Reconnaissance remote monitoring system based on artificial intelligence

Abstract

The invention discloses a survey remote monitoring system based on artificial intelligence, and particularly relates to the field of artificial intelligence, which comprises a monitoring time division module, a data acquisition module, a data analysis module, an enterprise environmental protection anomaly index calculation module, an early warning module and a man-machine interaction module. According to the invention, environmental protection monitoring process data of a target enterprise is divided into monitoring times and numbered through a monitoring time division module, environmental protection monitoring process data of the target enterprise in each monitoring time is acquired through a data acquisition module, and data transmitted by the acquisition module is analyzed through a data analysis module. The data transmitted by the data analysis module is integrated and calculated through the enterprise environmental protection anomaly index calculation module to obtain an enterprise environmental protection anomaly index, and the enterprise environmental protection anomaly index transmitted by the enterprise environmental protection anomaly index calculation module is evaluated and judged through the early warning module. And man-machine interaction is carried out through a dynamic interaction technology and a data visualization technology of the man-machine interaction module.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and more specifically, to a survey remote monitoring system based on artificial intelligence. Background Art

[0002] In recent years, environmental issues have received much attention, and corporate environmental protection work has also received more and more attention. Corporate environmental management is not only the fulfillment of social responsibility, but also a necessary measure to enhance corporate image and sustainable development. Corporate environmental monitoring specifically refers to the monitoring and analysis of corporate production activities and emissions to assess their impact on the environment. It can also help companies understand the impact of their production processes and products on the environment, so as to take appropriate measures to reduce the negative impact on the environment.

[0003] The existing survey remote monitoring system is an automated system that integrates monitoring, data transmission, processing, analysis and reporting. It ensures that the company's production activities comply with environmental laws and regulations and standards by real-time monitoring of emissions, energy consumption and resource utilization efficiency in the company's production process. At the same time, the system can also process and analyze monitoring data to generate detailed environmental monitoring reports, providing companies with a basis for environmental management and decision-making. In addition, the system also has data disclosure and sharing functions, which strengthens social supervision and corporate self-discipline.

[0004] However, there are still some shortcomings in its actual use. For example, the automation and intelligence level of the existing survey remote monitoring system needs to be improved. Although many systems have realized automatic monitoring and data analysis, there are still some links that require manual intervention, which not only increases the difficulty of operation, but may also cause inaccurate data due to human factors, affecting the monitoring efficiency and accuracy. In addition, the traditional data processing and analysis methods have low accuracy and it is difficult to detect some subtle environmental problems. Secondly, the visualization of the system needs to be strengthened. For corporate environmental monitoring, data visualization is very important. However, the existing systems can often only provide a relatively basic data display and cannot intuitively display the deep information and trends behind the data. This has affected the company's comprehensive understanding of the environmental situation to a certain extent. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a survey remote monitoring system based on artificial intelligence, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solution: a survey remote monitoring system based on artificial intelligence, comprising:

[0007] Monitoring time division module: used to obtain the environmental monitoring process data of the target enterprise, divide the environmental monitoring process data of the target enterprise into various monitoring times by equal time division, and mark each monitoring time as 1, 2...n in sequence.

[0008] Data acquisition module: used to collect the environmental monitoring process data of the target enterprise at each monitoring time, through cameras and intelligent sensors, and transmit the collected data to the data analysis module after data cleaning and preprocessing.

[0009] Data analysis module: including equipment safety factor calculation unit, wastewater discharge safety factor calculation unit, and exhaust gas emission safety factor calculation unit, which are used to receive data transmitted by the data acquisition module, and analyze the data transmitted by the acquisition module through the mathematical models corresponding to each calculation unit, and transmit the analysis results to the enterprise environmental protection abnormality index calculation module.

[0010] Enterprise environmental protection abnormality index calculation module: used to receive data transmitted by the data analysis module, integrate and calculate the data transmitted by the data analysis module through the enterprise environmental protection abnormality index calculation unit to obtain the enterprise environmental protection abnormality index, and transmit the enterprise environmental protection abnormality index to the early warning module.

