Chemical industrial park risk management and control system based on identification analysis

Abstract

The invention provides a chemical industry park risk management and control system based on identification analysis, and belongs to the technical field of industrial control, and the system comprises a sensor monitoring module which monitors the park environment and park equipment based on sensors, and reports the data to an Internet of Things gateway corresponding to each sensor; the identification coding module is used for carrying out identification coding on park equipment which needs to be incorporated into an identification system in the park; the Internet of Things gateway module is used for querying the identification codes of the sensors, processing the sensor data and then forwarding the processed sensor data to the park platform; and the terminal processing module is used for analyzing and processing the received data, generating a corresponding risk analysis report and pushing report data to the terminal management platform. Uniform identity identification and analysis services are provided for all elements of park safety risk management and control, and the intelligence and safety of park management are improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to a chemical park risk management and control system based on identity resolution. Background Art

[0002] With the rapid development of the chemical industry, the number and scale of chemical parks are also expanding. There are a large number of chemical substances and complex production equipment in chemical parks, which may bring potential safety risks. If these risks cannot be monitored and controlled in time, it may cause environmental pollution, equipment failure, and even major accidents such as fire and explosion. Therefore, how to effectively monitor and manage the risks in chemical parks has become an urgent problem to be solved.

[0003] Existing technical solutions mainly obtain environmental and equipment data in the park through manual inspections and fixed monitoring points. However, these methods have certain limitations, such as the risk of delays and missed inspections in manual inspections, and the limited monitoring range of fixed monitoring points, which cannot fully cover the entire park. At the same time, the data processing and analysis capabilities of existing technical solutions are limited, and real-time monitoring and efficient risk assessment cannot be achieved. Therefore, a more intelligent, comprehensive and efficient chemical park risk management and control system is needed to improve the level of safety management.

[0004] Therefore, the present invention provides a chemical park risk management and control system based on identity resolution. Summary of the invention

[0005] The present invention provides a chemical park risk management and control system based on identification resolution, which is used to realize real-time monitoring and risk management of the environment and equipment in the chemical park by combining identification resolution technology and the Internet of Things. Through sensor monitoring, identification coding, Internet of Things gateway processing and terminal analysis, the system can efficiently identify and handle potential risks, generate accurate risk analysis reports, provide unified identity identification and resolution services for various elements of park safety risk management, and improve the intelligence and security of park management.

[0006] The present invention provides a chemical park risk management and control system based on identity resolution, comprising: The sensor monitoring module monitors the campus environment and campus equipment based on sensors and reports the data to the IoT gateway corresponding to each sensor; The identification coding module is used to identify and code the park equipment that needs to be included in the identification system; IoT gateway module: used to query the sensor identification code, process the sensor data, and then forward the processed sensor data to the park platform; The terminal processing module is used to analyze and process the received data, generate corresponding risk analysis reports, and push the report data to the terminal management platform.

[0007] The present invention provides a chemical park risk management and control system based on identification resolution, a sensor monitoring module, comprising: Data acquisition unit: monitors the park environment and park equipment based on sensors, and obtains real-time monitoring data of the park environment and park equipment; Scoring determination unit: obtains historical operation data of park equipment, and determines the status score of each park equipment in combination with the park environment and real-time monitoring data of park equipment: ;in, Score the status of the i-th campus device, is the preset weight corresponding to the real-time status score of the i-th park equipment, is the preset weight corresponding to the historical status score of the i-th campus device, is the preset weight corresponding to the environmental adaptability score of the i-th park equipment; in, Score the real-time status of the i-th campus device: ;in, is the real-time monitoring value of the zth parameter of the i-th park equipment, is the preset normal value of the zth parameter of the i-th campus equipment, and are the historical maximum and minimum values ​​of the zth parameter of the i-th campus device, is the weight of the zth parameter of the i-th campus equipment, is the coefficient of the fluctuation rate of the zth parameter of the i-th park equipment, is the zth parameter of the i-th park equipment in unit time Real-time rate of change within t, is the nonlinear adjustment coefficient of the zth parameter of the i-th park equipment, is the number of parameters of the i-th park equipment, is the preset time attenuation factor corresponding to the zth parameter of the i-th park equipment, is the time difference between the real-time monitoring time of the zth parameter of the i-th park equipment and the current time; in, Score the historical status of the i-th campus device: ;in, The operation score of the i-th park equipment in the j-th historical period, is the reliability coefficient of the i-th park equipment in the j-th historical period, m is the total number of historical data periods, is the time decay factor of the jth historical period, t is the current time, is the preset time point of the jth historical period, is the time decay rate corresponding to the jth historical period, is the weight coefficient of the cycle-to-cycle correlation corresponding to the jth historical cycle, is the covariance between the j-th and k-th historical period running scores, is the standard deviation of the operation score of the i-th park equipment in the j-th historical period, is the standard deviation of the operation score of the i-th campus equipment in the k-th historical period, and ; in, Score the environmental adaptability of the i-th campus equipment: ;in, is the real-time value of the dth environmental parameter in the environment where the i-th campus device is located, is the preset normal value of the dth environmental parameter in the environment where the i-th campus device is located, is the historical maximum value of the dth environmental parameter in the environment where the i-th campus device is located. is the historical minimum value of the dth environmental parameter in the environment where the i-th campus device is located. is the weight of the dth environmental parameter in the environment where the i-th campus equipment is located relative to the equipment operating status, is the number of environmental parameters in the environment where the i-th campus equipment is located; Level determination unit: determines the priority of each campus device based on the status score of each campus device and a preset status score-priority data table; Determine the priority of the corresponding monitoring data based on the priority of each park device; Data reporting unit: reports the monitoring data and environmental monitoring data of the park equipment with a priority greater than the preset priority threshold to the IoT gateway corresponding to each sensor; Data storage unit: stores the monitoring data of the park equipment whose priority is less than or equal to the preset priority threshold and updates the priority.

