Dangerous chemical substance basic informatization data analysis system and method

Abstract

The invention belongs to the technical field of hazardous chemical substance analysis, and particularly relates to a hazardous chemical substance basic informatization data analysis system and method.The hazardous chemical substance basic informatization data analysis system comprises a hazardous substance basic information data acquisition module, a data storage module, a data analysis module, an early warning mechanism module, a visual display module and a user identity information management module; the system is used for collecting relevant basic information of dangerous goods in real time, and the basic information comprises material properties, storage conditions, real-time monitoring data and historical data. The hazardous chemical substance storage environment is monitored in real time through the sensor assembly, the system can find potential risks such as temperature, humidity and gas concentration abnormities in time, so that early warning is given out in advance, accidents are reduced, meanwhile, the early warning mechanism module is arranged, intelligent monitoring and early warning of environment parameters are achieved, and the safety and reliability of the system are improved. A worker can customize an early warning rule according to own requirements, so that timely response is ensured when the environmental parameters are abnormal, and safety is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of hazardous materials analysis technology, specifically to a basic information data analysis system and method for hazardous chemicals. Background Technology

[0002] Hazardous chemicals refer to highly toxic chemicals and other chemicals that have properties such as toxicity, corrosiveness, explosiveness, flammability, and combustion-supporting, and that are harmful to human health, facilities, and the environment.

[0003] With rapid industrialization, the use and management of hazardous chemicals have become increasingly important. Hazardous chemicals pose safety hazards during production, transportation, and storage; therefore, establishing an efficient information-based data analysis system is crucial for improving hazardous chemical management and ensuring safe production.

[0004] The existing technology has the following defects or problems:

[0005] Existing hazardous chemical management relies heavily on manual recording and decentralized storage, making data integration difficult. Furthermore, existing management methods cannot provide real-time feedback, resulting in a slow response after an accident.

[0006] Existing technologies often only provide basic data records and lack in-depth data analysis capabilities.

[0007] It should be noted that the above content falls within the inventor's technical knowledge and does not necessarily constitute prior art. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a basic information data analysis system and method for hazardous chemicals, solving the current problems.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a basic information data analysis system for hazardous chemicals, comprising:

[0010] The hazardous materials basic information data acquisition module is used to collect relevant basic information about hazardous materials in real time. The basic information includes material properties, storage conditions, real-time monitoring data and historical data.

[0011] The data storage module has a built-in cloud storage unit, which can classify and store the collected data to ensure data security and accessibility.

[0012] The data analysis module is capable of processing and analyzing stored data based on big data. The data analysis module has built-in statistical analysis tools to provide basic statistical analysis functions and help users understand data characteristics.

[0013] The early warning mechanism module has built-in early warning rules and alarm thresholds. When an abnormal situation occurs, it will issue an early warning in a timely manner and provide corresponding handling suggestions.

[0014] The visualization module visualizes the analysis results generated by the data analysis module through charts, allowing staff to intuitively understand the management status of hazardous chemicals.

[0015] The user identity information management module is used to record user identity information and operational behavior, which facilitates post-event auditing and traceability. At the same time, different access permissions can be set according to user roles.

[0016] In some embodiments, the hazardous materials basic information data acquisition module specifically consists of a sensor assembly unit, a real-time input terminal, and a PFID tag identification;

[0017] The sensor assembly unit specifically consists of a temperature sensor, a humidity sensor, a hazardous gas leak sensor, and a pressure sensor, which can monitor the hazardous chemical storage environment in real time.

[0018] The real-time data entry terminal can be a handheld device or a smartphone with a built-in data entry app, which can perform on-site data entry and real-time feedback.

[0019] In some embodiments, the PFID tags can be divided into passive, active and semi-active tag types, and each group of PFID tags contains a unique identifier, which is used to accurately identify each hazardous chemical container.

