiency parameter, light absorption characteristic parameter, and sensitive wavelength range of the reagent are obtained from the reagent property data. The photolysis efficiency parameter represents the proportion of molecules that decompose after the reagent absorbs a unit of light energy. The light absorption characteristic parameter represents the reagent's ability to absorb light of different wavelengths. The photolysis efficiency parameter and light absorption characteristic parameter are obtained by conducting monochromatic light irradiation experiments on the reagent with different wavelengths and intensities, measuring the change in reagent concentration before and after irradiation, and then calculating the results. The sensitive wavelength range refers to the wavelength range in which the reagent has significant light absorption, and is obtained by scanning the absorption spectrum of the reagent with a UV-Vis spectrophotometer. The current light intensity value and light wavelength distribution data are obtained from the real-time monitored storage environment data. The light wavelength distribution data includes the proportion of light intensity in different wavelength ranges. Based on the light absorption characteristic parameters and the sensitive wavelength range, the light wavelength distribution data is screened to determine the proportion of light intensity that can be effectively absorbed by the reagent in the current incident light, and thus obtain the effective absorption ratio. The actual photolysis rate of the reagent under the current illumination conditions is determined based on the effective absorption ratio, photolysis efficiency parameters, and light intensity values. For photodegradation reagents marked as temperature-sensitive in the attribute data, the photodegradation temperature coefficient of the reagent is obtained from the reagent attribute data. The photodegradation temperature coefficient is obtained by experimentally measuring the photodegradation rate of the same type of reagent under different temperature conditions and comparing it with the rate under standard temperature conditions. The current temperature value is obtained from the real-time monitored storage environment data. Based on the actual photodegradation rate and the photodegradation temperature coefficient, the final actual photodegradation rate is determined.

[0015] To address the aforementioned technical problems, this invention also provides a differentiated early warning and push system based on the type of chemical reagent, used to implement the above method, comprising: The data acquisition module is used to acquire attribute data and status data of various reagents. Attribute data includes chemical property data and storage environment requirement data, while status data includes real-time monitoring of storage environment data and usage process record data. The reagent classification module, connected to the data acquisition module, is used to classify reagents based on attribute data, determine the basic management category of each reagent, dynamically update the basic management category based on status data, and identify reagents in a high-risk state. The trend analysis module, connected to the reagent classification module, is used to analyze the deterioration trend of reagents in high-risk states by combining their attribute data and state data, and predict the remaining shelf life of the reagent. The early warning judgment module, connected to the trend analysis module, is used to compare the remaining validity period range with the preset inventory usage cycle. When the remaining validity period is less than the inventory usage cycle, an early warning signal matching the reagent characteristics is generated. The priority processing module, connected to the early warning judgment module, is used to determine the push priority based on the early warning signal, combined with the characteristics of the reagent and real-time status data, and generate an optimized push sequence. The message push module, connected to the priority processing module, is used to send a notification message containing reagent identification and warning information to the user terminal according to the push sequence; The status monitoring module is connected to the data acquisition module and the trend analysis module respectively. It is used to continuously monitor the status data changes of the reagents. When a change is detected in a key parameter that affects the shelf life of the reagent, the reagent classification module, trend analysis module, early warning judgment module, priority processing module and message push module are triggered to re-execute the corresponding operations to update the early warning push content.

[0016] The technical solution of the present invention has the following advantages compared with the prior art: The differentiated early warning push method based on chemical reagent type described in this invention achieves differentiated management of different types of chemical reagents by acquiring and comprehensively analyzing reagent attribute data and real-time status data. It can dynamically identify reagents in high-risk states and, combined with their specific characteristics and environmental conditions, predict their remaining shelf life, thereby generating accurate and targeted early warning signals. This effectively solves the problems of untimely expiration reminders and insufficient early warning accuracy in traditional management models, avoiding resource waste, experimental failures, and potential safety hazards caused by using expired reagents, and improving the intelligence and safety of chemical reagent management. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a flowchart of the steps of the differentiated early warning push method based on the type of chemical reagent of the present invention; Figure 2 This is a flowchart of the steps for predicting the remaining shelf life range of the reagents in this invention; Figure 3 This is a flowchart illustrating the steps of obtaining the actual deterioration rate when the dominant deterioration factor in this invention is volatilization; Figure 4 This is a flowchart illustrating the steps of obtaining the actual deterioration rate when the dominant deterioration factor in this invention is moisture absorption; Figure 5 This is a flowchart of the steps to obtain the actual metamorphism rate when the dominant metamorphic factor in this invention is thermal decomposition. Figure 6 This is a flowchart of the steps to obtain the actual degradation rate when the dominant degradation factor in this invention is photodecomposition; Figure 7This is a structural framework diagram of the differentiated early warning and push system based on the types of chemical reagents of this invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0019] Reference Figure 1 As shown, this invention provides a differentiated early warning push method based on chemical reagent types. First, it acquires attribute data and status data for various reagents. The attribute data includes the reagent's chemical characteristics and storage environment requirements, laying the foundation for subsequent differentiated processing. The status data includes real-time monitored storage environment data and usage records, ensuring accurate understanding of the reagent's current status. Based on this, the method classifies reagents according to the attribute data, determining the basic management category for each reagent. This step achieves an initial differentiation in management strategies for different reagents. Subsequently, the basic management category is dynamically updated based on the real-time acquired status data, enabling timely identification of reagents in high-risk states due to environmental changes or altered usage conditions, such as temperature-sensitive reagents stored in environments with abnormal temperature fluctuations, or volatile reagents that have exceeded their recommended shelf life after opening.

[0020] For reagents identified as being in a high-risk state, the method further analyzes their attribute and state data to delve deeper into their deterioration trends. This process does not simply rely on a single indicator for judgment, but comprehensively considers the inherent characteristics of the reagent, historical usage records, and real-time environmental data to scientifically predict the remaining shelf life of the reagent under current conditions. Compared to static expiration date reminders in existing technologies, this prediction method based on dynamic data analysis can more accurately reflect the actual usability of the reagent. Subsequently, the predicted remaining shelf life is compared with the preset inventory usage period. When it is determined that the remaining shelf life is less than the inventory usage period, it means that the reagent is likely to have expired before being exhausted, at which point an early warning signal matching the reagent's characteristics is generated. The triggering condition for this early warning is closely related to the actual usage status of the reagent, avoiding the problems of invalid or delayed early warnings.