[0008] The present invention provides a chemical park risk management and control system based on identification resolution, an identification coding module, including: The first encoding unit is used to obtain the basic attributes of the campus equipment that needs to be included in the identification system, and perform the first identification encoding on the campus equipment based on the preset encoding rules and the basic attributes of the campus equipment, thereby generating a basic identification chain for each campus equipment; Monitoring and acquisition unit: Based on the monitoring data of the park equipment and environment monitored by sensors, the current status of each park equipment and the current environmental data are acquired; A first updating unit: performing a first update on the first identification code of each park device based on the current state of each park device and the current environmental data, and generating an initial identification chain for each park device; The second updating unit: obtains the historical status data of each park device, performs a second update on the initial identification chain of each park device in combination with the current status of each park device, and obtains the dynamic identification chain corresponding to each park device.

[0009] The present invention provides a chemical park risk management and control system based on identity resolution, and the basic attributes of park equipment include: equipment type, equipment location, equipment model, equipment serial number, installation date and equipment function.

[0010] The present invention provides a chemical park risk management and control system based on identity resolution, an Internet of Things gateway module, comprising: Obtain the dynamic identification chain of each campus device and confirm the identity of the campus device in combination with sensor data; Determine the forwarding priority of each campus device's monitoring data based on the priority of the campus device's monitoring data, the amount of data monitored by the campus device, and the current network status: ;in, is the forwarding priority of the monitoring data of the i-th campus device, is the data priority of the i-th campus device, is the amount of monitoring data of the i-th park equipment, is the weight coefficient of the impact of data volume on priority, is the weight coefficient of the impact of network status on priority, is the network status influencing factor, is the current network load level, and , is the network jitter factor.

[0011] The present invention provides a chemical park risk management and control system based on identity resolution, a terminal processing module, comprising: Data allocation unit: allocates current sensor data to different processing paths based on the data type of the current sensor data; A first identification unit: performing a first identification on the processed sensor data based on a preset rule engine to determine first abnormal data of each type of sensor data; A second identification unit: performing a second identification on the processed sensor data based on a preset algorithm and historical operation data of the park equipment corresponding to each type of sensor data, and determining second abnormal data of each type of sensor data; State prediction unit: generates state prediction information for each park device based on a preset prediction model, processed sensor data, and historical operation data of the park device corresponding to each type of sensor data; Data matching unit: matching the first abnormal data and the second abnormal data of each type of sensor data with the park equipment, and then determining the third abnormal data and the fourth abnormal data of each park equipment; Report generation unit: Generates a corresponding risk analysis report based on the third abnormal data, the fourth abnormal data and the status prediction information of each park device, and pushes the report data to the terminal management platform.

[0012] The present invention provides a chemical park risk management and control system based on identity resolution, with a preset rule engine, comprising: a basic rule layer, an advanced rule layer and a dynamic rule layer.

[0013] The present invention provides a chemical park risk management and control system based on identity resolution, and the preset algorithms include: a convolutional neural network, a recurrent neural network and a long short-term memory network.

[0014] The present invention provides a chemical park risk management and control system based on identification resolution, which realizes real-time monitoring and risk management of the environment and equipment in the chemical park by combining identification resolution technology and the Internet of Things. Through sensor monitoring, identification coding, Internet of Things gateway processing and terminal analysis, the system can efficiently identify and handle potential risks, generate accurate risk analysis reports, and provide unified identity identification and resolution services for various elements of park safety risk management, thereby improving the intelligence and security of park management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a system schematic diagram of a chemical park risk management and control system based on identity resolution provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1 like Figure 1 As shown, a chemical park risk management and control system based on identity resolution provided by an embodiment of the present invention includes: The sensor monitoring module monitors the campus environment and campus equipment based on sensors and reports the data to the IoT gateway corresponding to each sensor; The identification coding module is used to identify and code the park equipment that needs to be included in the identification system; IoT gateway module: used to query the sensor identification code, process the sensor data, and then forward the processed sensor data to the park platform; The terminal processing module is used to analyze and process the received data, generate corresponding risk analysis reports, and push the report data to the terminal management platform.

