Dust explosion monitoring and early warning system

Abstract

The invention relates to the field of dust explosion-related early warning in enterprise production, and provides a dust explosion-related enterprise safety production risk monitoring early warning data access specification (trial operation) for building an enterprise terminal and a supervision terminal according to the regulation of the national emergency department. The dust explosion-related monitoring and early warning system is composed of a data acquisition module, a data transmission module, a data storage module, a data processing module, an early warning module and an online spot check module, solves the problems of standardization, unification, accuracy and timely disposal in dust explosion-proof safety management, and has the advantages of being high in operability, high in standardization procedure, high in reliability and the like. And the functions are comprehensive.

Description

Technical Field

[0001] The invention relates to the field of dust explosion warning in enterprise production, and in particular to a dust explosion monitoring and early warning system. Background Art

[0002] There are a large number of dust-related explosion enterprises and they are widely distributed. Metallurgy, nonferrous metals, building materials, machinery, light industry, textiles, tobacco, commerce and other industrial and trade sectors all have the production and processing of combustible dust. The safety risks in these links are still relatively high. Despite relevant laws and regulations and standards, some enterprises still have some problems. For example, some employees are not properly trained and do not have a firm grasp of job operating procedures; their self-protection awareness is weak, and they do not wear labor protection equipment correctly during the production process and do not clean thoroughly. Some enterprises may have defects in the design, construction and daily management of dust explosion-proof safety facilities, such as failure to set up safety equipment as required, dust removal systems that do not meet standards, and dust cleaning that is not timely. Once a dust explosion accident occurs, it often causes large casualties and property losses.

[0003] Patent No. CN202311358154.3 of the State Intellectual Property Office discloses a "dust explosion risk warning system based on the full process monitoring of industrial equipment operation". Through the construction module of the explosion-related dust database, the basic information of the industrial workshop's production-derived dust is collected, and the explosion-related dust database is built. The basic information of the industrial workshop's production-derived dust is collected, and the explosion-related dust database is built. The multi-level operation area based on the industrial workshop is delineated, which solves the technical problems of low accuracy of dust explosion risk assessment and inadequate management efficiency in the existing technology. However, the operability of the system needs to be improved, and the functional modules are not comprehensive, reasonable, and standardized. Summary of the invention

[0004] The purpose of the present invention is to provide a unified and standardized dust explosion monitoring and early warning system in accordance with the provisions of the "Dust Explosion Enterprise Production Safety Risk Monitoring and Early Warning Data Access Specifications (Trial)" of the Ministry of Emergency Management of the People's Republic of China, to strengthen the monitoring and early warning of production safety risks in dust explosion enterprises, and to solve the various problems of the existing dust explosion enterprises' dust removal system safety equipment and facilities and dust cleaning and other key links' production safety risks, such as insufficient real-time monitoring, dynamic early warning, intelligent analysis and timely disposal.

[0005] The invention objective of the present invention is achieved in this way: the system comprises a data acquisition module, a data transmission module, a data storage module, a data processing module, an early warning module and an online spot check module.

[0006] The data acquisition module includes the collection of enterprise information, sensors, basic equipment information data, and video data, and obtains data on the environment, equipment, and sensors through manual and automated methods.

[0007] The data transmission module realizes the data transmission between the acquisition device and the server through protocols such as MQTT, HTTP, and WebSocket;

[0008] The data storage module is used to store the data transmitted by the data transmission module and its derivative data, supports structured data storage, and adopts MySQL and Redis database technologies.

[0009] The data processing module cleans, converts the format, statistically analyzes the data transmitted by the data transmission module to the server, generates business data, and provides further data analysis support.

[0010] The early warning module obtains the comprehensive risk early warning level index R based on R = YR × TR, and compares it with the threshold values: 3 < R ≤ 7 is a major risk, 2 < R ≤ 3 is a relatively large risk, 1 < R ≤ 2 is a general risk, 0 ≤ R ≤ 1 is a low risk, generates the corresponding early warning level, and issues early warning information. The system has an enterprise terminal and a supervision terminal. The early warning information is pushed to the enterprise terminal and the supervision terminal via text messages or applications according to the early warning level, and stores the received data, calculation results, and early warning information data;

[0011] The online spot check module is used for spot checks, obtains real-time data, and generates inspection records to ensure the authenticity and effectiveness of the data.

