Bulk cargo port area environment monitoring system and method based on multi-sensor fusion algorithm

Abstract

The invention provides a bulk cargo port area environment monitoring system and method based on a multi-sensor fusion algorithm. The system comprises sensor monitoring, data acquisition, analysis, fusion and evaluation modules. The sensor network monitors the environment of a harbor area in real time, the data acquisition module summarizes data, and the analysis module calculates flying dust and noise pollution indexes, an air pollution comprehensive index and a water resource utilization efficiency index. The data fusion module integrates the indexes to obtain a comprehensive monitoring index, and the evaluation module compares the comprehensive index with a preset threshold value and judges environment abnormity. The system can comprehensively and efficiently monitor the environment of the harbor district in real time, and provides powerful support for environment management and decision making.

Description

Technical Field

[0001] This application belongs to the field of environmental monitoring, and particularly relates to an environmental monitoring system and method for bulk cargo port areas based on a multi-sensor fusion algorithm. Background Technique

[0002] During the operation of bulk cargo port areas, environmental monitoring is an important link to ensure the safety of port operations, protect the surrounding ecological environment, and comply with relevant environmental protection regulations. Traditional environmental monitoring methods often rely on manual sampling and fixed-point monitoring, which is not only inefficient but also difficult to comprehensively and real-time reflect the environmental conditions of the port area.

[0003] As a complex industrial environment, the environmental monitoring of bulk cargo port areas involves multiple aspects, including air quality, noise pollution, water resource utilization, etc. Air quality monitoring mainly focuses on dust pollution (such as PM10 concentration) and air pollutant concentrations (such as sulfur dioxide, nitrogen oxides, etc.). These pollutants not only affect the health of port operation personnel but also may have long-term impacts on the surrounding environment. Noise pollution mainly comes from port operation machinery, transportation vehicles, etc. Excessive noise levels not only affect the health of staff but also may interfere with the normal life of surrounding residents. Summary of the Invention

[0004] The purpose of this application is to overcome the defects in the above-mentioned prior art and provide an environmental monitoring system and method for bulk cargo port areas based on a multi-sensor fusion algorithm.

[0005] This application provides an environmental monitoring system for bulk cargo port areas based on a multi-sensor fusion algorithm, including: a sensor monitoring network, a data acquisition module, a data analysis module, a data fusion module, and a data evaluation module.

[0006] The sensor monitoring network monitors the environmental data of the port area in real time; The data acquisition module acquires the environmental data monitored by the sensor monitoring network; The data analysis module obtains the surface area of the bulk cargo yard in the port area, the average wind speed, humidity data, and the concentration of PM10 in the air in the environmental data, and calculates the dust pollution index; obtains the port operation noise, the average traffic noise in the port area, and the average background noise in the port area in the environmental data, and calculates the noise pollution index; obtains the historical data of air pollutant concentrations, obtains the air pollutant concentration data in the environmental data, and calculates the air pollution index for each pollutant; calculates the comprehensive air pollution index based on the air pollution index; obtains the water consumption, cargo throughput, rainwater collection volume, and recycled wastewater treatment volume in the environmental data, calculates the rainwater collection utilization rate and the recycled wastewater treatment rate respectively, and obtains the water resource utilization efficiency index based on the rainwater collection utilization rate and the recycled wastewater treatment rate; The data fusion module calculates a comprehensive monitoring index based on the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index; The evaluation module compares the comprehensive detection index with a preset threshold and determines environmental monitoring anomalies based on the comparison result.

[0007] Optionally, the sensor monitoring network includes: a wind speed sensor, a humidity sensor, a PM10 sensor, a noise sensor, an air quality sensor, and a water volume sensor.

[0008] Optionally, a drone is used to obtain the surface area of the bulk cargo port area yard.

[0009] Optionally, a comprehensive monitoring index is calculated based on the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index. The expression is: ; where EMCI represents the comprehensive monitoring index, DPI represents the dust pollution index, WUEI represents the water resource utilization efficiency index, NPI represents the noise pollution index, and AQCI represents the comprehensive air pollution index.