[0019] In this embodiment, the IoT gateway corresponding to each sensor means that each sensor will be connected to a specific IoT gateway. The IoT gateway is responsible for collecting data from the sensor and uploading it to the campus platform after aggregating it. This configuration ensures the accurate transmission and processing of data, so that the data of each sensor can be correctly identified and managed. Through this one-to-one or one-to-many configuration, the system can be flexibly expanded. When adding new sensors, they only need to be connected to the corresponding IoT gateway.

[0020] In this embodiment, the identification code is a unique identification assignment for all devices and sensors that need to be monitored in the park. Each device and sensor will be given a unique identification code (such as a QR code, RFID, etc.), which is used to identify and track specific devices and data sources in the entire monitoring system. This coding method ensures that in large-scale chemical parks, devices and sensors can be quickly and accurately identified and managed, thereby avoiding confusion and misoperation.

[0021] In this embodiment, querying the identification code of the sensor means that the IoT gateway module determines the identity of the sensor and the corresponding data source by reading or receiving the identification code of the sensor. This process is the basis of system data management. The IoT gateway needs to first query the identification code of the sensor, and then process, route and forward the sensor data to the campus platform according to this code. This ensures that the source of the data can be traced, enhancing the security and management efficiency of the system.

[0022] In this embodiment, the risk analysis report is a document or data set generated by the system terminal processing module after analyzing and processing the received sensor data. It contains the analysis results of the park environment and equipment operating status, including potential risk sources, abnormal conditions, trend forecasts, etc. The risk analysis report is an important basis for park managers to make decisions. It provides decision makers with an intuitive display of risk status through a combination of charts, text and data, and can be viewed and managed in real time through the terminal management platform.

[0023] The beneficial effects of the above technical solution are: by combining identification resolution technology and the Internet of Things, real-time monitoring and risk control of the environment and equipment in the chemical park are realized. Through sensor monitoring, identification coding, Internet of Things gateway processing and terminal analysis, the system can efficiently identify and handle potential risks, generate accurate risk analysis reports, and provide unified identity identification and resolution services for various elements of park safety risk management, thereby improving the intelligence and security of park management.

[0024] Example 2 The embodiment of the present invention provides a chemical park risk management and control system based on identity resolution, a sensor monitoring module, including: Data acquisition unit: monitors the park environment and park equipment based on sensors, and obtains real-time monitoring data of the park environment and park equipment; Scoring determination unit: obtains historical operation data of park equipment, and determines the status score of each park equipment in combination with the park environment and real-time monitoring data of park equipment: ;in, Score the status of the i-th campus device, is the preset weight corresponding to the real-time status score of the i-th park equipment, is the preset weight corresponding to the historical status score of the i-th campus device, is the preset weight corresponding to the environmental adaptability score of the i-th park equipment; in, Score the real-time status of the i-th campus device: ;in, is the real-time monitoring value of the zth parameter of the i-th park equipment, is the preset normal value of the zth parameter of the i-th campus equipment, and are the historical maximum and minimum values ​​of the zth parameter of the i-th campus device, is the weight of the zth parameter of the i-th campus equipment, is the coefficient of the fluctuation rate of the zth parameter of the i-th park equipment, is the zth parameter of the i-th park equipment in unit time Real-time rate of change within t, is the nonlinear adjustment coefficient of the zth parameter of the i-th park equipment, is the number of parameters of the i-th park equipment, is the preset time attenuation factor corresponding to the zth parameter of the i-th park equipment, is the time difference between the real-time monitoring time of the zth parameter of the i-th park equipment and the current time; in, Score the historical status of the i-th campus device: ;in, The operation score of the i-th park equipment in the j-th historical period, is the reliability coefficient of the i-th park equipment in the j-th historical period, m is the total number of historical data periods, is the time decay factor of the jth historical period, t is the current time, is the preset time point of the jth historical period, is the time decay rate corresponding to the jth historical period, is the weight coefficient of the cycle-to-cycle correlation corresponding to the jth historical cycle, is the covariance between the j-th and k-th historical period running scores, is the standard deviation of the operation score of the i-th park equipment in the j-th historical period, is the standard deviation of the operation score of the i-th campus equipment in the k-th historical period, and ; in, Score the environmental adaptability of the i-th campus equipment: ;in, is the real-time value of the dth environmental parameter in the environment where the i-th campus device is located, is the preset normal value of the dth environmental parameter in the environment where the i-th campus device is located, is the historical maximum value of the dth environmental parameter in the environment where the i-th campus device is located. is the historical minimum value of the dth environmental parameter in the environment where the i-th campus device is located. is the weight of the dth environmental parameter in the environment where the i-th campus equipment is located relative to the equipment operating status, is the number of environmental parameters in the environment where the i-th campus equipment is located; Level determination unit: determines the priority of each campus device based on the status score of each campus device and a preset status score-priority data table; Determine the priority of the corresponding monitoring data based on the priority of each park device; Data reporting unit: reports the monitoring data and environmental monitoring data of the park equipment with a priority greater than the preset priority threshold to the IoT gateway corresponding to each sensor; Data storage unit: stores the monitoring data of the park equipment whose priority is less than or equal to the preset priority threshold and updates the priority.