[0021] After generating the warning signal, the method further determines the push priority based on the reagent's characteristics and real-time status data, generating an optimized push sequence. This means that for reagents with higher failure risk and more severe impact, the system can assign a higher warning priority, ensuring that critical issues are addressed first. Finally, a notification message containing reagent identification and warning information is sent to the user terminal according to the push sequence, achieving precise push notification. Simultaneously, the method also incorporates a continuous monitoring mechanism. When a change in a key parameter affecting the reagent's shelf life is detected, the above analysis process is automatically re-executed, forming a closed-loop management system to ensure that the warning content is updated in real-time according to the dynamic changes in the reagent's status.

[0022] The beneficial effects of this invention through the above technical solution include: First, by acquiring and classifying attribute and status data, it solves the problem of lacking differentiated treatment of reagent characteristics in existing technologies, enabling each reagent to receive a management strategy that matches its chemical properties. Second, by combining attribute and status data to predict the remaining shelf life, it overcomes the shortcomings of traditional static alerts that cannot reflect the actual state of the reagent, making the timing of warnings more accurate and reasonable. Third, by dynamically updating the high-risk reagent identification and continuous monitoring mechanism, it solves the problem of separation between environmental monitoring and reagent management in existing technologies, achieving real-time response to changes in reagent status. Furthermore, the priority setting of push notifications allows for the rational allocation of management resources, avoiding information overload that could lead to key warnings being ignored.

[0023] Specifically, in the embodiments, the chemical property data includes the reagent's volatility parameters, hygroscopic parameters, thermal stability parameters, photosensitivity parameters, and safety attribute parameters; the storage environment requirement data includes at least one of the reagent's suitable temperature range, suitable humidity range, and light protection requirements; the real-time monitored storage environment data is obtained by collecting data at a preset frequency through temperature sensors, humidity sensors, and light sensors installed in the storage device; the usage process record data includes the reagent's entry and exit time, opening time, remaining quantity information, and user information.

[0024] Specifically, chemical property data are key information describing the inherent properties of reagents, encompassing their behavioral characteristics under different environmental conditions. Volatility parameters characterize the tendency of a reagent to transform from a liquid or solid state to a gaseous state at certain temperatures and pressures, such as saturated vapor pressure and evaporation rate. This is crucial for assessing the consumption rate and potential environmental pollution risks of reagents. Hygroscopic parameters reflect the ability of a reagent to absorb moisture from the air, such as the moisture absorption rate and critical relative humidity, directly affecting the purity, physical state, and stability of the reagent. Thermal stability parameters measure the ability of a reagent to maintain its chemical structure under heating conditions, such as decomposition temperature and activation energy, and are the basis for predicting the deterioration trend of reagents at high temperatures. Photosensitivity parameters describe the tendency of a reagent to undergo chemical reactions or decomposition under light exposure, such as photolysis efficiency and sensitive wavelength range, and are particularly important for reagents that need to be stored in the dark. Safety attribute parameters involve the hazardous characteristics of reagents, such as flash point, corrosivity level, and toxicity level. These parameters are important bases for safety classification and risk management.

[0025] Storage environment requirements define the ideal storage conditions for each reagent, serving as a benchmark for determining whether the current storage environment is suitable. The suitable temperature range refers to the temperature range a reagent can withstand while maintaining its chemical stability and effectiveness. The suitable humidity range refers to the humidity range a reagent can withstand while maintaining its physical and chemical stability, which is particularly crucial for hygroscopic or deliquescent reagents. Light protection requirements specify whether a reagent needs to be protected from light, and specify limitations on light intensity and wavelength to prevent photodecomposition reactions. These requirements collectively constitute the standards for safe reagent storage.

[0026] Real-time monitoring of the storage environment data is automatically acquired through a sensor network deployed within the reagent storage device. Temperature sensors are used to accurately measure real-time temperature values ​​within the storage space, humidity sensors monitor the relative humidity of the environment, and light sensors detect light intensity or spectral distribution. These sensors automatically collect data at preset frequencies (e.g., every few minutes or hours), ensuring the real-time nature and continuity of environmental data and providing an objective basis for dynamically assessing storage conditions.

[0027] Specifically, based on chemical property data and storage environment requirements data, this application further proposes classifying reagents according to attribute data to determine the basic management category for each reagent. This includes classifying reagents into volatile, moderately volatile, and non-volatile categories based on their volatility parameters. The volatility parameter measures the ease with which a reagent changes from a liquid or solid state to a gaseous state at a specific temperature and pressure. This parameter can be obtained by measuring physicochemical constants such as the reagent's saturated vapor pressure, boiling point, or evaporation rate. For example, reagents with high saturated vapor pressure and low boiling point are typically classified as volatile, while those with high boiling points are classified as non-volatile. Specific classification thresholds can be set based on industry standards or experimental data.

[0028] Reagents are classified into deliquescent, hygroscopic, and hydrophobic categories based on their hygroscopicity parameters. The hygroscopicity parameter reflects the reagent's ability to absorb moisture from its surrounding environment. This parameter can be determined by measuring the reagent's moisture absorption rate, equilibrium moisture absorption, or critical relative humidity under different relative humidity conditions. For example, reagents that significantly increase in mass within a short time under standard humidity conditions can be classified as deliquescent; those that slowly absorb moisture over a long period are hygroscopic; and those that absorb almost no moisture are hydrophobic.

[0029] Reagents are classified into thermosensitive, temperature-sensitive, and heat-resistant categories based on their thermal stability parameters. Thermal stability parameters indicate a reagent's ability to maintain its chemical structure and properties under heating conditions. These parameters can be determined using experimental methods such as thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), measuring the reagent's decomposition temperature, weight loss rate, or thermal decomposition kinetics. For example, reagents that begin to decompose at relatively low temperatures are classified as thermosensitive; those that begin to decompose at higher temperatures are classified as temperature-sensitive; and those that remain stable at even higher temperatures are classified as heat-resistant.