[0020] The PFID tag can store various basic information about hazardous chemicals, including the chemical name, hazard classification, storage requirements, production date, and expiration date of the hazardous chemical.

[0021] The RFID tag identification is accomplished by a fixed reader installed in warehouses, production lines, and transportation channels. This fixed reader can identify passing RFID tags, thereby achieving real-time monitoring.

[0022] In some embodiments, the data storage module comprises a database management submodule, a cloud storage service submodule, and a data encryption and access control submodule.

[0023] The database management submodule combines relational and non-relational databases to meet different data storage needs.

[0024] The relational database uses tables to store data, supports strong type definitions, and ensures data consistency. The non-relational database adopts multiple data models such as document, key-value, column family, and graph, and supports semi-structured and unstructured data.

[0025] The cloud storage service submodule is based on a cloud server and uses the cloud server for data backup and disaster recovery. The cloud storage service submodule completes data backup through wireless data to ensure data security and high availability.

[0026] The data encryption and access control submodule uses the AES encryption algorithm, and the same key is used in both encryption and decryption. The specific process is as follows:

[0027] 1) Generate a random encryption key, which is securely transmitted and stored between the encryption and decryption parties. The key can be 128, 192, or 256 bits in length.

[0028] 2) Divide the plaintext data into fixed-size blocks, and encrypt each block using the AES algorithm to generate ciphertext;

[0029] The encryption process consists of multiple rounds, each of which involves various operations, including byte substitution, row shifting, column mixing, and round key addition.

[0030] Decryption uses the same key, reversing the encryption process.

[0031] The specific process of the AES algorithm is as follows:

[0032] Byte replacement uses an S-box replacement table to replace each byte in the state;

[0033] The formula is as follows:

[0034]

[0035] in, The position of the state matrix;

[0036] The shift operation performs a circular left shift on the rows in the state.

[0037] The formula is as follows:

[0038]

[0039] Column blending blends the columns in the state;

[0040] Treat each column as a polynomial and multiply it by a fixed polynomial, using the following formula:

[0041]

[0042] Its design includes operations on finite weight polynomials;

[0043] The current state of the wheel key is XORed with the wheel key;

[0044] The formula is as follows:

[0045] .

[0046] In some embodiments, the data cleaning tool built into the data analysis module is OpenRefine, which can perform deduplication, noise reduction, and format conversion on the raw data.

[0047] In some embodiments, the data analysis module establishes a predictive model for basic information data of hazardous chemicals through machine learning. The machine learning uses a neural network algorithm, and the specific process is as follows:

[0048] For the data processed by the pre-processing data cleaning tools, feature engineering is performed to select features that are relevant to the prediction target;

[0049] The basic structure of a neural network includes an input layer, hidden layers, and an output layer. The output of each layer in a neural network is represented as follows:

[0050]

[0051] in, For the first The output of the layer, For the first The weight matrix of the layer, For the first Layer bias, For activation functions;

[0052] The output of the last layer is used to calculate the loss:

[0053]

[0054] in, This is the actual value. It is a predicted value;

[0055] The weights and biases are updated using the backpropagation algorithm to minimize the loss function, and the updated formula is as follows:

[0056]

[0057] in The learning rate;

[0058] Cross-validation is used to evaluate the model's performance.

[0059] In some embodiments, the rules of the early warning mechanism module are defined as follows:

[0060] Temperature detection: When the temperature around and inside the hazardous chemical container exceeds the set value, an alarm is triggered. The threshold is controlled at 70 degrees Celsius.

[0061] Gas concentration detection triggers an alarm when the carbon dioxide concentration in the environment surrounding the hazardous chemical container exceeds the safe range, wherein the carbon dioxide concentration threshold is 1000 ppm;

[0062] Humidity detection triggers an alarm when the humidity of the hazardous chemical storage environment is higher or lower than the set value, with the threshold controlled between 30-70%.