[0011] Early warning module: used to receive the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module, evaluate and judge the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module, and issue an early warning based on the judgment result.

[0012] Human-computer interaction module: Through dynamic interaction technology, real-time interaction between users and the system is realized. Users can interact with the system through gestures and touch, and provide historical data query and visualization functions. Through data visualization technology, the company's environmental monitoring historical data is presented to users in the form of charts and images.

[0013] Preferably, the data acquisition module is used to collect the total feed amount, material loss, material leakage, organic solvent recovery rate, equipment failure times, number of operators, number of equipment, rainwater and sewage diversion rate, sewage tank corrosion depth, emergency valve nominal pressure, emergency valve nominal diameter, waste gas emissions, pollution source waste gas recovery range, waste gas storage seal qualified rate, condensation recovery efficiency and waste gas combustion efficiency of the enterprise at each monitoring time, respectively marked as , , , , , , , , , , , , , , as well as , where i=1, 2…n, i represents the i-th detection time.

[0014] Preferably, the mathematical model used by the equipment safety factor calculation unit is: , represents the equipment safety factor of the enterprise at the i-th monitoring time, represents the total feed amount of the enterprise at the i-th monitoring time, represents the material loss of the enterprise at the i-th monitoring time, represents the material leakage of the enterprise at the i-th monitoring time, represents the organic solvent recovery rate of the enterprise at the i-th monitoring time, represents the number of equipment failures of the enterprise at the i-th monitoring time, represents the number of operators in the enterprise at the i-th monitoring time, represents the number of devices of the enterprise at the i-th monitoring time, Indicates other factors affecting the equipment safety factor.

[0015] Preferably, the mathematical model used by the wastewater discharge safety factor calculation unit is: , represents the enterprise's wastewater discharge safety factor at the i-th monitoring time, represents the rainwater and sewage diversion rate of the enterprise at the i-th monitoring time, represents the corrosion depth of the sewage pool of the enterprise at the i-th monitoring time, represents the nominal pressure of the emergency valve of the enterprise at the i-th monitoring time, represents the nominal diameter of the emergency valve of the enterprise at the i-th monitoring time, represents the time difference between the i-th monitoring time and the i-1-th monitoring time, Represents other influencing factors of wastewater discharge safety factor.

[0016] Preferably, the mathematical model used by the exhaust gas emission safety factor calculation unit is: , represents the exhaust gas emission safety factor of the enterprise at the i-th monitoring time, represents the exhaust gas emissions of the enterprise at the i-th monitoring time, It represents the waste gas recovery scope of the pollution source of the enterprise at the i-th monitoring time, It represents the qualified rate of waste gas storage sealing of the enterprise at the i-th monitoring time, represents the condensation recovery efficiency of the enterprise at the i-th monitoring time, represents the exhaust gas combustion efficiency of the enterprise at the i-th monitoring time, represents the time difference between the i-th monitoring time and the i-1-th monitoring time, Represents other influencing factors of exhaust emission safety factor.

[0017] Preferably, the mathematical model used by the enterprise environmental protection abnormality index calculation unit is: , represents the environmental abnormality index of the enterprise at the i-th monitoring time, represents the equipment safety factor of the enterprise at the i-th monitoring time, represents the enterprise's wastewater discharge safety factor at the i-th monitoring time, represents the exhaust gas emission safety factor of the enterprise at the i-th monitoring time, Indicates the preset value of the enterprise's equipment safety factor. Indicates the preset value of the enterprise's wastewater discharge safety factor, Indicates the preset value of the enterprise's exhaust emission safety factor. Represents other influencing factors of the enterprise's environmental protection abnormality index.