[0025] In this embodiment, the nonlinear adjustment coefficient is used to adjust the influence of the park equipment parameters so that the influence of certain parameters on the equipment status score does not change linearly, but is nonlinearly enlarged or reduced according to the specific situation. It is used to consider the complexity of parameter changes. For example, the change of a certain parameter has different effects on the equipment status in different ranges. If the temperature exceeds a certain critical value, the damage to the equipment will increase sharply. Then the nonlinear adjustment coefficient of the parameter can amplify this impact and reflect it in the equipment status score.

[0026] In this embodiment, the reliability coefficient reflects the stability and reliability of the equipment in the historical period, which is obtained by statistically analyzing the equipment's operating performance in different historical periods. A high reliability coefficient means that the equipment has performed stably in the past and has a low failure rate, while a low reliability coefficient indicates that the equipment may have a high risk of failure. If a device has an extremely low failure frequency in the past year, its reliability coefficient will be higher. Conversely, if the device fails frequently, the reliability coefficient will be lower.

[0027] In this embodiment, the time difference between the real-time monitoring time and the current time refers to the difference between the time when the sensor last successfully monitored the device parameters and the current time. This difference is used to evaluate the timeliness of the monitoring data and ensure that the status score is based on the latest real-time data. If this time difference is too large, it means that the data is not fresh enough, which may reduce the accuracy of the device status score. For example, the temperature parameter of a device was measured 1 hour ago, and the data now has a time difference of 1 hour from the current time. This time difference will be used to adjust the calculation of the device status score.

[0028] In this embodiment, the preset priority threshold is an important criterion used by the system to screen and process device monitoring data. Based on the status score of the device, the system assigns a priority to each device, and the preset priority threshold determines which devices need to be prioritized for processing, monitoring and reporting data. If the threshold is set to 80 points, then devices with a priority higher than 80 points will be given priority attention, and relevant data will be uploaded and processed in a timely manner, while device data with a priority lower than or equal to 80 points may only be stored or delayed.

[0029] In this embodiment, updating the priority means that the system dynamically adjusts the priority of the device according to the latest monitoring data and device status score. As the device operating status and environmental conditions change, the risk level of the device will also change. Therefore, the system needs to update the priority of the device regularly or under specific conditions. For example, when the real-time status score of a device decreases due to abnormal fluctuations in a certain parameter, its priority may need to be increased to ensure that it receives more attention and timely maintenance. The updated priority will affect subsequent data processing and resource allocation decisions.

[0030] The beneficial effects of the above technical solution are: by integrating real-time monitoring, historical data analysis and environmental adaptability assessment, the chemical park equipment is dynamically scored and prioritized, achieving accurate management and control of equipment risks. Based on the comprehensive evaluation of equipment status and environmental data, the system can automatically adjust the processing priority of monitoring data and intelligently report high-risk equipment data, thereby improving the accuracy and real-time performance of equipment monitoring, optimizing resource allocation, and reducing potential risks.

[0031] Example 3 The embodiment of the present invention provides a chemical park risk management and control system based on identification resolution, an identification coding module, including: The first encoding unit is used to obtain the basic attributes of the campus equipment that needs to be included in the identification system, and perform the first identification encoding on the campus equipment based on the preset encoding rules and the basic attributes of the campus equipment, thereby generating a basic identification chain for each campus equipment; Monitoring and acquisition unit: Based on the monitoring data of the park equipment and environment monitored by sensors, the current status of each park equipment and the current environmental data are acquired; A first updating unit: performing a first update on the first identification code of each park device based on the current state of each park device and the current environmental data, and generating an initial identification chain for each park device; The second updating unit: obtains the historical status data of each park device, performs a second update on the initial identification chain of each park device in combination with the current status of each park device, and obtains the dynamic identification chain corresponding to each park device.