[0063] The system uses a logical judgment algorithm to assess whether the current environment meets the warning conditions. When the warning is triggered, it can be activated via wireless network. The warning algorithm formula is as follows:

[0064] Temperature warning: When This will trigger an alert;

[0065] in, The current temperature. The maximum temperature threshold is set.

[0066] Gas concentration warning: When This will trigger an alert;

[0067] in, The current gas concentration, The maximum gas concentration threshold is set.

[0068] Humidity warning: When and This will trigger an alert;

[0069] in, Given the current ambient humidity, , These are the set minimum and maximum concentration thresholds, respectively.

[0070] Another technical problem to be solved by this invention is to propose a basic information data analysis method for hazardous chemicals, comprising the following steps:

[0071] Step 1: Through the sensor assembly unit built into the hazardous materials basic information data acquisition module, the storage environment of hazardous chemicals is monitored in real time, including environmental information such as ambient temperature, humidity, gas leakage and container pressure.

[0072] Step 2: Staff can use handheld instant data entry terminals to manually record basic information about hazardous chemicals, including the properties of the stored substances and their storage conditions. The data is entered through a built-in APP.

[0073] Step 3: Label each group of hazardous chemical containers with a PFID label, ensuring that the label includes the chemical name, hazard classification, storage requirements, production date, and expiration date;

[0074] Step 4: Transmit the collected basic information of hazardous materials to the data storage module, and use the cloud storage unit to classify and store the data. To ensure data security, the data is encrypted through the data encryption and access control submodule based on the AES encryption algorithm.

[0075] Step 5: Use OpenRefine as the data cleaning tool to perform deduplication, noise reduction, and format conversion on the raw data, and check the consistency and integrity of the data to ensure that there are no missing values ​​or outliers;

[0076] Step Six: Perform statistical analysis on the processed data, generate a data feature report using statistical analysis tools to help users understand the data status, and then establish a predictive model for basic information data of hazardous chemicals based on neural network algorithms.

[0077] Step 7: Define early warning rules, monitor environmental data in real time, and issue an early warning immediately once an anomaly is detected;

[0078] Step 8: Utilize the visualization module to display the results generated by the data analysis module in the form of charts, ensuring that staff can more intuitively understand the management status of hazardous chemicals;

[0079] Step 9: Through the user identity information management module, set different access permissions according to user roles, and record the identity information and operation behavior of all users for easy auditing and traceability.

[0080] Step 10: Regularly use the cloud storage service submodule to back up your data to ensure data security and high availability.

[0081] Compared with existing technologies, the present invention provides a basic information data analysis system and method for hazardous chemicals, which has the following beneficial effects:

[0082] This is a basic information data analysis system and method for hazardous chemicals. Through a sensor assembly, it monitors the storage environment of hazardous chemicals in real time. The system can promptly detect potential risks, such as abnormal temperature, humidity and gas concentration, and issue early warnings to reduce the occurrence of accidents. At the same time, it is equipped with an early warning mechanism module, which realizes intelligent monitoring and early warning of environmental parameters. Staff can customize early warning rules according to their own needs to ensure timely response when environmental parameters are abnormal and ensure safety.

[0083] By employing data cleaning and preprocessing, the system ensures the accuracy and completeness of basic information on hazardous materials, avoiding decision-making errors caused by data inaccuracies. The classification and cloud storage technologies for information data make data management more efficient, facilitating retrieval, analysis, and use, and reducing errors and time waste caused by manual operations. At the same time, the system, based on machine learning-based predictive models, can intelligently analyze the status of hazardous chemicals, providing enterprises with scientific decision support and optimizing resource allocation and hazardous chemical management processes. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of the basic information data analysis system for hazardous chemicals of the present invention. Detailed Implementation

[0085] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0087] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0088] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0089] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0090] Please see Figure 1 In this implementation plan: a basic information data analysis system for hazardous chemicals, comprising:

[0091] The hazardous materials basic information data acquisition module is used to collect relevant basic information about hazardous materials in real time. The basic information includes material properties, storage conditions, real-time monitoring data and historical data.