[0030] Reagents are classified into photosensitizing and light-resistant categories based on their photosensitivity parameters. The photosensitivity parameter refers to the degree to which a reagent undergoes a chemical reaction or decomposition under light exposure. This parameter can be assessed by exposing the reagent to light of different wavelengths and intensities and observing indicators such as color changes, the formation of decomposition products, or a decrease in the content of active ingredients. For example, reagents that show significant changes within a short period under visible or ultraviolet light irradiation can be labeled as photosensitizing; those that remain stable after prolonged irradiation are classified as light-resistant.

[0031] Based on safety attribute parameters such as flash point, corrosivity rating, and toxicity rating, reagents are classified into flammable, corrosive, and toxic categories. Safety attribute parameters assess the potential hazards to humans and the environment during storage, transportation, and use. Flash point measures the lowest temperature at which a liquid volatile substance ignites upon contact with an ignition source; according to national or international standards, reagents with flash points below a specific threshold are classified as flammable. Corrosivity rating assesses the degree of damage to skin, eyes, and metals caused by the reagent, using indicators such as pH value and corrosion rate. Toxicity rating assesses the degree of harm to organisms caused by the reagent based on toxicological data such as median lethal dose (LD50) and median lethal concentration (LC50).

[0032] Storage conditions are categorized based on the temperature range, humidity range, and light protection requirements specified in the storage environment requirements data. These requirements define the external environmental conditions necessary for the reagent to maintain its stability and effectiveness. This information is typically found on reagent labels, product instructions, or Material Safety Data Sheets (MSDS). For example, reagents requiring cryogenic storage are categorized as cryogenic storage, those requiring a dry environment are categorized as dry storage, and those requiring light protection are categorized as light-protected storage.

[0033] Based on this, a basic management file containing classification tags is established for each reagent in the database. This file is a structured record storing all classification information and related attribute data of the reagent. Data tables can be created in relational or non-relational databases, with one record corresponding to each reagent. Each record contains the reagent's unique identifier, name, CAS number, and the tag fields for the aforementioned classifications. These tags can be enumerated values ​​or strings, facilitating subsequent querying, filtering, and management.

[0034] The usage process records detailed information about the reagents throughout their entire lifecycle, from warehousing to use. Inbound and outbound times track the reagents' logistics, aiding in traceability and inventory management. Opening time is a crucial point for determining the reagent's shelf life after opening, as the stability of many reagents significantly decreases after opening, drastically shortening their expiration date. Remaining quantity information reflects actual reagent consumption and can be used to predict when reagents will run out. User information facilitates tracing responsibility for reagent use and management processes.

[0035] Building upon the aforementioned classification, this application further proposes dynamically updating basic management categories based on status data to identify reagents in high-risk states. Specifically, this includes comparing real-time monitored storage environment data with the reagent's storage environment requirements data. When temperature, humidity, or light intensity exceeds suitable ranges, the reagent is marked as a high-risk reagent with abnormal environment. This means the system continuously acquires real-time monitored storage environment data collected at preset frequencies by temperature, humidity, and light sensors installed within the storage device. Simultaneously, the system obtains the reagent's storage environment requirements data from its attribute data. This data typically includes the permissible temperature range, humidity range, and light avoidance requirements for safe storage. The system compares the real-time monitored temperature, humidity, or light intensity values ​​with the corresponding storage environment requirements data one by one. Once any environmental parameter (such as temperature, humidity, or light intensity) is found to exceed the reagent's required suitable range (e.g., excessively high or low temperature, excessively high humidity, or excessively high light intensity), the system marks the reagent as a "high-risk reagent with abnormal environment." This marking indicates that the reagent is currently stored in an environment that does not meet its safe storage conditions, and there is a risk of accelerated deterioration.

[0036] Furthermore, based on the opening time and the expiration date after opening in the reagent attribute data, when the current time exceeds the expiration date after opening, the reagent is marked as a high-risk expired reagent. This means that the system records the opening time in the reagent usage record data when the reagent is first opened and used. Simultaneously, the system retrieves the reagent's "expiration date after opening" information from its attribute data. This information is typically given as a time period (e.g., valid for 3 months after opening) or a specific date. The system periodically or in real-time calculates the expiration date obtained by adding the reagent's opening time and expiration date after opening to the current time. Once the current time exceeds this expiration date, meaning the reagent's expiration date after opening has passed, the system marks the reagent as a "high-risk expired reagent." This marking indicates that even if the reagent is within its original packaging expiration period, its effectiveness cannot be guaranteed due to opening and use, and there is a risk of deterioration or ineffectiveness.

[0037] By precisely comparing real-time monitored storage environment data with reagent storage environment requirements, this application can promptly identify potential deterioration risks caused by non-compliance with storage conditions, thereby marking reagents as high-risk reagents due to environmental anomalies. Simultaneously, by recording the reagent's opening time and combining it with its expiration date after opening, this application can accurately determine whether the reagent has reached its expiration date due to opening and use, marking it as an expired high-risk reagent. These specific judgment mechanisms enable the system to more refined and accurately identify different types of high-risk reagents, avoiding delayed or missed warnings due to ambiguous judgments. This provides clear input for subsequent deterioration trend analysis, significantly improving the timeliness and effectiveness of warning pushes, thereby effectively reducing experimental failures or safety hazards caused by reagent deterioration.

[0038] In some of the embodiments described above in this application, a method is proposed to analyze the deterioration trend of reagents in high-risk states by combining their attribute data and state data to predict the remaining shelf life of the reagents. However, in practice, if only a rough deterioration trend analysis is performed, it may not accurately reflect the true shelf life of the reagents in complex and variable storage environments, leading to untimely warnings or false alarms, thus affecting the accuracy and reliability of reagent management.