[0032] In this embodiment, the basic attributes of the park equipment refer to the basic information of each device in the park, including the type, model, function, manufacturer, installation location, etc. of the equipment, which is used to identify the identity and functional characteristics of the equipment and generate a unique code for each device during the identification coding. For example, the basic attributes of a certain device may include "pump", model "P-1001", manufacturer "Company X", etc.; In this embodiment, the preset coding rule is a set of coding standards formulated based on the basic attributes of the park equipment and other related information, which is used to uniquely identify the equipment in the park and is defined according to factors such as the type, purpose, and installation location of the equipment. For example, the coding rule may require that the equipment code start with the equipment type, followed by the model and serial number, such as "PUMP-P-1001-01" represents a pump. Assume that the basic identification code of a park equipment follows the following structure: TLLMMSSSSDD, T: Equipment type (1 digit) - Use 1 letter to indicate the equipment type, such as 'S' for sensor, 'V' for valve, LL: Equipment location (2 digits) - Use 2 digits to indicate the area where the equipment is located, such as '01' for area 1, '02' for area 2, MM: Equipment model (2 digits) - Use 2 letters to indicate the specific model of the equipment, such as 'AB' for model AB, 'CD' for model CD, SSSS: Equipment serial number (4 digits) - Use 4 digits to indicate the unique serial number of the equipment, such as '1234', DD: Check code (2 digits) - The check code generated according to the previous coding part; In this embodiment, the first identification code is a unique code generated based on the basic attributes of the park equipment and the preset coding rules, which is used for the initial identification of the equipment. The code represents the initial information of the equipment and can be updated as the equipment status or environment changes. For example, the first identification code of a chemical reactor may be "REACTOR-R-5002"; In this embodiment, the basic identification chain is a series of identification information chains generated based on the basic attributes of the device and the first identification code. It is part of the unique identification of the device and contains the initial attributes of the device and its associated information. The chain will be continuously updated as the status of the device changes. For example, the basic identification chain of a pump may contain information such as the device code, installation date, and basic performance indicators. The basic identification chain of a park may include: S01AB567825, V02CD123425, P03EF678935, ...

[0033] In this embodiment, the status of each park device is the current operating status of the device obtained through sensor monitoring, including the real-time parameters of the device (such as temperature, pressure, vibration, etc.) and whether there are faults, abnormalities, etc. These status data are important basis for equipment management and risk assessment. For example, the status of a certain device can show that its operating temperature is 75°C, the pressure is 1.2MPa, and the status is "normal".

[0034] In this embodiment, the first update refers to adjusting the first identification code of the device according to the real-time status and environmental data of the device, so that the identification code of the device can dynamically reflect its current operating conditions and environmental impacts, and the result generated by the first update is the initial identification chain of the device. For example, when the device is working in a high temperature environment, the first update may adjust its identification chain to reflect the potential impact of the environment on the device; In this embodiment, the initial identification chain is based on the basic identification chain of the device, and is generated after the first update in combination with the real-time status of the device and the environmental data, indicating the current status of the device. This chain serves as the basis for the dynamic identification of the device in the subsequent management process. For example, the initial identification chain of a device may include information such as "device type-real-time temperature-real-time status".

[0035] In this embodiment, the second update is to further adjust the initial identification chain of the device based on the historical status data and current status of the device, generate a dynamic identification chain that reflects the historical and current operation status of the device, and ensure that the identification chain of the device can comprehensively reflect the operation trend and status change of the device. For example, if the device has experienced a fault repair in the past, the second update will add this information to the dynamic identification chain; In this embodiment, the dynamic identification chain is a complete identification chain generated by integrating historical status data and current status on the basis of the initial identification chain, dynamically reflecting the status and historical changes of the equipment in its entire life cycle, and is used for risk management and predictive maintenance of the equipment. For example, the dynamic identification chain of a certain equipment can record its historical fault count, current operating parameters, predicted fault risk and other information.

[0036] The beneficial effects of the above technical solution are: through the dynamic identification and coding system, the full life cycle risk management of chemical park equipment is realized. By acquiring equipment and environmental data in real time and updating the identification chain at multiple levels, the system can accurately reflect the current status and historical operation of the equipment, thereby improving the accuracy and response speed of equipment management. It also realizes dynamic tracking of equipment status and risk warning, significantly improving the safety management level of the park.

[0037] Example 4 An embodiment of the present invention provides a chemical park risk management and control system based on identity resolution, and the basic attributes of the park equipment include: equipment type, equipment location, equipment model, equipment serial number, installation date and equipment function.

[0038] In this embodiment, the device type refers to the classification of the device, such as a sensor, a valve, a pump, a reactor, etc. For example, a centrifugal pump responsible for conveying chemicals belongs to the "pump" type; In this embodiment, the equipment location refers to the specific installation location of the equipment in the park, which helps to locate and maintain the equipment. For example, the centrifugal pump is installed next to the No. 3 reactor in the No. 2 production workshop in Area A of the park; In this embodiment, the equipment model refers to the specific model of the equipment, which is used to distinguish different products of the same type. For example, the model of this centrifugal pump is "CPX-1000"; In this embodiment, the device serial number refers to a unique identification number of the device, which is used to track the production and historical information of the device. For example, the serial number of this centrifugal pump is "SN1234567890"; In this embodiment, the installation date refers to the specific date when the equipment is installed in the park, which is used to calculate the service life and maintenance cycle of the equipment. For example, the installation date of this centrifugal pump is "June 15, 2022"; In this embodiment, the function of the equipment refers to the main role or responsibility of the equipment, which helps determine its position in the entire production process. For example, the function of this centrifugal pump is to transport chemical solution from one container to another.

[0039] The beneficial effects of the above technical solution are: by integrating basic attributes such as equipment type, location, model, serial number, installation date and function, accurate identification and comprehensive management of chemical park equipment are achieved, and these multi-dimensional attributes are used to generate unique identification, which improves the efficiency and accuracy of equipment tracking, maintenance and risk assessment, and ensures safe management throughout the equipment life cycle.