[0092] The hazardous materials basic information data acquisition module consists of a sensor assembly unit, a real-time data entry terminal, and PFID tag identification.

[0093] The sensor assembly unit is specifically composed of a temperature sensor, a humidity sensor, a hazardous gas leak sensor, and a pressure sensor, which can monitor the hazardous chemical storage environment in real time.

[0094] The real-time data entry terminal can be a handheld device or smartphone with a built-in data entry APP, which can perform on-site data entry and real-time feedback;

[0095] PFID tags can be divided into passive, active and semi-active tag types. Each set of PFID tags contains a unique identifier, which is used to accurately identify each hazardous chemical container.

[0096] PFID tags can store various basic information about hazardous chemicals, including the chemical name, hazard classification, storage requirements, production date, and expiration date.

[0097] RFID tag identification is accomplished through fixed readers, which are installed in warehouses, production lines, and transportation channels. These readers can identify passing RFID tags, thereby enabling real-time monitoring.

[0098] The data storage module has a built-in cloud storage unit, which can classify and store the collected data to ensure data security and accessibility.

[0099] The data storage module consists of a database management submodule, a cloud storage service submodule, and a data encryption and access control submodule.

[0100] The database management submodule combines relational and non-relational databases to meet different data storage needs.

[0101] Relational databases use tables to store data, support strong type definitions, and ensure data consistency. Non-relational databases use multiple data models such as documents, key-value pairs, column families, and graphs, and support semi-structured and unstructured data.

[0102] The cloud storage service submodule is based on cloud server settings. It uses cloud servers for data backup and disaster recovery. The cloud storage service submodule completes data backup through wireless data to ensure data security and high availability.

[0103] The data encryption and access control submodule uses the AES encryption algorithm, employing the same key for both encryption and decryption. The specific process is as follows:

[0104] 1) Generate a random encryption key, which is securely transmitted and stored between the encryption and decryption parties. The key can be 128, 192, or 256 bits in length.

[0105] 2) Divide the plaintext data into fixed-size blocks, and encrypt each block using the AES algorithm to generate ciphertext;

[0106] The encryption process consists of multiple rounds, each of which involves various operations, including byte substitution, row shifting, column mixing, and round key addition.

[0107] Decryption uses the same key, reversing the encryption process.

[0108] The specific process of the AES algorithm is as follows:

[0109] Byte replacement uses an S-box replacement table to replace each byte in the state;

[0110] The formula is as follows:

[0111]

[0112] in, The position of the state matrix;

[0113] The shift operation performs a circular left shift on the rows in the state.

[0114] The formula is as follows:

[0115]

[0116] Column blending blends the columns in the state;

[0117] Treat each column as a polynomial and multiply it by a fixed polynomial, using the following formula:

[0118]

[0119] Its design includes operations on finite weight polynomials;

[0120] The current state of the wheel key is XORed with the wheel key;

[0121] The formula is as follows:

[0122] ;

[0123] The data analysis module can process and analyze stored data based on big data. The data analysis module has built-in statistical analysis tools to provide basic statistical analysis functions and help users understand data characteristics.

[0124] The data cleaning tool built into the data analysis module uses OpenRefine, which can perform deduplication, noise reduction, and format conversion on the raw data.

[0125] The data analysis module uses machine learning to build predictive models for basic information data on hazardous chemicals. The machine learning uses neural network algorithms, and the specific process is as follows:

[0126] For the data processed by the pre-processing data cleaning tools, feature engineering is performed to select features that are relevant to the prediction target;

[0127] The basic structure of a neural network includes an input layer, hidden layers, and an output layer. The output of each layer in a neural network is represented as follows:

[0128]

[0129] in, For the first The output of the layer, For the first The weight matrix of the layer, For the first Layer bias, For activation functions;

[0130] The output of the last layer is used to calculate the loss:

[0131]

[0132] in, This is the actual value. It is a predicted value;

[0133] The weights and biases are updated using the backpropagation algorithm to minimize the loss function, and the updated formula is as follows:

[0134]

[0135] in The learning rate;

[0136] Cross-validation is used to evaluate the model's performance.