[0010] Optionally, the comprehensive detection index is compared with a preset threshold, and environmental monitoring anomalies are determined based on the comparison result. The expression is: EPC = exp(EMCI - EMCI 0 ) where if EPC < 1, the environmental monitoring is abnormal.

[0011] This application also provides a method for environmental monitoring of a bulk cargo port area based on a multi-sensor fusion algorithm, including: Real-time monitoring of port area environmental data; Obtaining the surface area, average wind speed, humidity data, and concentration of PM10 in the air of the bulk cargo port area yard in the environmental data, and calculating the dust pollution index; Obtaining the port operation noise, average port traffic noise, and average port background noise in the environmental data, and calculating the noise pollution index; Obtaining the historical data of air pollutant concentrations, obtaining the air pollutant concentration data in the environmental data, and calculating the air pollution index for each pollutant; calculating the comprehensive air pollution index based on the air pollution index; Obtaining the water consumption, cargo throughput, rainwater collection volume, and reused wastewater treatment volume in the environmental data, respectively calculating the rainwater collection utilization rate and the reused wastewater treatment rate, and obtaining the water resource utilization efficiency index based on the rainwater collection utilization rate and the reused wastewater treatment rate; Calculate a comprehensive monitoring index based on the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index; Compare the comprehensive detection index with a preset threshold, and determine environmental monitoring anomalies based on the comparison result.

[0012] Optionally, sensors for real-time monitoring of port area environmental data include: wind speed sensors, humidity sensors, PM10 sensors, noise sensors, air quality sensors, and water volume sensors.

[0013] Optionally, it is characterized in that a drone is used to obtain the surface area of the bulk cargo port yard.

[0014] Optionally, calculate a comprehensive monitoring index based on the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index. The expression is: ; where EMCI represents the comprehensive monitoring index, DPI represents the dust pollution index, WUEI represents the water resource utilization efficiency index, NPI represents the noise pollution index, and AQCI represents the comprehensive air pollution index.

[0015] Optionally, compare the comprehensive detection index with a preset threshold, and determine environmental monitoring anomalies based on the comparison result. The expression is: EPC = exp(EMCI - EMCI 0 ) where if EPC < 1, the environmental monitoring is abnormal.

[0016] The beneficial effects of this application are: This application provides an environmental monitoring system for bulk cargo port areas based on a multi-sensor fusion algorithm, including: a sensor monitoring network, a data acquisition module, a data analysis module, a data fusion module, and a data evaluation module. The sensor monitoring network monitors the environmental data of the port area in real time; the data acquisition module acquires the environmental data monitored by the sensor monitoring network; the data analysis module obtains the surface area, average wind speed, humidity data, and PM10 concentration in the air of the bulk cargo yard in the environmental data, and calculates the dust pollution index; obtains the port operation noise, average port traffic noise, and average port background noise in the environmental data, and calculates the noise pollution index; obtains the historical data of air pollutant concentrations, obtains the air pollutant concentration data in the environmental data, and calculates the air pollution index for each pollutant; calculates the comprehensive air pollution index based on the air pollution index; obtains the water consumption, cargo throughput, rainwater collection volume, and recycled wastewater treatment volume in the environmental data, calculates the rainwater collection and utilization rate and the recycled wastewater treatment rate respectively, and obtains the water resource utilization efficiency index based on the rainwater collection and utilization rate and the recycled wastewater treatment rate; the data fusion module calculates the comprehensive monitoring index based on the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index; the evaluation module compares the comprehensive detection index with a preset threshold, and determines the environmental monitoring abnormal situation according to the comparison result. The environmental monitoring system for bulk cargo port areas based on the multi-sensor fusion algorithm of this application can realize comprehensive, real-time, and efficient monitoring of the port area environment, and provide strong support for the environmental management and decision-making of the port area. Brief Description of the Drawings

[0017] Figure 1 It is a schematic diagram of an environmental monitoring system for bulk cargo port areas based on a multi-sensor fusion algorithm. Detailed Embodiments

[0018] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, the provided embodiments are for the purpose of enabling a more thorough understanding of the present disclosure and for being able to fully convey the scope of the present disclosure to those skilled in the art.