[0018] Preferably, the early warning module evaluates and judges the enterprise environmental protection abnormality index by comparing the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module with the preset value of the enterprise environmental protection abnormality index. When , it means that the enterprise environmental protection abnormality index at the i-th monitoring time is less than the preset value of the enterprise environmental protection abnormality index, indicating that there is no abnormality in the enterprise environmental protection monitoring process at the i-th monitoring time, then the enterprise environmental protection monitoring is maintained. When , it means that the enterprise environmental protection abnormality index at the i-th monitoring time is greater than the preset value of the enterprise environmental protection abnormality index, indicating that the enterprise environmental protection monitoring process at the i-th monitoring time is abnormal, and an early warning signal is issued to relevant personnel.

[0019] Technical effects and advantages of the present invention:

[0020] The present invention monitors and analyzes real-time data through artificial intelligence technology, can quickly and accurately discover abnormal data, and improve monitoring efficiency. Through automated monitoring and analysis, the cost of manual intervention and manual monitoring can be reduced, and the accuracy and reliability of monitoring can also be improved. Through monitoring and analysis of equipment operating status and emission data, potential safety hazards and situations that do not meet environmental protection standards can be discovered and resolved in a timely manner, thereby improving the environmental performance of the enterprise. Through analysis and prediction of equipment operating status and performance, intelligent suggestions and improvement measures can be provided to the enterprise, and the operating status and efficiency of the equipment can be optimized. Through the setting of the early warning module and the application of abnormal detection technology, potential safety hazards and attack behaviors can be discovered and resolved in a timely manner, and the security of the system can be enhanced. Through the visualization of historical data, it can help enterprises better understand the operating status and environmental protection of the equipment, and improve the accuracy and scientificity of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0023] refer to Figure 1 The artificial intelligence-based survey remote monitoring system shown includes a monitoring time division module, a data acquisition module, a data analysis module, an enterprise environmental protection abnormality index calculation module, an early warning module and a human-computer interaction module.

[0024] The monitoring time division module is used to obtain the environmental monitoring process data of the target enterprise, divide the environmental monitoring process data of the target enterprise into various monitoring times by equal time division, and mark each monitoring time as 1, 2...n in sequence.

[0025] The data acquisition module is used to collect the environmental monitoring process data of the target enterprise at each monitoring time, and transmit the collected data to the data analysis module after data cleaning and preprocessing.

[0026] The data collection module is used to collect the total feed amount, material loss, material leakage, organic solvent recovery rate, equipment failure times, number of operators, number of equipment, rainwater and sewage diversion rate, sewage tank corrosion depth, emergency valve nominal pressure, emergency valve nominal diameter, waste gas emissions, pollution source waste gas recovery range, waste gas storage sealing qualified rate, condensation recovery efficiency and waste gas combustion efficiency of the enterprise at each monitoring time, marked as , , , , , , , , , , , , , , as well as , where i=1, 2…n, i represents the i-th detection time.

[0027] The data analysis module includes an equipment safety factor calculation unit, a wastewater discharge safety factor calculation unit, and an exhaust gas emission safety factor calculation unit, which are used to receive data transmitted by the data acquisition module, and analyze the data transmitted by the acquisition module through the mathematical models corresponding to each calculation unit, and transmit the analysis results to the enterprise environmental protection abnormality index calculation module.

[0028] The equipment safety factor calculation unit is used to evaluate and monitor the operating status and safety performance of the enterprise's environmental protection equipment. The coefficient can help the enterprise understand the operating status and performance of the equipment, promptly discover and resolve potential safety hazards, and also provide a basis for the enterprise to improve environmental protection equipment and processes.

[0029] The mathematical model used by the equipment safety factor calculation unit is: , represents the equipment safety factor of the enterprise at the i-th monitoring time, represents the total feed amount of the enterprise at the i-th monitoring time, represents the material loss of the enterprise at the i-th monitoring time, represents the material leakage of the enterprise at the i-th monitoring time, represents the organic solvent recovery rate of the enterprise at the i-th monitoring time, represents the number of equipment failures of the enterprise at the i-th monitoring time, represents the number of operators in the enterprise at the i-th monitoring time, represents the number of devices of the enterprise at the i-th monitoring time, Indicates other factors affecting the equipment safety factor.