[0137] The early warning mechanism module has built-in early warning rules and alarm thresholds. When an abnormal situation occurs, it will issue an early warning in a timely manner and provide corresponding handling suggestions.

[0138] The rules for the early warning mechanism module are defined as follows:

[0139] Temperature detection: When the temperature around and inside the hazardous chemical container exceeds the set value, an alarm is triggered. The threshold is controlled at 70 degrees Celsius.

[0140] Gas concentration detection: When the carbon dioxide concentration in the environment surrounding the hazardous chemical container exceeds the safe range, an early warning is triggered. The carbon dioxide concentration threshold is 1000 ppm.

[0141] Humidity detection triggers an alarm when the humidity of the hazardous chemical storage environment is higher or lower than the set value, with the threshold controlled between 30-70%.

[0142] The system uses a logical judgment algorithm to assess whether the current environment meets the warning conditions. When the warning is triggered, it can be activated via wireless network. The warning algorithm formula is as follows:

[0143] Temperature warning: When This will trigger an alert;

[0144] in, The current temperature. The maximum temperature threshold is set.

[0145] Gas concentration warning: When This will trigger an alert;

[0146] in, The current gas concentration, The maximum gas concentration threshold is set.

[0147] Humidity warning: When and This will trigger an alert;

[0148] in, Given the current ambient humidity, , These are the set minimum and maximum concentration thresholds, respectively;

[0149] The visualization module will visualize the analysis results generated by the data analysis module through charts, allowing staff to intuitively understand the management status of hazardous chemicals.

[0150] The user identity information management module is used to record user identity information and operational behavior, which facilitates post-event auditing and traceability. At the same time, different access permissions can be set according to user roles.

[0151] Based on the aforementioned basic information data analysis system for hazardous chemicals, a method for analyzing basic information data for hazardous chemicals is proposed, comprising the following steps:

[0152] Step 1: Through the sensor assembly unit built into the hazardous materials basic information data acquisition module, the storage environment of hazardous chemicals is monitored in real time, including environmental information such as ambient temperature, humidity, gas leakage and container pressure.

[0153] Step 2: Staff can use handheld instant data entry terminals to manually record basic information about hazardous chemicals, including the properties of the stored substances and their storage conditions. The data is entered through a built-in APP.

[0154] Step 3: Label each group of hazardous chemical containers with a PFID label, ensuring that the label includes the chemical name, hazard classification, storage requirements, production date, and expiration date;

[0155] Step 4: Transmit the collected basic information of hazardous materials to the data storage module, and use the cloud storage unit to classify and store the data. To ensure data security, the data is encrypted through the data encryption and access control submodule based on the AES encryption algorithm.

[0156] Step 5: Use OpenRefine as the data cleaning tool to perform deduplication, noise reduction, and format conversion on the raw data, and check the consistency and integrity of the data to ensure that there are no missing values ​​or outliers;

[0157] Step Six: Perform statistical analysis on the processed data, generate a data feature report using statistical analysis tools to help users understand the data status, and then establish a predictive model for basic information data of hazardous chemicals based on neural network algorithms.

[0158] Step 7: Define early warning rules, monitor environmental data in real time, and issue an early warning immediately once an anomaly is detected;

[0159] Step 8: Utilize the visualization module to display the results generated by the data analysis module in the form of charts, ensuring that staff can more intuitively understand the management status of hazardous chemicals;

[0160] Step 9: Through the user identity information management module, set different access permissions according to user roles, and record the identity information and operation behavior of all users for easy auditing and traceability.