[0039] In this regard, refer to Figure 2 As shown, this application further proposes a step for analyzing the deterioration trend of reagents in high-risk states by combining their attribute data and state data, and predicting the remaining shelf life of the reagents. Specifically, this includes: determining the dominant deterioration factor of the reagent based on its chemical characteristic data, where the dominant deterioration factor includes one of volatilization, hygroscopicity, thermal decomposition, or photodecomposition; obtaining the basic deterioration rate parameter of the reagent under standard conditions from the reagent attribute data based on the dominant deterioration factor; extracting environmental factor data related to the dominant deterioration factor from real-time monitored storage environment data, where the environmental factor data includes at least one of temperature, humidity, or light intensity; correcting the basic deterioration rate parameter based on the environmental factor data to obtain the actual deterioration rate of the reagent under the current storage environment; obtaining the current remaining quantity of the reagent based on usage record data and calculating the theoretical remaining time of the reagent in its current state; setting an environmental fluctuation range based on historical fluctuation data of the storage environment, simulating the most unfavorable and most favorable conditions within the environmental fluctuation range, and calculating the corresponding remaining time to obtain the remaining shelf life range including the lower and upper limits.

[0040] Specifically, the deterioration of chemical reagents is often caused by a combination of factors, but usually one or several dominant factors exist. For example, for reagents with low boiling points and high vapor pressures, volatilization is the dominant factor; for reagents containing easily hydrolyzed groups or with strong hydrophilicity, hygroscopicity is the dominant factor; for reagents containing unstable chemical bonds or prone to thermal decomposition, thermal decomposition is the dominant factor; and for reagents containing chromophores or prone to photochemical reactions, photodecomposition is the dominant factor. By analyzing the chemical property data of reagents, we can preliminarily determine the most likely type of deterioration under common storage conditions, thereby focusing analytical resources and improving prediction efficiency and accuracy.

[0041] Based on this, the fundamental degradation rate parameter is the rate at which the active ingredient of a reagent changes over time under specific standard conditions (e.g., standard temperature, humidity, light intensity, etc.). These parameters are typically obtained through accelerated aging experiments or long-term stability experiments under strictly controlled laboratory conditions and recorded in the reagent's property data. For example, for volatile reagents, it could be the evaporation rate at standard temperature; for hygroscopic reagents, it could be the moisture absorption rate at standard humidity; for thermally decomposable reagents, it could be the decomposition rate constant at standard temperature; and for photodecomposable reagents, it could be the photolysis rate under standard light conditions. These parameters form the basis for subsequent corrections and predictions.

[0042] Simultaneously, for the identified dominant degradation factors, it is necessary to filter out environmental parameters that directly affect the degradation process from the real-time monitored storage environment data. For example, if the dominant degradation factor is volatilization or thermal decomposition, then temperature data is the primary focus; if it is moisture absorption, then humidity data is the primary focus; and if it is photodecomposition, then light intensity data is the primary focus. This environmental factor data is collected in real time by sensors within the storage device, providing dynamic input for subsequent rate correction.

[0043] Given that the rate of reagent deterioration is not constant but significantly influenced by environmental conditions—for example, increased temperature typically accelerates volatilization and chemical reaction rates, while increased humidity accelerates moisture absorption—it is necessary to establish mathematical models or empirical relationships (such as the Arrhenius equation or humidity influence models) between environmental factors and the deterioration rate. This allows for the dynamic adjustment of basic deterioration rate parameters using real-time monitored environmental factor data, thereby obtaining a deterioration rate that better reflects the current actual storage environment. This makes the prediction results more real-time and accurate.

[0044] Furthermore, the remaining shelf life of a reagent is not only related to the rate of deterioration but also closely related to its actual remaining quantity. Process log data (such as inbound / outbound records, opening times, and estimated remaining quantities) provides information on the current physical state of the reagent. Combined with the obtained actual rate of deterioration, the theoretical time required for the reagent to decrease from its current remaining quantity to the expiration threshold (e.g., the content of the active ingredient falls below a specified standard or significant changes in physical properties) at the current rate of deterioration can be calculated. This provides an initial estimate for subsequent shelf-life prediction.

[0045] Furthermore, actual storage environments are not completely stable but fluctuate to some extent. To provide more reliable early warnings, this volatility needs to be considered. By analyzing historical monitoring data of the storage environment, the fluctuation range and frequency of environmental factors such as temperature, humidity, and light intensity can be statistically determined. Based on this, the most unfavorable conditions (e.g., sustained high temperature and humidity, strong light, leading to the fastest deterioration rate) and the most favorable conditions (e.g., sustained low temperature and humidity, light avoidance, leading to the slowest deterioration rate) that may occur within the historical fluctuation range are simulated, and the remaining shelf life of the reagent under these extreme conditions is calculated. Finally, these two calculation results are used as the lower and upper limits of the remaining shelf life, forming a prediction interval, thereby providing more comprehensive and robust early warning information.

[0046] To further illustrate the need for different analytical strategies for predicting metamorphic trends under the influence of different dominant metamorphic factors, this invention discloses specific methods for obtaining the actual metamorphic rate when the dominant factors are volatilization, hygroscopicity, thermal decomposition, and photodecomposition.

[0047] Reference Figure 3 As shown, this application further proposes a specific method for obtaining the actual deterioration rate by correcting the basic deterioration rate parameter based on environmental factor data when the dominant deterioration factor is volatilization. This method includes obtaining the baseline volatilization rate of the reagent under standard temperature conditions from reagent property data. The baseline volatilization rate is the amount or rate of volatilization of the reagent per unit time at a specific standard temperature, typically determined through controlled laboratory experiments and stored in the reagent property data as the starting point for subsequent calculations. Simultaneously, the current temperature value is obtained from real-time monitored storage environment data, which is collected in real-time by a temperature sensor deployed within the storage device.

[0048] Subsequently, based on the preset temperature-evaporation rate correlation, a evaporation rate correction coefficient corresponding to the current temperature value is determined. This correlation is obtained by experimentally measuring and fitting the evaporation rates of the same type of reagent at different temperatures, reflecting the physical law that increased temperature leads to intensified molecular motion, thereby accelerating evaporation. Based on the obtained baseline evaporation rate and the determined evaporation rate correction coefficient, the real-time evaporation rate of the reagent under the current temperature conditions can be determined.