[0030] The wastewater discharge safety factor calculation unit is used to evaluate and monitor the performance of enterprises in rainwater and sewage diversion, sewage treatment and emergency response. This factor can help enterprises understand the safety status and performance of the sewage discharge process, and can also provide a basis for enterprises to improve the sewage discharge process technology.

[0031] The mathematical model used by the wastewater discharge safety factor calculation unit is: , represents the enterprise's wastewater discharge safety factor at the i-th monitoring time, represents the rainwater and sewage diversion rate of the enterprise at the i-th monitoring time, represents the corrosion depth of the sewage pool of the enterprise at the i-th monitoring time, represents the nominal pressure of the emergency valve of the enterprise at the i-th monitoring time, represents the nominal diameter of the emergency valve of the enterprise at the i-th monitoring time, represents the time difference between the i-th monitoring time and the i-1-th monitoring time, Represents other influencing factors of wastewater discharge safety factor.

[0032] The waste gas emission safety factor calculation unit is used to evaluate and monitor the performance of the enterprise in waste gas recovery, storage and treatment. This factor can help the enterprise understand the safety status and performance of the waste gas emission process, and can also provide a basis for the enterprise to improve the waste gas emission process technology.

[0033] The mathematical model used by the exhaust gas emission safety factor calculation unit is: , represents the exhaust gas emission safety factor of the enterprise at the i-th monitoring time, represents the exhaust gas emissions of the enterprise at the i-th monitoring time, It represents the waste gas recovery scope of the pollution source of the enterprise at the i-th monitoring time, It represents the qualified rate of waste gas storage sealing of the enterprise at the i-th monitoring time, represents the condensation recovery efficiency of the enterprise at the i-th monitoring time, represents the exhaust gas combustion efficiency of the enterprise at the i-th monitoring time, represents the time difference between the i-th monitoring time and the i-1-th monitoring time, Represents other influencing factors of exhaust emission safety factor.

[0034] The enterprise environmental protection abnormality index calculation module is used to receive data transmitted by the data analysis module, integrate and calculate the data transmitted by the data analysis module through the enterprise environmental protection abnormality index calculation unit to obtain the enterprise environmental protection abnormality index, and transmit the enterprise environmental protection abnormality index to the early warning module.

[0035] The mathematical model used by the enterprise environmental protection abnormality index calculation unit is: , represents the environmental protection abnormality index of the enterprise at the i-th monitoring time, represents the equipment safety factor of the enterprise at the i-th monitoring time, represents the enterprise's wastewater discharge safety factor at the i-th monitoring time, represents the exhaust gas emission safety factor of the enterprise at the i-th monitoring time, Indicates the preset value of the enterprise's equipment safety factor. Indicates the preset value of the enterprise's wastewater discharge safety factor, Indicates the preset value of the enterprise's exhaust emission safety factor. Represents other influencing factors of the enterprise's environmental protection abnormality index.

[0036] The early warning module is used to receive the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module, evaluate and judge the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module, and issue an early warning based on the judgment result.

[0037] The early warning module evaluates and judges the enterprise environmental protection abnormality index by comparing the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module with the preset value of the enterprise environmental protection abnormality index. When , it means that the enterprise environmental protection abnormality index at the i-th monitoring time is less than the preset value of the enterprise environmental protection abnormality index, indicating that there is no abnormality in the enterprise environmental protection monitoring process at the i-th monitoring time, then the enterprise environmental protection monitoring is maintained. When , it means that the enterprise environmental protection abnormality index at the i-th monitoring time is greater than the preset value of the enterprise environmental protection abnormality index, indicating that the enterprise environmental protection monitoring process at the i-th monitoring time is abnormal, and an early warning signal is issued to relevant personnel.