[0012] The tracking and supervision module is used for the tracking and management of tasks and events. It mainly conducts task assignment, progress tracking, and management of implementation status based on the early warning level to ensure that all work is completed on time.

[0013] The data acquisition module includes the acquisition of enterprise basic information, dust-related information, dust removal system information, cleaning check-in information, dynamic perception data information, video information, early warning push information, etc.;

[0014] The enterprise basic information includes, but is not limited to, the following information related to the enterprise: enterprise name, unified social credit code, business status, production and operation mode, administrative slave relationship, national economic type, business address, administrative region code, legal representative, legal representative's phone number, person in charge of work safety management, person in charge of work safety management's phone number, main business;

[0015] The dust-related information includes, but is not limited to, the following information related to dust: dust industry type, dust-related high-risk processes, main dust types, daily dust production, number of dust workers, related explosion prevention measures;

[0016] The dust removal system information includes but is not limited to the following information related to the dust removal system: equipment code, equipment name, workshop, equipment type, equipment manufacturer, installation date, installation area, and dust removal type; the dynamic sensing data information is the analog signal of the sensor and the switch information of the sensor, including but not limited to one or more of the following sensor parameters: dust removal system start and stop signal, dust collector inlet and outlet pressure difference, air lock and ash unloading fault signal, dust collector ash hopper temperature, explosion prevention and control measures failure signal, dust collector fire extinguishing device effectiveness signal, explosion relief device failure signal, explosion isolation valve self-locking feedback signal, filter cleaning air source pressure, water tank water level signal, spray water flow, metal powder storage place hydrogen concentration signal, and filtered water pressure.

[0017] The cleaning punch-in information includes but is not limited to the following information related to cleaning punch-in: cleaning items, completion status, punch-in person, punch-in date;

[0018] The video information is a monitoring video of an explosion-related environment, which can be completed by a video module to support the collection, uploading and playback of video data.

[0019] The warning push information includes but is not limited to the following information related to the warning: warning level, warning start time, warning end time, warning status, warning description, and warning issuing unit;

[0020] Preferably, the system includes a closed-loop disposal module, which processes and corrects the inspection records generated by online spot checks, records the results of the correction process, and realizes closed-loop management;

[0021] The working steps of this system are as follows:

[0022] S1. The data acquisition module collects the following data: enterprise information, sensor data, equipment information basic data, and video data.

[0023] S2. The data transmission module includes a network module, an MQTT protocol, and an MQTT agent; the data acquisition module uses the MQTT protocol as a communication protocol through the network module to send the collected data to the server.

[0024] S3, the data storage module can be one or more of the following: Mysql data storage, Redis data storage, cloud storage, local storage, and the data transmitted by the data transmission module is backed up to the data storage module;

[0025] S4. The data processing module is installed at the enterprise end and the regulatory end. After receiving the data, the data processing module cleans, converts, counts and analyzes the data to generate analytical data; the data in the data processing module is presented at the enterprise end and the regulatory end.

[0026] S5. The analyzed data enters the early warning module. The early warning module calculates the comprehensive risk early warning level index R based on R = YR × TR. By comparing with the threshold values, where 3 < R ≤ 7 represents major risks, 2 < R ≤ 3 represents relatively large risks, 1 < R ≤ 2 represents general risks, and 0 ≤ R ≤ 1 represents low risks, corresponding early warning signals of different danger levels are generated and early warning messages are sent to the enterprise side and the supervision side.

[0027] The early warning messages can also be input into the tracking and supervision module to form hierarchical reports of different risk levels, facilitating the supervision of the handling of early warning messages.

[0028] S6. The online spot check module: can initiate remote spot checks at any time, obtain enterprise-side information in real time to ensure the authenticity and effectiveness of the data, and generate inspection records; the closed-loop handling module is to handle and rectify the inspection records generated by the online spot check, record the results of the rectification process, and achieve closed-loop management.