[0049] Furthermore, for volatile reagents marked as humidity-sensitive in the attribute data, this method obtains the critical humidity value and humidity influence coefficient of the reagent from the reagent attribute data. The critical humidity value refers to the lowest ambient humidity at which the evaporation rate of the reagent begins to change significantly, while the humidity influence coefficient is obtained by experimentally measuring and statistically analyzing the evaporation rate changes of the same type of reagent under different humidity conditions. Next, the current humidity value is obtained from the real-time monitored stored environmental data and compared with the critical humidity value. If the current humidity value exceeds the critical humidity value, a humidity correction coefficient is determined based on the humidity influence coefficient. Finally, based on the determined real-time evaporation rate and humidity correction coefficient, the final actual evaporation rate is determined.

[0050] Reference Figure 4 As shown, this application further proposes specific steps for correcting the basic deterioration rate parameter based on environmental factor data to obtain the actual deterioration rate when the dominant deterioration factor is hygroscopicity. This step first obtains the baseline hygroscopic rate of the reagent under standard humidity conditions from the reagent property data; then, it obtains the current humidity value from real-time monitored storage environment data; next, it determines the hygroscopic rate correction coefficient corresponding to the current humidity value based on a preset humidity-hygroscopic rate correspondence, which reflects the non-linear growth law of the hygroscopic rate when the ambient humidity exceeds the reagent's critical humidity; based on this, the baseline hygroscopic rate is multiplied by the hygroscopic rate correction coefficient to obtain the actual hygroscopic rate of the reagent under the current humidity conditions; furthermore, for hygroscopic reagents marked as temperature-sensitive in the property data, the temperature influence coefficient of the reagent is obtained from the reagent property data. This temperature influence coefficient is obtained by experimentally measuring and statistically analyzing the hygroscopic rate variation of the same type of reagent under different temperature conditions; simultaneously, the current temperature value is obtained from real-time monitored storage environment data; finally, the final actual hygroscopic rate is determined based on the actual hygroscopic rate and the temperature influence coefficient.

[0051] Specifically, the baseline moisture absorption rate of a reagent under standard humidity conditions refers to the amount of moisture absorbed by the reagent per unit time under standard environmental conditions such as a specific relative humidity (e.g., 50% RH) and temperature (e.g., 25°C). This parameter is usually obtained through standard laboratory testing methods or provided by the reagent manufacturer and represents the inherent moisture absorption tendency of the reagent. The current humidity value refers to the ambient humidity data collected in real time by a humidity sensor deployed in the storage device. This data is dynamically changing and reflects the instantaneous humidity status of the environment in which the reagent is located. The humidity-moisture absorption rate correspondence is a mathematical model or lookup table describing how the reagent's moisture absorption rate changes with ambient humidity. The moisture absorption rate correction factor is a multiplier factor that adjusts the baseline moisture absorption rate to the moisture absorption rate under the current humidity conditions based on this correspondence. This correspondence is usually established by conducting moisture absorption experiments on the same type of reagent under different humidity conditions, monitoring the changes in its mass or water content over time, and fitting a non-linear growth function curve, paying particular attention to the accelerating effect of the moisture absorption rate when the ambient humidity exceeds the reagent's critical humidity (i.e., the humidity threshold at which the moisture absorption rate begins to increase significantly). The actual moisture absorption rate refers to the reagent's moisture absorption rate, corrected from the baseline moisture absorption rate after considering the influence of current ambient humidity. It more accurately reflects the actual rate of deterioration of the reagent under current humidity conditions. Temperature-sensitive hygroscopic reagents are those whose moisture absorption rate is affected not only by humidity but also significantly by temperature. For example, increased temperature may accelerate the diffusion of water molecules or change the adsorption capacity of the reagent surface, thus affecting the moisture absorption process. The temperature influence coefficient is a parameter that quantifies the effect of temperature on the moisture absorption rate. This coefficient is obtained by conducting moisture absorption experiments on the same type of reagent under different temperature conditions (keeping humidity constant), comparing the changes in their moisture absorption rates, and using statistical analysis methods (such as regression analysis). It reflects the accelerating or decelerating effect of temperature changes on the moisture absorption rate. The final actual moisture absorption rate is the final corrected value for the reagent's moisture absorption rate after considering the dual effects of current ambient humidity and temperature. This rate is the most accurate quantitative indicator of the reagent's deterioration due to moisture absorption under current conditions.

[0052] Reference Figure 5As shown, this application further proposes a scheme to obtain the actual degradation rate by correcting the basic degradation rate parameters based on environmental factor data when the dominant degradation factor is thermal decomposition. Specifically, this includes: First, obtaining the thermal decomposition kinetic parameters of the reagent from the reagent property data. These thermal decomposition kinetic parameters include activation energy parameters, pre-exponential factor parameters, and reaction order. The activation energy parameter represents the energy barrier that the decomposition reaction needs to overcome, and the pre-exponential factor parameter represents the frequency of effective intermolecular collisions. The activation energy parameter and pre-exponential factor parameter are usually obtained by performing thermogravimetric analysis experiments on the reagent at different heating rates, recording the mass change curves with temperature and time, and calculating based on these curves. The reaction order represents the dependence of the decomposition rate on the remaining amount, and is usually determined by fitting isothermal thermogravimetric experimental data. These kinetic parameters are intrinsic properties that accurately describe the thermal decomposition behavior of the reagent, providing a scientific basis for subsequent rate calculations.

[0053] Simultaneously, the current temperature value is obtained from real-time monitored storage environment data. Temperature is one of the most critical environmental factors affecting the thermal decomposition reaction rate, and obtaining the current temperature value in real time and accurately is a prerequisite for precise rate correction. This temperature value is typically collected in real time by temperature sensors deployed within the reagent storage device and transmitted to the processing system.

[0054] Next, based on the obtained activation energy parameters, pre-exponential factor parameters, and current temperature value, the thermal decomposition rate constant under the current temperature conditions is determined using an exponential function relationship. This exponential function relationship (such as the Arrhenius equation) can accurately reflect the intensified molecular thermal motion as the temperature increases, causing the proportion of molecules with energy exceeding the activation energy to increase exponentially, thus making the calculation of the thermal decomposition rate constant more consistent with physicochemical principles.