[0038] The human-computer interaction module realizes real-time interaction between users and the system through dynamic interaction technology. Users can interact with the system through gestures and touch, and provides historical data query and visualization functions. Through data visualization technology, the company's environmental monitoring historical data is presented to users in the form of charts and images.

[0039] The present invention divides the environmental monitoring process data of the target enterprise into various monitoring times and numbers them through a monitoring time division module, collects the environmental monitoring process data of the target enterprise within each monitoring time through a data acquisition module, analyzes the data transmitted by the acquisition module through a data analysis module, integrates and calculates the data transmitted by the data analysis module through an enterprise environmental anomaly index calculation module to obtain the enterprise environmental anomaly index, evaluates and judges the enterprise environmental anomaly index transmitted by the enterprise environmental anomaly index calculation module through an early warning module, and issues an early warning based on the judgment result, and performs human-computer interaction through the dynamic interaction technology and data visualization technology of the human-computer interaction module.

[0040] The present invention monitors and analyzes real-time data through artificial intelligence technology, can quickly and accurately discover abnormal data, and improve monitoring efficiency. Through automated monitoring and analysis, the cost of manual intervention and manual monitoring can be reduced, and the accuracy and reliability of monitoring can also be improved. Through monitoring and analysis of equipment operating status and emission data, potential safety hazards and situations that do not meet environmental protection standards can be discovered and resolved in a timely manner, thereby improving the environmental performance of the enterprise. Through analysis and prediction of equipment operating status and performance, intelligent suggestions and improvement measures can be provided to the enterprise, and the operating status and efficiency of the equipment can be optimized. Through the setting of the early warning module and the application of abnormal detection technology, potential safety hazards and attack behaviors can be discovered and resolved in a timely manner, and the security of the system can be enhanced. Through the visualization of historical data, it can help enterprises better understand the operating status and environmental protection of the equipment, and improve the accuracy and scientificity of decision-making.

[0041] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0042] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A survey remote monitoring system based on artificial intelligence, The invention comprises, wherein: Monitoring time division module: used to obtain the environmental monitoring process data of the target enterprise, divide the environmental monitoring process data of the target enterprise into various monitoring times by equal time division, and mark each monitoring time as 1, 2...n in sequence; Data collection module: used to collect the environmental monitoring process data of the target enterprise at each monitoring time, through cameras and intelligent sensors, and transmit the collected data to the data analysis module after data cleaning and preprocessing; Data analysis module: including equipment safety factor calculation unit, wastewater discharge safety factor calculation unit, and exhaust gas discharge safety factor calculation unit, which are used to receive data transmitted by the data acquisition module, analyze the data transmitted by the acquisition module through the mathematical model corresponding to each calculation unit, and transmit the analysis results to the enterprise environmental protection abnormality index calculation module; Enterprise environmental protection abnormality index calculation module: used to receive data transmitted by the data analysis module, integrate and calculate the data transmitted by the data analysis module through the enterprise environmental protection abnormality index calculation unit to obtain the enterprise environmental protection abnormality index, and transmit the enterprise environmental protection abnormality index to the early warning module; Early warning module: used to receive the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module, evaluate and judge the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module, and issue an early warning based on the judgment result; Human-computer interaction module: Through dynamic interaction technology, real-time interaction between users and the system is realized. Users can interact with the system through gestures and touch, and provide historical data query and visualization functions. Through data visualization technology, the company's environmental monitoring historical data is presented to users in the form of charts and images.

2. The survey remote monitoring system based on artificial intelligence according to claim 1, characterized in that: The data collection module is used to collect the total feed amount, material loss, material leakage, organic solvent recovery rate, equipment failure times, number of operators, number of equipment, rainwater and sewage diversion rate, sewage tank corrosion depth, emergency valve nominal pressure, emergency valve nominal diameter, waste gas emissions, pollution source waste gas recovery range, waste gas storage sealing qualified rate, condensation recovery efficiency and waste gas combustion efficiency of the enterprise at each monitoring time, marked as , , , , , , , , , , , , , , as well as , where i=1, 2…n, i represents the i-th detection time.