[0029] In S4, the data in the data processing module is presented as a simulation diagram, monitoring status, and monitoring values of the dust removal system equipment at the enterprise side and the supervision side. In the simulation diagram of the dust removal system equipment, the equipment for early warning can be located.

[0030] For the data monitoring of the enterprise side, it is collected and updated at most every 5 minutes according to requirements.

[0031] In S5, the risk levels are divided into four levels: major risks, relatively large risks, general risks, and low risks. When there is one or more early warnings, the highest risk level and early warning value are taken.

[0032] The supervision side can conduct online spot checks on the supervised units and register them. The spot check registration includes spot check risk points, spot check pictures, and spot check status.

[0033] Advantages of the present invention

[0034] Compared with the prior art, the advantages of the present invention are as follows:

[0035] 1. Through the system construction of the enterprise side and the supervision side, it promotes the standardization and unification of dust explosion prevention and safety governance, transforms towards pre-event prevention, and urges enterprises to actively implement the main responsibility for safety risk prevention and control.

[0036] 2. Through the closed-loop design of the data acquisition module, data transmission module, data storage module, data processing module, early warning module, video module, tracking and supervision module, online spot check module, and closed-loop handling module, it strengthens the safety production risk monitoring and early warning of dust-related explosion enterprises, and solves the problems of insufficient real-time monitoring, dynamic early warning, intelligent judgment, and timely handling of safety production risks in key links such as the safety equipment and facilities of the dust removal system and dust cleaning of existing dust-related explosion enterprises.

[0037] The risk warning level index R in this system is calculated as follows: R = YR × TR

[0038] YR is the inherent risk correction factor, and its determination method is as follows: YR is determined by the inherent safety of the dust removal system and its related processes, the configuration of explosion protection measures and their effectiveness; the indicator assignment method is used to assign values ​​to various indicators, and the weights are adjusted according to the degree of influence of the indicators on the possibility and consequences of accidents, and the inherent risk warning value G is calculated. When the process changes, the inherent risk warning value G is recalculated; the inherent risk warning value is normalized and converted into the inherent risk correction factor YR.

[0039] The inherent risk warning value G is calculated according to the following formula: The evaluation dimensions of the inherent risk warning value G are tentatively set at 6 items, and the values ​​are assigned according to the following table:

[0040] Table 1 Inherent risk warning value related indicators and assignments

[0041]

[0042]

[0043]

[0044]

[0045] According to the process conditions, refer to the above table, multiply the "level" and "coefficient" of each dimension and sum them up. As listed in the following formula: G, = level × coefficient. The score of the inherent risk warning value G is normalized by the "linear proportional conversion algorithm" and converted into the corresponding inherent risk correction coefficient YR∈[0.6,1.4]. The rules are as follows:

[0046] Table 2 Inherent risk warning value and correction factor rules

[0047]

[0048]

[0049] Note: While the model framework remains unchanged, the level and coefficient can be adjusted according to the actual process risk during the verification process.

[0050] TR is the dynamic risk warning index, and its determination method is: the dynamic risk warning index TR is determined by the operating status of the dust removal system and its related processes, mainly involving the alarm triggering and elimination of monitoring parameters, and the management level of the enterprise. TR is normalized by the dynamic risk warning value Ta, and Ta is calculated according to the following formula: Ta = Talarm + Tg; where: Ta is the dynamic risk warning value; Talarm is the warning value of the main monitoring parameters of the dust removal system; Tg is the enterprise on-site management warning value.

[0051] (I) Determination of monitoring parameter warning value T alarm Monitoring parameter warning value Talarm, take the higher score of monitoring parameter operation risk warning value T; and high risk parameter over limit warning value Tm; Talarm = max{T;, Tm} where: T; is the operation risk warning value; Tm is the high risk parameter over limit warning value. Monitoring parameter operation risk warning value T; is composed of "key parameter T;" and "non-key parameter T 2 j" is obtained by weighted summation of the warning values. For key parameters, the weight is higher and the weight coefficient is set to α□; for non-key parameters, the weight is lower and the weight coefficient is set to α□. The calculation time span is days and the calculation time segment is 5 minutes. It can be summarized as the following formula: Where: T 1 j is the key parameter of the operation risk warning value; T 2 j is a non-critical parameter of the operation risk warning value; α1, α□ are the weight coefficients of the secondary indicators, a 1 =1.2, α□=0When programming, this can be designed as a variable, and the value can be adjusted later according to the data verification situation. The distinction between key parameters and non-key parameters is based on the following table:

[0052] Table 3 Distinguishing between key parameters and non-key parameters

[0053]

[0054]

[0055]

[0056] Note: The "effectiveness of the spark detection and extinguishing system" in Table 3 is alarmed by the following judgment logic based on the three monitoring indicators: the spark extinguishing device action trigger signal, the spark extinguishing device spray pipeline flow signal, and the spark detection system operation signal:

[0057] a) When the dust collector is turned on and the "spark detection system operation signal" is in the off state, the "spark detection and extinguishing system effectiveness" will generate an alarm; b) When the dust collector is turned on and the "spark extinguishing device action trigger signal" is generated, and the "spark extinguishing device spray pipeline flow signal" indicates that there is no flow within 20 seconds, the "spark detection and extinguishing system effectiveness" will generate an alarm. Risk warning value T 1 ; and T 2 ,; Use the integral method to temporarily evaluate from 6 dimensions. When corresponding situations occur, add points according to the rules in the following table, add up the scores of each dimension, set the maximum value to 100 points, and continue until the score is full.

[0058] Table 4 Risk warning value dimension evaluation and scoring rules

[0059]

[0060]

[0061]

[0062] The high-risk parameter over-limit warning value Tm is temporarily evaluated from two dimensions using the integral method. When the corresponding situation occurs, points are added according to the rules in the table below. The scores of each dimension are added together, and the maximum value is set to 200 points until it is full. The calculation time span is days, and the calculation time segment is 10 minutes. Formula: Where: Tx is the high-risk parameter over-limit risk warning value of each dimension.

[0063] Table 5 Dimensional evaluation and scoring rules for high-risk parameter over-limit warning values

[0064]

[0065]

[0066] Note: The standard value of the pressure difference between the dust collector inlet and outlet is related to the design of the dust collector and is not a fixed value. The wind pressure difference of the enterprise equipment is adjusted within a fixed range. The range can be adjusted after the enterprise sets it. If it exceeds the range, it will be considered an alarm. It can also be designed as a pressure difference deviation. Each enterprise needs to adjust it after setting it, and it can only be carried out after application confirmation.

[0067] Enterprise on-site management warning value T 9 Determination of:

[0068] Using the indicator assignment method, we extract the management status indicators, assign values ​​to each indicator, and calculate the enterprise site management status index: Where Ti is the warning value of each dimension of enterprise site management. The enterprise site management warning value adopts the integral method, which is temporarily composed of evaluation indicators of two dimensions. The calculation time span is days, the calculation time segment is 10 minutes, and the points of each dimension are added up. The full score is 30 points, and the accumulation is full.

[0069] Table 6 Enterprise on-site management warning value dimension evaluation and scoring rules

[0070]

[0071] Normalize Ta to TR: Normalize the dynamic risk warning value Ta of the dust removal system, and divide it into five risk levels from high to low, namely severe, relatively high, high, medium and low, and convert it into the corresponding risk warning index Tp.

[0072] Table 7 Conversion relationship between dynamic risk value and early warning index of dust removal system

[0073]

[0074] Calculation of risk warning value R: R = YR × TR where: R is the risk warning value of the dust removal system, R∈[0,7]; YR is the inherent risk correction factor, YR∈[0.6,1.4]; TR is the risk warning index, TR∈[0,5].