[0055] Based on this, the actual decomposition rate of the reagent under the current temperature conditions is determined according to the calculated thermal decomposition rate constant and the known reaction order. This actual decomposition rate represents the proportion of the reagent's effective components decaying per unit time. By combining the rate constant and the reaction order, the true decomposition rate of the reagent under the current environment can be calculated according to the corresponding reaction kinetic equations (such as the rate equation), thereby quantifying the deterioration trend.

[0056] Furthermore, for reagents marked as humidity-sensitive in the attribute data for thermal decomposition, this application further obtains the humidity acceleration factor from the reagent attribute data. The humidity acceleration factor is obtained by experimentally measuring the thermal decomposition rate of the same type of reagent under different humidity conditions and comparing it with the rate under standard humidity conditions; it quantifies the accelerating effect of humidity on the thermal decomposition process. Simultaneously, the current humidity value is obtained from real-time monitored storage environment data. Finally, based on the previously determined actual decomposition rate and this humidity acceleration factor, the corrected final actual decomposition rate is determined. This step ensures a more comprehensive and accurate assessment of the actual deterioration rate of the reagent, even when humidity has a significant impact on thermal decomposition.

[0057] Reference Figure 6 As shown, this application further proposes a specific method for obtaining the actual degradation rate by correcting the basic degradation rate parameters based on environmental factor data when photodecomposition is the dominant degradation factor. This method includes the following steps: First, obtain the photolysis efficiency parameter, light absorption characteristic parameter, and sensitive wavelength range of the reagent from the reagent property data. The photolysis efficiency parameter quantifies the proportion of molecules that decompose after absorbing a unit of light energy, while the light absorption characteristic parameter describes the reagent's ability to absorb light of different wavelengths. These parameters are typically obtained by conducting monochromatic light irradiation experiments on the reagent at different wavelengths and intensities, measuring the change in reagent concentration before and after irradiation, and then calculating the results. The sensitive wavelength range refers to the wavelength range in which the reagent exhibits significant light absorption, which is usually determined by scanning the reagent's absorption spectrum using a UV-Vis spectrophotometer. These parameters are fundamental to understanding how the reagent interacts with light and undergoes decomposition, providing crucial intrinsic chemical information for subsequent accurate calculation of the photodecomposition rate.

[0058] Secondly, the current light intensity and wavelength distribution data are obtained from the real-time monitored storage environment data. The wavelength distribution data typically includes the proportion of light intensity within different wavelength ranges. This data is collected in real-time by professional light sensors or spectral sensors installed in the storage device, ensuring the timeliness and accuracy of ambient light information, which is crucial for assessing the impact of current ambient light conditions on the reagents.

[0059] Next, based on the acquired light absorption characteristic parameters and sensitive wavelength range, the real-time monitored light wavelength distribution data is filtered. This filtering process aims to determine what proportion of light intensity in the current incident light can be effectively absorbed by the reagent and trigger decomposition, thus obtaining the effective absorption ratio. This step is crucial in connecting the inherent photodecomposition properties of the reagent with actual environmental lighting conditions, avoiding the one-sidedness of simply using total light intensity to assess the risk of photodecomposition.

[0060] Subsequently, by comprehensively considering the effective absorption ratio, the photolysis efficiency parameter of the reagent, and the current light intensity, the actual photolysis rate of the reagent under the current light conditions was determined. This rate accurately reflects the decomposition speed of the reagent under specific light conditions, providing a more accurate input for subsequent prediction of remaining shelf life.

[0061] Finally, for photodegradable reagents marked as temperature-sensitive in the attribute data, further correction is required. At this stage, the photodegradation temperature coefficient of the reagent is obtained from the reagent attribute data. This coefficient is obtained by experimentally measuring the photodegradation rate of the same type of reagent under different temperature conditions and comparing it with the rate under standard temperature conditions. Simultaneously, the current temperature value is obtained from real-time monitored storage environment data. Based on the previously calculated actual photodegradation rate and this photodegradation temperature coefficient, the final actual photodegradation rate is determined. This correction step considers the synergistic effect of temperature on the photodegradation process, making the final decomposition rate prediction closer to reality.

[0062] Reference Figure 7 As shown, in order to implement the above method, this embodiment also provides a differentiated early warning push system based on the type of chemical reagent, including: The data acquisition module is used to acquire attribute data and status data of various reagents. Attribute data includes chemical property data and storage environment requirement data, while status data includes real-time monitoring of storage environment data and usage process record data. The reagent classification module, connected to the data acquisition module, is used to classify reagents based on attribute data, determine the basic management category of each reagent, dynamically update the basic management category based on status data, and identify reagents in a high-risk state. The trend analysis module, connected to the reagent classification module, is used to analyze the deterioration trend of reagents in high-risk states by combining their attribute data and state data, and predict the remaining shelf life of the reagent. The early warning judgment module, connected to the trend analysis module, is used to compare the remaining validity period range with the preset inventory usage cycle. When the remaining validity period is less than the inventory usage cycle, an early warning signal matching the reagent characteristics is generated. The priority processing module, connected to the early warning judgment module, is used to determine the push priority based on the early warning signal, combined with the characteristics of the reagent and real-time status data, and generate an optimized push sequence. The message push module, connected to the priority processing module, is used to send a notification message containing reagent identification and warning information to the user terminal according to the push sequence; The status monitoring module is connected to the data acquisition module and the trend analysis module respectively. It is used to continuously monitor the status data changes of the reagents. When a change is detected in a key parameter that affects the shelf life of the reagent, the reagent classification module, trend analysis module, early warning judgment module, priority processing module and message push module are triggered to re-execute the corresponding operations to update the early warning push content.