[0161] Step 10: Regularly use the cloud storage service submodule to back up your data to ensure data security and high availability.

[0162] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0163] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A basic information data analysis system for hazardous chemicals, characterized in that, include: The hazardous materials basic information data acquisition module is used to collect relevant basic information about hazardous materials in real time. The basic information includes material properties, storage conditions, real-time monitoring data and historical data. The data storage module has a built-in cloud storage unit, which can classify and store the collected data to ensure data security and accessibility. The data analysis module is capable of processing and analyzing stored data based on big data. The data analysis module has built-in statistical analysis tools to provide basic statistical analysis functions and help users understand data characteristics. The early warning mechanism module has built-in early warning rules and alarm thresholds. When an abnormal situation occurs, it will issue an early warning in a timely manner and provide corresponding handling suggestions. The visualization module visualizes the analysis results generated by the data analysis module through charts, allowing staff to intuitively understand the management status of hazardous chemicals. The user identity information management module is used to record user identity information and operational behavior, which facilitates post-event auditing and traceability. At the same time, different access permissions can be set according to user roles.

2. The hazardous chemicals basic information data analysis system according to claim 1, characterized in that, The hazardous materials basic information data acquisition module is specifically composed of a sensor assembly unit, a real-time input terminal, and a PFID tag identification system. The sensor assembly unit specifically consists of a temperature sensor, a humidity sensor, a hazardous gas leak sensor, and a pressure sensor, which can monitor the hazardous chemical storage environment in real time. The real-time data entry terminal can be a handheld device or a smartphone with a built-in data entry app, which can perform on-site data entry and real-time feedback.

3. The hazardous chemicals basic information data analysis system according to claim 2, characterized in that, The PFID tags can be divided into passive, active and semi-active tag types. Each group of PFID tags contains a unique identifier, which is used to accurately identify each hazardous chemical container. The PFID tag can store various basic information about hazardous chemicals, including the chemical name, hazard classification, storage requirements, production date, and expiration date of the hazardous chemical. The RFID tag identification is accomplished by a fixed reader installed in warehouses, production lines, and transportation channels. This fixed reader can identify passing RFID tags, thereby achieving real-time monitoring.

4. The hazardous chemicals basic information data analysis system according to claim 1, characterized in that, The data storage module consists of a database management submodule, a cloud storage service submodule, and a data encryption and access control submodule. The database management submodule combines relational and non-relational databases to meet different data storage needs. The relational database uses tables to store data, supports strong type definitions, and ensures data consistency. The non-relational database adopts multiple data models such as document, key-value, column family, and graph, and supports semi-structured and unstructured data. The cloud storage service submodule is based on a cloud server and uses the cloud server for data backup and disaster recovery. The cloud storage service submodule completes data backup through wireless data to ensure data security and high availability. The data encryption and access control submodule uses the AES encryption algorithm, and the same key is used in both encryption and decryption. The specific process is as follows: 1) Generate a random encryption key, which is securely transmitted and stored between the encryption and decryption parties. The key can be 128, 192, or 256 bits in length. 2) Divide the plaintext data into fixed-size blocks, and encrypt each block using the AES algorithm to generate ciphertext; The encryption process consists of multiple rounds, each of which involves various operations, including byte substitution, row shifting, column mixing, and round key addition. Decryption uses the same key, reversing the encryption process. The specific process of the AES algorithm is as follows: Byte replacement uses an S-box replacement table to replace each byte in the state; The formula is as follows: in, The position of the state matrix; The shift operation performs a circular left shift on the rows in the state. The formula is as follows: Column blending blends the columns in the state; Treat each column as a polynomial and multiply it by a fixed polynomial, using the following formula: Its design includes operations on finite weight polynomials; The current state of the wheel key is XORed with the wheel key; The formula is as follows: 。 5. The hazardous chemicals basic information data analysis system according to claim 1, characterized in that, The data analysis module uses OpenRefine as its built-in data cleaning tool, which can perform deduplication, noise reduction, and format conversion on the raw data.