[0075] Table 8 Correspondence between risk warning values ​​and risk levels

[0076]

[0077] Specific embodiments

[0078] A dust explosion monitoring and early warning system of the present invention includes a data acquisition module, a data transmission module, a data storage module, a data processing module, an early warning module, a video module, an online spot check module, a tracking and supervision module and a closed-loop disposal module, and is provided with an enterprise end and a supervision end. The enterprise end includes a data acquisition module, a data transmission module, a data storage module, a data processing module, a video module, an early warning module, a tracking and supervision module, and a closed-loop disposal module; the supervision end includes a data transmission module, a data storage module, a data processing module, a video module, an early warning module, a tracking and supervision module, an online spot check module, and a closed-loop disposal module. The enterprise end is responsible for the security management work within the enterprise, and the supervision end is responsible for the enterprise security supervision work within its respective jurisdiction; when logging in to the enterprise end and the supervision end, the user needs to insert an encryption lock and enter a PIN code or combine the public key infrastructure technology to perform user identity authentication through a hardware encryption module.

[0079] Taking a company with a dry centralized dust removal system as an example, an embodiment of the present invention is described in detail:

[0080] S1, the data acquisition module collects data;

[0081] The data acquisition module includes sensors, video acquisition devices and data entry.

[0082] Method for obtaining sensor parameters: The dust collector is connected to the workbench in the powder-related place through the air inlet pipe; a spark alarm is set on the air inlet pipe to facilitate real-time monitoring and response to possible sparks; wind pressure monitoring devices are set at the inlet and outlet of the dust collector, and when the wind pressure change is greater than the allowable value, an audible and visual alarm signal is issued; an explosion-proof valve is set at the air inlet of the dust collector to ensure safe and stable operation of the system; there is an ash hopper under the dust collector, and the ash hopper is provided with a dust collector ash hopper temperature monitoring sensor, and an air-locking ash unloading device is set at the bottom of the ash hopper. The ash unloading is achieved by the rotor rolling blades or flapping actions, and at the same time a good sealing effect is achieved to avoid air leakage. The design of the ash unloading working cycle must ensure that there is no dust accumulation in the ash hopper, and a monitoring device for abnormal operation and fault shutdown of the ash unloading device is set. When an abnormality occurs, an audible and visual alarm signal can be issued. The dust collector is connected to the dust collection bin through a dust transmission device; a start-stop signal sensor is installed near the fan on the side of the air outlet duct connected to the dust collector;

[0083] The signals collected above include the start and stop signals of the dust removal system, the pressure difference between the inlet and outlet of the dust collector, the air lock and ash unloading fault signal, and the dust collector ash hopper temperature. The above parameters are transmitted to the data acquisition terminal.

[0084] Video acquisition refers to the monitoring of video surveillance images of dust collectors, dust collection bins, dust-producing workshops and other parts, which is completed by the video module.

[0085] Data entry includes basic enterprise information, dust-related information, dust removal system information, etc.; basic enterprise information includes enterprise name, unified social credit code, business status, industry type, production and operation mode, administrative affiliation, national economic type, business address, administrative region code, legal representative, legal representative telephone number, person in charge of production safety management, telephone number of person in charge of production safety management, main business, whether it is above scale, longitude, latitude, data access identification, enterprise shutdown status, effective mark, etc.; dust-related information includes the enterprise's high-risk dust processes, main dust types, dust industry types, relevant explosion control measures, number of dust workers, Daily dust production, etc. Dust removal system information includes equipment code, equipment name, data access identification, region, equipment type, equipment manufacturer, installation date, relevant explosion control measures, installation environment, dust removal type, the highest number of powder-related positions in a single shift of this dust removal system, the highest number of powder-related workers in a single shift of this dust removal system, equipment-related high-risk processes, daily dust production of this dust removal system, system shutdown status, effective mark, dust removal system schematic, supporting accessories, etc. Dust cleaning punch-in records include daily cleaning records of dust operation areas, regular cleaning records of dust bins, air ducts, inside equipment and facilities, and high-altitude dead corners of factory buildings, to achieve online dust cleaning punch-in, etc.