[0040] Example 5 The embodiment of the present invention provides a chemical park risk management and control system based on identity resolution, an Internet of Things gateway module, including: Obtain the dynamic identification chain of each campus device and confirm the identity of the campus device in combination with sensor data; Determine the forwarding priority of each campus device's monitoring data based on the priority of the campus device's monitoring data, the amount of data monitored by the campus device, and the current network status: ;in, is the forwarding priority of the monitoring data of the i-th campus device, is the data priority of the i-th campus device, is the amount of monitoring data of the i-th park equipment, is the weight coefficient of the impact of data volume on priority, is the weight coefficient of the impact of network status on priority, is the network status influencing factor, is the current network load level, and , is the network jitter factor.

[0041] In this embodiment, forwarding priority refers to the order and importance of data transmission by each device when transmitting data in the network. Data with high priority will be forwarded first to ensure that important information can be delivered in time. For example, for emergency alarm data, the forwarding priority is set to the highest to ensure that it can be transmitted in time even in the case of network congestion. In this embodiment, the network jitter factor refers to the degree of fluctuation of the network status, which has an important impact on the stability of data transmission. When calculating the forwarding priority, the network jitter factor is used to weigh the current network status. For example, if the current network jitter factor is high, it means that the network is unstable. The system will adjust the data forwarding strategy to give priority to the transmission of important data to avoid delays.

[0042] The beneficial effects of the above technical solution are: accurately identifying campus equipment through dynamic identification chains and sensor data, and intelligently determining data forwarding priority based on data priority, data volume and network status, considering network load and jitter factors, optimizing data transmission paths, and ensuring the real-time and reliability of key monitoring data in complex network environments.

[0043] Example 6 The embodiment of the present invention provides a chemical park risk management and control system based on identity resolution, and a terminal processing module, including: Data allocation unit: allocates current sensor data to different processing paths based on the data type of the current sensor data; A first identification unit: performing a first identification on the processed sensor data based on a preset rule engine to determine first abnormal data of each type of sensor data; A second identification unit: performing a second identification on the processed sensor data based on a preset algorithm and historical operation data of the park equipment corresponding to each type of sensor data, and determining second abnormal data of each type of sensor data; State prediction unit: generates state prediction information for each park device based on a preset prediction model, processed sensor data, and historical operation data of the park device corresponding to each type of sensor data; Data matching unit: matching the first abnormal data and the second abnormal data of each type of sensor data with the park equipment, and then determining the third abnormal data and the fourth abnormal data of each park equipment; Report generation unit: Generates a corresponding risk analysis report based on the third abnormal data, the fourth abnormal data and the status prediction information of each park device, and pushes the report data to the terminal management platform.

[0044] In this embodiment, the processing paths are to distribute data to different processing paths according to the sensor data type (such as temperature, pressure, humidity, etc.), for example, temperature sensor data may enter a dedicated temperature analysis path, and pressure sensor data may enter a pressure monitoring path; In this embodiment, the preset rule engine is a decision-making system based on predefined rules (such as thresholds or pattern matching) for analyzing sensor data. The rule engine can pre-set certain data to trigger an alarm when it exceeds a specific range. For example, if the temperature exceeds the set threshold, the rule engine will identify this data as abnormal; In this embodiment, the first identification is a preliminary data analysis based on the rule engine, which is used to quickly identify the obviously abnormal parts in the sensor data. For example, when the temperature sensor reading exceeds the preset range, the first identification marks it as abnormal data; In this embodiment, the first abnormal data (data after the first identification, which is initially determined to be data that does not conform to the normal range, for example, if the temperature of a device suddenly rises to more than 100°C, the temperature data is marked as the first abnormal data.

[0045] In this embodiment, the second identification is an in-depth analysis based on a preset complex algorithm (such as machine learning or historical data analysis), taking into account the historical operation data and long-term trends of the device to determine the abnormal situation. For example, combined with the historical operation mode of the device, the second identification may find that the sensor data of a certain device fluctuates abnormally multiple times in a short period of time; In this embodiment, the second abnormal data is data that has undergone the second identification and is further determined to be abnormal. For example, if a device has abnormal pressure fluctuations for multiple times within a period of time, these data are marked as second abnormal data.

[0046] In this embodiment, the preset predictive model (Predefined Predictive Model) This is a mathematical or machine learning model used to predict the future state of a device based on current sensor data and historical data. For example, a predictive model can predict the operating state of a device in the next 24 hours to prevent potential failures. In this embodiment, the status prediction information The prediction model generates an estimate of the future operating status of the equipment. For example, the prediction information of a certain equipment may show that it has a 30% probability of failure within the next day.

[0047] In this embodiment, the third abnormal data is determined as the first comprehensive abnormal data of the device by matching the first abnormal data with the device. For example, the system determines the third abnormal data of the device by combining multiple abnormal data of the temperature and pressure sensors. In this embodiment, the fourth abnormal data is determined as the second comprehensive abnormal data of the device by matching the second abnormal data with the device.