6. The hazardous chemicals basic information data analysis system according to claim 1, characterized in that: The data analysis module establishes a predictive model for basic information data of hazardous chemicals through machine learning. The machine learning uses a neural network algorithm, and its specific process is as follows: For the data processed by the pre-processing data cleaning tools, feature engineering is performed to select features that are relevant to the prediction target; The basic structure of a neural network includes an input layer, hidden layers, and an output layer. The output of each layer in a neural network is represented as follows: in, For the first The output of the layer, For the first The weight matrix of the layer, For the first Layer bias, For activation functions; The output of the last layer is used to calculate the loss: in, This is the actual value. It is a predicted value; The weights and biases are updated using the backpropagation algorithm to minimize the loss function, and the updated formula is as follows: in The learning rate; Cross-validation is used to evaluate the model's performance.

7. The hazardous chemicals basic information data analysis system according to claim 1, characterized in that, The rules for the early warning mechanism module are defined as follows: Temperature detection: When the temperature around and inside the hazardous chemical container exceeds the set value, an alarm is triggered. The threshold is controlled at 70 degrees Celsius. Gas concentration detection triggers an alarm when the carbon dioxide concentration in the environment surrounding the hazardous chemical container exceeds the safe range, wherein the carbon dioxide concentration threshold is 1000 ppm; Humidity detection triggers an alarm when the humidity of the hazardous chemical storage environment is higher or lower than the set value, with the threshold controlled between 30-70%. The system uses a logical judgment algorithm to assess whether the current environment meets the warning conditions. When the warning is triggered, it can be activated via wireless network. The warning algorithm formula is as follows: Temperature warning: When This will trigger an alert; in, The current temperature. The maximum temperature threshold is set. Gas concentration warning: When This will trigger an alert; in, The current gas concentration, The maximum gas concentration threshold is set. Humidity warning: When and This will trigger an alert; in, Given the current ambient humidity, , These are the set minimum and maximum concentration thresholds, respectively.

8. A method for analyzing basic informational data of hazardous chemicals, characterized in that, Includes the following steps: Step 1: Through the sensor assembly unit built into the hazardous materials basic information data acquisition module, the storage environment of hazardous chemicals is monitored in real time, including environmental information such as ambient temperature, humidity, gas leakage and container pressure. Step 2: Staff can use handheld instant data entry terminals to manually record basic information about hazardous chemicals, including the properties of the stored substances and their storage conditions. The data is entered through a built-in APP. Step 3: Label each group of hazardous chemical containers with a PFID label, ensuring that the label includes the chemical name, hazard classification, storage requirements, production date, and expiration date; Step 4: Transmit the collected basic information of hazardous materials to the data storage module, and use the cloud storage unit to classify and store the data. To ensure data security, the data is encrypted through the data encryption and access control submodule based on the AES encryption algorithm. Step 5: Use OpenRefine as the data cleaning tool to perform deduplication, noise reduction, and format conversion on the raw data, and check the consistency and integrity of the data to ensure that there are no missing values ​​or outliers; Step Six: Perform statistical analysis on the processed data, generate a data feature report using statistical analysis tools to help users understand the data status, and then establish a predictive model for basic information data of hazardous chemicals based on neural network algorithms. Step 7: Define early warning rules, monitor environmental data in real time, and issue an early warning immediately once an anomaly is detected; Step 8: Utilize the visualization module to display the results generated by the data analysis module in the form of charts, ensuring that staff can more intuitively understand the management status of hazardous chemicals; Step 9: Through the user identity information management module, set different access permissions according to user roles, and record the identity information and operation behavior of all users for easy auditing and traceability. Step 10: Regularly use the cloud storage service submodule to back up your data to ensure data security and high availability.

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