3. The survey remote monitoring system based on artificial intelligence according to claim 1, characterized in that: The mathematical model used by the equipment safety factor calculation unit is: , represents the equipment safety factor of the enterprise at the i-th monitoring time, represents the total feed amount of the enterprise at the i-th monitoring time, represents the material loss of the enterprise at the i-th monitoring time, represents the material leakage of the enterprise at the i-th monitoring time, represents the organic solvent recovery rate of the enterprise at the i-th monitoring time, represents the number of equipment failures of the enterprise at the i-th monitoring time, represents the number of operators in the enterprise at the i-th monitoring time, represents the number of devices of the enterprise at the i-th monitoring time, Indicates other factors affecting the equipment safety factor.

4. The survey remote monitoring system based on artificial intelligence according to claim 1, characterized in that: The mathematical model used by the wastewater discharge safety factor calculation unit is: , represents the enterprise's wastewater discharge safety factor at the i-th monitoring time, represents the rainwater and sewage diversion rate of the enterprise at the i-th monitoring time, represents the corrosion depth of the sewage pool of the enterprise at the i-th monitoring time, represents the nominal pressure of the emergency valve of the enterprise at the i-th monitoring time, represents the nominal diameter of the emergency valve of the enterprise at the i-th monitoring time, represents the time difference between the i-th monitoring time and the i-1-th monitoring time, Represents other influencing factors of wastewater discharge safety factor.

5. The survey remote monitoring system based on artificial intelligence according to claim 1, characterized in that: The mathematical model used by the exhaust gas emission safety factor calculation unit is: , represents the exhaust gas emission safety factor of the enterprise at the i-th monitoring time, represents the exhaust gas emissions of the enterprise at the i-th monitoring time, It represents the waste gas recovery scope of the pollution source of the enterprise at the i-th monitoring time, It represents the qualified rate of waste gas storage sealing of the enterprise at the i-th monitoring time, represents the condensation recovery efficiency of the enterprise at the i-th monitoring time, represents the exhaust gas combustion efficiency of the enterprise at the i-th monitoring time, represents the time difference between the i-th monitoring time and the i-1-th monitoring time, Represents other influencing factors of exhaust emission safety factor.

6. The survey remote monitoring system based on artificial intelligence according to claim 1, characterized in that: The mathematical model used by the enterprise environmental protection abnormality index calculation unit is: , represents the environmental abnormality index of the enterprise at the i-th monitoring time, represents the equipment safety factor of the enterprise at the i-th monitoring time, represents the enterprise's wastewater discharge safety factor at the i-th monitoring time, represents the exhaust gas emission safety factor of the enterprise at the i-th monitoring time, Indicates the preset value of the enterprise's equipment safety factor. Indicates the preset value of the enterprise's wastewater discharge safety factor, Indicates the preset value of the enterprise's exhaust emission safety factor. Represents other influencing factors of the enterprise's environmental protection abnormality index.

7. The survey remote monitoring system based on artificial intelligence according to claim 1, characterized in that: The early warning module evaluates and judges the enterprise environmental protection abnormality index by comparing the enterprise environmental protection abnormality index transmitted by the enterprise environmental protection abnormality index calculation module with the preset value of the enterprise environmental protection abnormality index. When , it means that the enterprise environmental protection abnormality index at the i-th monitoring time is less than the preset value of the enterprise environmental protection abnormality index, indicating that there is no abnormality in the enterprise environmental protection monitoring process at the i-th monitoring time, then the enterprise environmental protection monitoring is maintained. When , it means that the enterprise environmental protection abnormality index at the i-th monitoring time is greater than the preset value of the enterprise environmental protection abnormality index, indicating that the enterprise environmental protection monitoring process at the i-th monitoring time is abnormal, and an early warning signal is issued to relevant personnel.

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