[0048] The beneficial effects of the above technical solution are: through multi-level data processing and intelligent analysis, it realizes the anomaly identification and equipment status prediction of sensor data, combines preset algorithms with historical data for in-depth analysis, accurately matches abnormal data, and generates comprehensive risk analysis reports, which effectively improves the safety management level of the park, reduces potential risks, and ensures the stable operation of equipment.

[0049] Example 7 The embodiment of the present invention provides a chemical park risk management and control system based on identity resolution, with a preset rule engine, including: a basic rule layer, an advanced rule layer and a dynamic rule layer.

[0050] In this embodiment, the basic rule layer defines a set of fixed and relatively simple rules for identifying obvious abnormal situations or illegal operations, and monitoring based on standard thresholds and boundary conditions. For example, a basic rule may be "if the temperature of a certain device exceeds 100°C, trigger a high temperature alarm." This rule is simple and direct, and is used to quickly respond to obvious abnormal data; In this embodiment, the advanced rule layer contains more complex rules for identifying abnormal situations that require a combination of multiple factors. It is based on multivariate analysis, historical data trends or association rules, and is intended to capture more complex abnormal patterns or potential risks. An advanced rule may be "if the temperature of a device exceeds 90°C for 30 minutes and the pressure fluctuates by more than 20% during this period, a composite abnormal alarm is triggered". This rule considers the relationship between multiple parameters and time factors, and can identify more complex abnormal situations. In this embodiment, the dynamic rule layer is the most flexible rule layer, which can automatically adjust or generate rules based on real-time data and machine learning algorithms. It uses adaptive models and data analysis techniques to dynamically optimize and update rules over time to cope with the ever-changing operating environment and potential risks. Dynamic rules may be generated based on machine learning models, such as "automatically adjusting the monitoring thresholds of temperature and pressure based on pattern changes in historical operating data and current sensor data to reduce false alarm rates and improve the accuracy of anomaly detection." The system will regularly evaluate and adjust rules to ensure that they adapt to new environments and risk characteristics.

[0051] The beneficial effects of the above technical solution are: through layered design, including basic, advanced, and dynamic rule layers, combined with fixed thresholds, multivariate analysis, and adaptive adjustment, it provides a more flexible and accurate risk management method, effectively improves the safety monitoring capabilities of chemical parks, reduces false alarms, and can dynamically respond to complex operating environments, significantly improving the intelligence level of risk management.

[0052] Example 8 An embodiment of the present invention provides a chemical park risk management and control system based on identity resolution, and the preset algorithms include: a convolutional neural network, a recurrent neural network, and a long short-term memory network.

[0053] In this embodiment, a convolutional neural network is used to identify short-term anomalies in time series data, and a recurrent neural network or a long short-term memory network is used to identify long-term trends in historical data. For example, when identifying seasonal risk patterns, the system can analyze the operating conditions of different seasons in historical data, extract the impact of seasonal changes on equipment status, and make predictions based on current data to provide early warning of possible safety hazards.

[0054] The beneficial effects of the above technical solution are: by integrating convolutional neural networks, recurrent neural networks and long short-term memory networks, an efficient multidimensional data analysis model is constructed, which realizes the accurate identification and prediction of risks in chemical parks. The combination enhances the system's ability to capture complex data patterns, improves the accuracy and real-time performance of anomaly detection, and effectively supports the intelligent risk management of the park.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A chemical park risk management and control system based on identity resolution, characterized in that: include: The sensor monitoring module monitors the campus environment and campus equipment based on sensors and reports the data to the IoT gateway corresponding to each sensor; The identification coding module is used to identify and code the park equipment that needs to be included in the identification system; IoT gateway module: used to query the sensor identification code, process the sensor data, and then forward the processed sensor data to the park platform; The terminal processing module is used to analyze and process the received data, generate corresponding risk analysis reports, and push the report data to the terminal management platform.