[0063] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A differentiated early warning push method based on the type of chemical reagent, characterized in that, Includes the following steps: Acquire attribute and status data for various reagents. Attribute data includes chemical property data and storage environment requirements data, while status data includes real-time monitoring of the storage environment and usage records. The reagents are classified based on attribute data to determine the basic management category of each reagent, and the basic management category is dynamically updated based on status data to identify reagents in a high-risk state. For reagents in high-risk states, analyze their deterioration trends by combining their attribute data and state data, and predict the remaining shelf life of the reagents. The remaining shelf life range is compared with the preset inventory usage period. When the remaining shelf life is less than the inventory usage period, an early warning signal matching the reagent characteristics is generated. Based on the warning signal, the push priority is determined by combining the characteristics of the reagent and real-time status data, and an optimized push sequence is generated; Send notification messages containing reagent identification and warning information to user terminals according to the push sequence; Continuously monitor changes in reagent status data. When a change is detected in a key parameter affecting the reagent's shelf life, repeat the above steps to update the alert notification.

2. The differentiated early warning push method based on chemical reagent type according to claim 1, characterized in that: Chemical property data includes the reagent's volatility parameters, hygroscopic parameters, thermal stability parameters, photosensitivity parameters, and safety attribute parameters; storage environment requirement data includes at least one of the reagent's suitable temperature range, suitable humidity range, and light protection requirements; real-time monitoring of storage environment data is obtained by temperature sensors, humidity sensors, and light sensors installed in the storage device at preset frequencies; usage process record data includes the reagent's entry and exit times, opening time, remaining quantity information, and user information.

3. The differentiated early warning push method based on chemical reagent type according to claim 2, characterized in that: Reagents are categorized based on attribute data to determine the basic management category for each reagent, specifically including: Based on their volatility parameters, reagents are classified into volatile, moderately volatile, and non-volatile categories. Reagents are classified into deliquescent, hygroscopic, and hydrophobic categories based on their hygroscopicity parameters. Reagents are classified into thermosensitive, temperature-sensitive, and heat-resistant types based on their thermal stability parameters; and into photosensitivity and light-resistant types based on their photosensitivity parameters. Based on the flash point, corrosiveness level, and toxicity level in the safety attribute parameters, they are classified into flammable, corrosive, and toxic categories, respectively. Based on the temperature range, humidity range, and light protection requirements in the storage environment requirements data, storage conditions are categorized, and a basic management file containing classification labels is established for each reagent in the database.

4. The differentiated early warning push method based on chemical reagent type according to claim 2, characterized in that: Based on status data, the basic management categories are dynamically updated to identify reagents in a high-risk state, specifically including: The real-time monitoring data of the storage environment is compared with the storage environment requirements of the reagents. When the temperature, humidity or light intensity exceeds the suitable range, the reagents are marked as high-risk reagents with abnormal environment. Based on the opening time and the expiration date after opening in the reagent attribute data, if the current time exceeds the expiration date after opening, the reagent will be marked as a high-risk expired reagent.

5. The differentiated early warning push method based on chemical reagent type according to claim 1, characterized in that: For reagents in high-risk states, the deterioration trend is analyzed by combining their attribute data and state data to predict the remaining shelf life of the reagents, specifically including: The dominant deterioration factor of the reagent is determined based on its chemical property data. The dominant deterioration factor includes one of the following: volatility, hygroscopicity, thermal decomposition, or photodecomposition. Based on the dominant deterioration factors, the basic deterioration rate parameters of the reagent under standard conditions are obtained from the reagent property data; Extract environmental factor data related to the dominant deterioration factors from real-time monitored storage environment data. The environmental factor data includes at least one of temperature, humidity, or light intensity. The basic deterioration rate parameters were corrected based on environmental factor data to obtain the actual deterioration rate of the reagent under the current storage environment. The current remaining amount of reagent is obtained based on the data recorded during the usage process, and the theoretical remaining time of the reagent in the current state is calculated. Based on historical fluctuation data of the storage environment, the range of environmental fluctuations is set, the most unfavorable and most favorable conditions within the range of environmental fluctuations are simulated, and the corresponding remaining time is calculated to obtain the remaining validity period range including the lower limit and the upper limit.

6. The differentiated early warning push method based on chemical reagent type according to claim 5, characterized in that: When the dominant metamorphic factor is volatile matter, the basic metamorphic rate parameters are corrected based on environmental factor data to obtain the actual metamorphic rate, which specifically includes: Obtain the baseline evaporation rate of the reagent under standard temperature conditions from the reagent property data; Obtain the current temperature value from real-time monitored storage environment data; Based on the preset temperature-evaporation rate correspondence, the evaporation rate correction coefficient corresponding to the current temperature value is determined. The correspondence is obtained by experimentally measuring the evaporation rate of the same type of reagent at different temperatures and fitting the data, reflecting the physical law that the increase in temperature leads to the intensification of molecular motion and thus accelerates evaporation. Based on the baseline evaporation rate and the evaporation rate correction factor, the real-time evaporation rate of the reagent under the current temperature conditions is determined; For volatile reagents marked as humidity-sensitive in the attribute data, the critical humidity value and humidity influence coefficient of the reagent are obtained from the reagent attribute data. The critical humidity value refers to the lowest ambient humidity at which the evaporation rate of the reagent begins to change significantly. The humidity influence coefficient is obtained by experimentally measuring the change range of the evaporation rate of the same type of reagent under different humidity conditions and statistically analyzing it. The current humidity value is obtained from the real-time monitored storage environment data. The current humidity value is compared with the critical humidity value. If the current humidity value exceeds the critical humidity value, the humidity correction coefficient is determined according to the humidity influence coefficient. The final actual evaporation rate is determined based on the real-time evaporation rate and the humidity correction factor.