[0086] S2, the data transmission module includes a network module, an MQTT protocol, and an MQTT agent; the data acquisition module uses the MQTT protocol as a communication protocol through the network module, and the data of the data acquisition module is sent to the server through the data transmission module;

[0087] S3. Data storage module: After the data is transferred to the server, the data storage module classifies and stores it, supporting structured data storage. The data storage module includes Mysql data storage, Redis data storage, cloud storage and local storage; before and during data transmission, the data is backed up to the data storage module. Mysql data storage: MySQL's data storage mechanism depends on its storage engine. MySQL supports multiple storage engines, mainly InnoDB and MyISAM. InnoDB is used here for illustration. Data is stored in tablespaces, which can be files or file sets. Tablespaces are divided into independent tablespaces and shared tablespaces. Independent tablespaces are used to store user data, while shared tablespaces serve metadata; pages are used as the basic unit of memory-disk interaction, and each page usually contains a fixed number of bytes (such as 16KB). Multiple pages form a tablespace for storing data and indexes; a B+ tree structure is used to organize data and indexes to improve query performance. The data of the primary key index (clustered index) is directly stored in the leaf node, while the non-primary key index (secondary index) stores the index column and primary key value, and needs to be queried back to the table to obtain complete data; there is a memory area called a buffer pool for caching data and indexes. When a query needs to access data, InnoDB will first check whether the data is already in the buffer pool to reduce disk I / O operations; use redo logs (Redo Log) and undo logs (Undo Log) to ensure data integrity and consistency. The redo log records all changes to the data so that the data can be restored in the event of a system crash; the undo log is used to support the undo operation of transactions; Redis data storage: Redis is a memory-based key-value storage system that mainly uses a variety of data structures, such as strings, hash tables, lists, sets, and ordered sets (Sorted Sets); in order to make the reading and writing speeds faster, all data will be stored in Redis memory, among which strings are the most commonly used data type in the project and can be used to cache key-value pairs. When data is frequently read and updated less frequently, caching data in Redis can significantly improve the reading speed and system performance. Secondly, lists can implement blocking queues for message passing in the producer-consumer model. Deduplication scenarios are mostly used for collections. Finally, hash tables can be used to store key-value pairs of configuration files to quickly read and update configuration parameters. Cloud storage: Cloud storage can store and manage large-scale unstructured data, such as pictures, videos, log files, etc., to provide support for big data analysis. It provides reliable data backup and recovery services to ensure the security and integrity of user data.By regularly backing up data to the cloud, data can be quickly restored when it is lost or damaged. Cloud storage can be used for data mining, analysis, and prediction to support their business decisions. Data can be accessed from anywhere via the Internet as long as there is a network connection. This is very useful for companies that need to access data across regions, and can improve the flexibility and convenience of data access.

[0088] S4. The data processing module is installed at the enterprise end and the regulatory end. After receiving the data, the data processing module processes the real-time value and forms analysis data through edge computing based on the preset threshold and the comprehensive risk warning level index R calculation formula;

[0089] The enterprise side presents the simulation diagram, monitoring status, and monitoring values ​​of the dust removal system equipment. The mandatory parameters for the detection of dry centralized dust removal systems are the dust removal system start and stop signal, the pressure difference between the dust collector inlet and outlet, the air lock and ash unloading fault signal, and the dust collector ash hopper temperature. The optional parameters are the explosion prevention and control measures failure signal, the dust collector fire extinguishing device effectiveness signal, the explosion relief device failure signal, the self-locking feedback signal of the self-explosion valve, and the filter cleaning air source pressure. The monitoring values ​​are collected and updated every 5 minutes according to the relevant requirements of national regulations. The frequency of data collection can be shortened according to needs. When an early warning is generated, the equipment part indicating the early warning in the simulation diagram of the dust removal system equipment will flash on the screen.

[0090] Provides query of historical monitoring data of monitoring indicators and displays the query interval in the form of sensor curves for 7 days. Uses IoT monitoring methods such as sensors to monitor videos in wireless, wired, and LoRa transmission modes for 24-hour online monitoring.

[0091] S5. The analysis data processed by the data processing module is input into the early warning module, and the comprehensive risk early warning level index R is generated through calculation. By comparing the threshold, early warning signals corresponding to different danger levels are generated, and alarm information is sent to the enterprise end and the regulatory end through SMS or other applications; and the alarm is input into the alarm to directly alarm the working environment.

[0092] If an enterprise has multiple dust removal systems, calculate their risk levels separately, and characterize the risk assessment results of the enterprise based on the highest risk level and warning value in the enterprise's dust removal systems.