2. According to claim 1, a chemical park risk management and control system based on identity resolution is characterized in that: Sensor monitoring module, including: Data acquisition unit: monitors the park environment and park equipment based on sensors, and obtains real-time monitoring data of the park environment and park equipment; Scoring determination unit: obtains historical operation data of park equipment, and determines the status score of each park equipment in combination with the park environment and real-time monitoring data of park equipment: ;in, Score the status of the i-th campus device, is the preset weight corresponding to the real-time status score of the i-th park equipment, is the preset weight corresponding to the historical status score of the i-th campus device, is the preset weight corresponding to the environmental adaptability score of the i-th park equipment; in, Score the real-time status of the i-th campus device: ;in, is the real-time monitoring value of the zth parameter of the i-th park equipment, is the preset normal value of the zth parameter of the i-th campus equipment, and are the historical maximum and minimum values ​​of the zth parameter of the i-th campus device, is the weight of the zth parameter of the i-th campus equipment, is the coefficient of the fluctuation rate of the zth parameter of the i-th park equipment, is the zth parameter of the i-th park equipment in unit time Real-time rate of change within t, is the nonlinear adjustment coefficient of the zth parameter of the i-th park equipment, is the number of parameters of the i-th park equipment, is the preset time attenuation factor corresponding to the zth parameter of the i-th park equipment, is the time difference between the real-time monitoring time of the zth parameter of the i-th park equipment and the current time; in, Score the historical status of the i-th campus device: ;in, is the operation score of the i-th park equipment in the j-th historical period, is the reliability coefficient of the i-th park equipment in the j-th historical period, m is the total number of historical data periods, is the time decay factor of the jth historical period, t is the current time, is the preset time point of the jth historical period, is the time decay rate corresponding to the jth historical period, is the weight coefficient of the cycle-to-cycle correlation corresponding to the jth historical cycle, is the covariance between the j-th and k-th historical period running scores, is the standard deviation of the operation score of the i-th park equipment in the j-th historical period, is the standard deviation of the operation score of the i-th campus equipment in the k-th historical period, and ; in, Score the environmental adaptability of the i-th campus equipment: ;in, is the real-time value of the dth environmental parameter in the environment where the i-th campus device is located, is the preset normal value of the dth environmental parameter in the environment where the i-th campus device is located, is the historical maximum value of the dth environmental parameter in the environment where the i-th campus device is located. is the historical minimum value of the dth environmental parameter in the environment where the i-th campus device is located. is the weight of the dth environmental parameter in the environment where the i-th campus equipment is located relative to the equipment operating status, is the number of environmental parameters in the environment where the i-th park equipment is located; Level determination unit: determines the priority of each campus device based on the status score of each campus device and a preset status score-priority data table; Determine the priority of the corresponding monitoring data based on the priority of each park device; Data reporting unit: reports the monitoring data and environmental monitoring data of the park equipment with a priority greater than the preset priority threshold to the IoT gateway corresponding to each sensor; Data storage unit: stores the monitoring data of the park equipment whose priority is less than or equal to the preset priority threshold and updates the priority.

3. According to claim 1, a chemical park risk management and control system based on identity resolution is characterized in that: Identification coding module, including: The first encoding unit is used to obtain the basic attributes of the campus equipment that needs to be included in the identification system, and perform the first identification encoding on the campus equipment based on the preset encoding rules and the basic attributes of the campus equipment, thereby generating a basic identification chain for each campus equipment; Monitoring and acquisition unit: Based on the monitoring data of the park equipment and environment monitored by sensors, the current status of each park equipment and the current environmental data are acquired; A first updating unit: performing a first update on the first identification code of each park device based on the current state of each park device and the current environmental data, and generating an initial identification chain for each park device; The second updating unit: obtains the historical status data of each park device, performs a second update on the initial identification chain of each park device in combination with the current status of each park device, and obtains the dynamic identification chain corresponding to each park device.

4. According to claim 1, a chemical park risk management and control system based on identity resolution is characterized in that: Basic attributes of campus equipment, including: equipment type, equipment location, equipment model, equipment serial number, installation date, and equipment function.

5. According to claim 3, a chemical park risk management and control system based on identity resolution is characterized in that: IoT gateway module, including: Obtain the dynamic identification chain of each campus device and confirm the identity of the campus device in combination with sensor data; Determine the forwarding priority of each campus device's monitoring data based on the priority of the campus device's monitoring data, the amount of data monitored by the campus device, and the current network status: ;in, is the forwarding priority of the monitoring data of the i-th campus device, is the data priority of the i-th campus device, is the amount of monitoring data of the i-th park equipment, is the weight coefficient of the impact of data volume on priority, is the weight coefficient of the impact of network status on priority, is the network status influencing factor, is the current network load level, and , is the network jitter factor.

6. A chemical park risk management and control system based on identity resolution according to claim 2, characterized in that: Terminal processing module, including: Data allocation unit: allocates current sensor data to different processing paths based on the data type of the current sensor data; A first identification unit: performing a first identification on the processed sensor data based on a preset rule engine to determine first abnormal data of each type of sensor data; A second identification unit: performing a second identification on the processed sensor data based on a preset algorithm and historical operation data of the park equipment corresponding to each type of sensor data, and determining second abnormal data of each type of sensor data; State prediction unit: generates state prediction information for each park device based on a preset prediction model, processed sensor data, and historical operation data of the park device corresponding to each type of sensor data; Data matching unit: matching the first abnormal data and the second abnormal data of each type of sensor data with the park equipment, and then determining the third abnormal data and the fourth abnormal data of each park equipment; Report generation unit: Generates a corresponding risk analysis report based on the third abnormal data, the fourth abnormal data and the status prediction information of each park device, and pushes the report data to the terminal management platform.

7. A chemical park risk management and control system based on identity resolution according to claim 6, characterized in that: The preset rule engine includes: basic rule layer, advanced rule layer and dynamic rule layer.

8. A chemical park risk management and control system based on identity resolution according to claim 6, characterized in that: Preset algorithms include: convolutional neural networks, recurrent neural networks, and long short-term memory networks.

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