7. The differentiated early warning push method based on chemical reagent type according to claim 5, characterized in that: When the dominant metamorphic factor is hygroscopicity, the basic metamorphic rate parameters are corrected based on environmental factor data to obtain the actual metamorphic rate, which specifically includes: Obtain the baseline moisture absorption rate of the reagent under standard humidity conditions from the reagent property data; Obtain the current humidity value from real-time monitored storage environment data; Based on the preset humidity-moisture absorption rate correspondence, determine the moisture absorption rate correction coefficient corresponding to the current humidity value. The correspondence reflects the non-linear growth law of the moisture absorption rate when the ambient humidity exceeds the critical humidity of the reagent. Multiply the baseline moisture absorption rate by the moisture absorption rate correction factor to obtain the actual moisture absorption rate of the reagent under the current humidity conditions. For hygroscopic reagents marked as temperature-sensitive in the attribute data, the temperature influence coefficient of the reagent is obtained from the reagent attribute data. The temperature influence coefficient is obtained by experimentally measuring the change range of the moisture absorption rate of the same type of reagent under different temperature conditions and statistically analyzing it. The current temperature value is obtained from the real-time monitored storage environment data. Based on the actual moisture absorption rate and the temperature influence coefficient, the final actual moisture absorption rate is determined.

8. The differentiated early warning push method based on chemical reagent type according to claim 5, characterized in that: When thermal decomposition is the dominant metamorphic factor, the basic metamorphic rate parameters are corrected based on environmental factor data to obtain the actual metamorphic rate, which specifically includes: The thermal decomposition kinetic parameters of the reagent are obtained from the reagent property data. These parameters include activation energy, pre-exponential factor, and reaction order. The activation energy represents the energy barrier that the decomposition reaction needs to overcome, and the pre-exponential factor represents the frequency of effective intermolecular collisions. The activation energy and pre-exponential factor are obtained by performing thermogravimetric analysis experiments on the reagent at different heating rates, recording the mass change curves with temperature and time, and fitting these curves. The reaction order represents the dependence of the decomposition rate on the remaining amount and is determined by fitting isothermal thermogravimetric experimental data. Obtain the current temperature value from real-time monitored storage environment data; Based on the activation energy parameter, pre-exponential factor parameter, and current temperature value, the thermal decomposition rate constant under the current temperature condition is determined by an exponential function relationship. The exponential function relationship reflects that as the temperature increases, the molecular thermal motion intensifies, causing the proportion of molecules with energy exceeding the activation energy to increase exponentially. Based on the thermal decomposition rate constant and reaction order, determine the actual decomposition rate of the reagent under the current temperature conditions. The actual decomposition rate represents the attenuation ratio of the effective component of the reagent per unit time. For thermally decomposition reagents marked as humidity-sensitive in the attribute data, the humidity acceleration coefficient of the reagent is obtained from the reagent attribute data. The humidity acceleration coefficient is obtained by experimentally measuring the thermal decomposition rate of the same type of reagent under different humidity conditions and comparing it with the rate under standard humidity conditions. The current humidity value is obtained from the real-time monitored storage environment data. Based on the actual decomposition rate and the humidity acceleration coefficient, the corrected actual decomposition rate is determined.

9. The differentiated early warning push method based on chemical reagent type according to claim 5, characterized in that: When the dominant metamorphic factor is photodecomposition, the basic metamorphic rate parameters are corrected based on environmental factor data to obtain the actual metamorphic rate, which specifically includes: The photolysis efficiency parameter, light absorption characteristic parameter, and sensitive wavelength range of the reagent are obtained from the reagent property data. The photolysis efficiency parameter represents the proportion of molecules that decompose after the reagent absorbs a unit of light energy. The light absorption characteristic parameter represents the reagent's ability to absorb light of different wavelengths. The photolysis efficiency parameter and light absorption characteristic parameter are obtained by conducting monochromatic light irradiation experiments on the reagent with different wavelengths and intensities, measuring the change in reagent concentration before and after irradiation, and then calculating the results. The sensitive wavelength range refers to the wavelength range in which the reagent has significant light absorption, and is obtained by scanning the absorption spectrum of the reagent with a UV-Vis spectrophotometer. The current light intensity value and light wavelength distribution data are obtained from the real-time monitored storage environment data. The light wavelength distribution data includes the proportion of light intensity in different wavelength ranges. Based on the light absorption characteristic parameters and the sensitive wavelength range, the light wavelength distribution data is screened to determine the proportion of light intensity that can be effectively absorbed by the reagent in the current incident light, and thus obtain the effective absorption ratio. The actual photolysis rate of the reagent under the current illumination conditions is determined based on the effective absorption ratio, photolysis efficiency parameters, and light intensity values. For photodegradation reagents marked as temperature-sensitive in the attribute data, the photodegradation temperature coefficient of the reagent is obtained from the reagent attribute data. The photodegradation temperature coefficient is obtained by experimentally measuring the photodegradation rate of the same type of reagent under different temperature conditions and comparing it with the rate under standard temperature conditions. The current temperature value is obtained from the real-time monitored storage environment data. Based on the actual photodegradation rate and the photodegradation temperature coefficient, the final actual photodegradation rate is determined.

10. A differentiated early warning and push system based on the type of chemical reagent, used to implement the method described in any one of claims 1 to 9, characterized in that: include: The data acquisition module is used to acquire attribute data and status data of various reagents. Attribute data includes chemical property data and storage environment requirement data, while status data includes real-time monitoring of storage environment data and usage process record data. The reagent classification module, connected to the data acquisition module, is used to classify reagents based on attribute data, determine the basic management category of each reagent, dynamically update the basic management category based on status data, and identify reagents in a high-risk state. The trend analysis module, connected to the reagent classification module, is used to analyze the deterioration trend of reagents in high-risk states by combining their attribute data and state data, and predict the remaining shelf life of the reagent. The early warning judgment module, connected to the trend analysis module, is used to compare the remaining validity period range with the preset inventory usage cycle. When the remaining validity period is less than the inventory usage cycle, an early warning signal matching the reagent characteristics is generated. The priority processing module, connected to the early warning judgment module, is used to determine the push priority based on the early warning signal, combined with the characteristics of the reagent and real-time status data, and generate an optimized push sequence. The message push module, connected to the priority processing module, is used to send a notification message containing reagent identification and warning information to the user terminal according to the push sequence; The status monitoring module is connected to the data acquisition module and the trend analysis module respectively. It is used to continuously monitor the status data changes of the reagents. When a change is detected in a key parameter that affects the shelf life of the reagent, the reagent classification module, trend analysis module, early warning judgment module, priority processing module and message push module are triggered to re-execute the corresponding operations to update the early warning push content.

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