[0093] The early warning information is set as four-level early warning signals of red, orange, yellow and blue, corresponding to major risks, relatively large risks, general risks and low risks, and the corresponding early warning push levels are: provincial and ministerial levels, municipal levels, county levels, enterprise levels and other different levels. The provincial and ministerial levels, municipal levels and county levels are set to the government supervision end, and the enterprise level is set to the enterprise end. According to the different early warning signals, the enterprise end will report to the highest supervision level step by step. The blue risk warning is pushed to the enterprise end and handled by the enterprise. The yellow is pushed step by step to the county-level supervision end, the orange is pushed step by step to the municipal-level supervision end, and the red warning is pushed to the provincial and ministerial supervision end. The early warning will send the collected early warning data at a frequency of no more than 5 minutes / time. After the enterprise has processed the early warning information, it will promptly eliminate the early warning through clocking in and normal equipment sensor parameters, and report the elimination of the early warning through the tracking and supervision module.

[0094] S6. The supervision end can also establish a file for each enterprise. The online spot check module can initiate remote spot checks at any time, obtain enterprise information in real time, ensure the authenticity and validity of the data, and generate inspection records. After receiving the inspection records, the enterprise will make rectifications to ensure that all problems are thoroughly resolved, and the complete management process is realized through the closed-loop disposal module.

[0095] The access enterprises can be searched in multiple dimensions, supporting the regulatory authorities to carry out targeted spot checks, realizing the "one-screen aggregation" of the information of the inspected enterprises, making it easier to understand the overall situation of the enterprises. The aggregated display content includes the access equipment status, alarm and disposal status, early warning and follow-up supervision status, dust cleaning and clocking status, dust removal equipment operation status, video monitoring status, etc. of the inspected enterprises.

Claims

1. A dust explosion monitoring and early warning system, including a data acquisition module, a data transmission module, a data storage module, a data processing module, an early warning module, and an online spot check module; The data acquisition module includes the collection of enterprise information, sensor dynamic perception data, equipment information basic data, and video data, and obtains data on the environment, equipment, and sensors through manual and automated methods; The data transmission module realizes data transmission between the acquisition device and the server through protocols such as MQTT, HTTP, and WebSocket; The data storage module is used to store data transmitted by the data transmission module and its derivative data, supports structured data storage, and adopts MySQL and Redis database technology; The data processing module cleans, converts the format, counts and analyzes the data transmitted to the server by the data transmission module, and generates business data to provide further data analysis support; The warning module obtains the comprehensive risk warning level index R based on R=YR×TR, calculates and generates the corresponding warning level according to the threshold, issues warning information, and stores the received data, calculation results and warning information data; The online spot check module is used for spot checks, obtaining real-time data and generating inspection records to ensure the authenticity and validity of the data.

2. A dust explosion monitoring and early warning system according to claim 1, characterized in that: The system also includes a tracking and supervision module, which is used to track and manage tasks and events. Based on the early warning module, it calculates the enterprise early warning level, manages task allocation, progress tracking and implementation status, and ensures that all tasks are completed on time.

3. The dust explosion monitoring and early warning system according to claim 1 is characterized in that: The system also includes a closed-loop disposal module, which disposes and corrects the inspection records generated by online spot checks, records the results of the rectification process, and realizes closed-loop management.

4. The dust explosion monitoring and early warning system according to claim 1 is characterized in that: In the data acquisition module, video data is collected, uploaded and played through the video module, supporting real-time video monitoring.

5. The dust explosion monitoring and early warning system according to claim 1 is characterized in that: The system includes an enterprise end and a supervision end. The enterprise end includes a data acquisition module, a data transmission module, a data storage module, a data processing module, a video module, an early warning module, and a tracking and supervision module; the supervision end includes a data transmission module, a data storage module, a data processing module, a video module, an early warning module, a tracking and supervision module, an online spot check module, and a closed-loop disposal module.

6. A dust explosion monitoring and early warning system according to claim 1, 2, 3, 4 or 5, characterized in that: The data collection module includes the collection of basic enterprise information, dust-related information, dust removal system information, cleaning punch-in information, dynamic perception data information, video information, early warning push information and other information; the basic enterprise information includes but is not limited to the following information related to the enterprise: enterprise name, unified social credit code, business status, production and operation mode, administrative affiliation, national economic type, business address, administrative region code, legal representative, legal representative telephone number, person in charge of production safety management, telephone number of person in charge of production safety management, main business; the dust-related information includes but is not limited to the following information related to dust: dust industry type, high-risk processes involving dust, main dust types, daily dust production, number of dust workers, and related explosion control measures; the dust removal system information includes but is not limited to the following information related to the dust removal system: equipment code, equipment name, workshop, equipment type, Equipment manufacturer, installation date, installation area, dust removal type; the dynamic sensing data information includes but is not limited to one or more of the following sensor parameters: dust removal system start and stop signal, dust collector inlet and outlet pressure difference, air lock and ash unloading fault signal, dust collector ash hopper temperature, explosion prevention and control measures failure signal, dust collector fire extinguishing device effectiveness signal, explosion relief device failure signal, explosion-proof valve self-locking feedback signal, filter cleaning air source pressure, water tank water level signal, spray water flow, metal powder storage place hydrogen concentration signal, filtered water pressure; the cleaning punch-in information includes but is not limited to the following information related to cleaning punch-in: cleaning items, completion status, punch-in person, punch-in date; the video information is a surveillance video of an explosive environment; the warning push information includes but is not limited to the following information related to the warning: warning level, warning start time, warning end time, warning status, warning description, and warning issuing unit.

7. A dust explosion monitoring and early warning system according to claim 6, characterized in that: The working method is as follows: S1. Data acquisition of the data acquisition module, including enterprise information, sensor dynamic perception data information, equipment information basic data, and video data. The data of the environment, equipment and sensors are obtained through manual and automated methods. The video data is collected through the video module to collect, upload and play videos, supporting real-time video monitoring functions. The switch information of the equipment sensor is ensured to be accurate through high-speed detection circuits and multiple de-jitter processing. The analog signal of the equipment sensor is processed by anti-interference and then used with a ∑-Δ analog / digital converter, combined with weighted average and median value algorithms. The final data is displayed as operating status or actual value, with marked status such as normal, fault, alarm, etc. S2, the data transmission module includes a network module, which transmits the collected data from the device to the server through MQTT, HTTP, WebSocket and other protocols. This module ensures data integrity and provides support for real-time analysis and early warning response of the system. After the data collection module is sent to the server through the data transmission module, it enters the data processing module; S3, the data storage module process: after the data is transmitted to the server, the data storage module stores it in categories, supports structured data storage, uses MySQL for persistent storage, and combines Redis technology to optimize data caching and retrieval; S4. The data processing module organizes, cleans, counts and analyzes the data collected by the data collection module to generate usable business data related to the calculation of the comprehensive risk warning level index R, supporting subsequent warning judgment, equipment warning prompts and decision support. The data in the data processing module is presented on the enterprise side and the regulatory side, where the enterprise side and the regulatory side need to log in through the encryption lock; S5. The early warning module monitors the data processed by the data processing module in real time. When the data exceeds the standard or is abnormal (with the threshold of the risk assessment model), it generates early warning signals of different levels, triggers an alarm and notifies the enterprise and regulatory ends; S6, Online spot check module: remote spot checks can be initiated at any time to obtain enterprise information in real time, ensure the authenticity and validity of data, and generate inspection records; After receiving the inspection records, the enterprise will make rectifications to ensure that all problems are thoroughly resolved, and realize a complete closed-loop management process through the closed-loop disposal module.

8. A dust explosion monitoring and early warning system according to claim 7, characterized in that: In S4, the enterprise side presents the simulation diagram, monitoring status, and monitoring values ​​of the dust removal system equipment, which can prompt the equipment location for early warning.

9. A dust explosion monitoring and early warning system according to claim 8, characterized in that: In S5, the monitoring data is collected and updated once every 5 minutes at most as required.

10. A dust explosion monitoring and early warning system according to claim 9, characterized in that: In S6, the supervisory end can switch to the enterprise end to view the enterprise operation status.

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