[0019] Please refer to Figure 1 As shown, this application provides an environmental monitoring system for bulk cargo port areas based on a multi-sensor fusion algorithm, which is characterized by including: a sensor monitoring network, a data acquisition module, a data analysis module, a data fusion module, and a data evaluation module.

[0020] The sensor monitoring network monitors the environmental data of the port area in real time; The sensor monitoring network in the bulk cargo port area monitors the environmental data in the bulk cargo port area in real time through various types of sensors and transmits it to the data acquisition module. The various types of sensors include, but are not limited to, noise sensors, meteorological sensors, gas sensors, etc.

[0021] The data acquisition module acquires the environmental data monitored by the sensor monitoring network. Through sensor technology, environmental data is acquired from the sensor monitoring network in the bulk cargo port area to obtain the environmental data for the intelligent environmental monitoring of the bulk cargo port area and transmitted to the data analysis module.

[0022] The environmental data includes: dust pollution data of port area goods, noise data, air quality data, and water resource utilization data. Among them, the dust pollution data includes the surface area of the port area yard, the average wind speed of the yard, the humidity of the surface and internal of the goods yard, and the concentration of PM10 in the yard air. The port area noise data includes the operation noise of each port area, the operation duration of the mechanical monitoring cycle, the number of operating machines, the average traffic noise in the port area, and the average background noise in the port area. The port area air quality data includes air pollutant concentration data, concentration limits, and air pollution index limits. The port area water resource utilization data includes the water consumption during the port area monitoring cycle, the port area cargo throughput, the rainwater collection volume, the total rainfall, the recycled amount of treated wastewater, and the total wastewater volume.

[0023] The data analysis module obtains the surface area, average wind speed, humidity data, and the concentration of PM10 in the air of the bulk cargo port area yard in the environmental data, and calculates the dust pollution index; obtains the port area operation noise, the average traffic noise in the port area, and the average background noise in the port area in the environmental data, and calculates the noise pollution index; obtains the historical data of air pollutant concentrations, obtains the air pollutant concentration data in the environmental data, and calculates the air pollution index for each pollutant; calculates the comprehensive air pollution index based on the air pollution index; obtains the water consumption, cargo throughput, rainwater collection volume, and recycled amount of treated wastewater in the environmental data, calculates the rainwater collection utilization rate and the recycled rate of treated wastewater respectively, and obtains the water resource utilization efficiency index based on the rainwater collection utilization rate and the recycled rate of treated wastewater; The data analysis module includes a port area goods dust pollution index analysis unit, a port area noise impact index analysis unit, a port area comprehensive air pollution index analysis unit, and a port area water resource utilization rate index analysis unit. Transmits the environmental monitoring indexes of the bulk cargo port area obtained by each analysis unit to the data fusion module.

[0024] The port area cargo dust pollution index analysis unit is used to analyze the port area cargo dust pollution data, the port area noise impact index analysis unit is used to analyze the port area noise data, the port area air pollution comprehensive index analysis unit is used to analyze the port area air quality data, and the port area water resource utilization rate index analysis unit is used to analyze the port area water resource utilization data.

[0025] Port area cargo dust pollution index analysis unit: Use drones to obtain the surface area of the bulk cargo port yard .

[0026] By setting wind speed sensors at multiple different positions in the cargo yard, the wind speed is monitored in real time to obtain the average wind speed of the cargo yard .

[0027] Obtain the humidity on the surface and inside of the cargo yard through humidity sensors and the internal humidity .

[0028] Obtain the concentration of PM10 in the air of the cargo yard through PM10 sensors .

[0029] Finally, through big data analysis technology, obtain the port area cargo dust pollution monitoring index DPI, expression: ; where, represents the maximum possible humidity of air at the same temperature, and 10 is added to adjust the value of the molecule.

[0030] Real-time monitoring of wind speed changes helps to understand the impact of wind speed on dust emissions; the size of the yard surface area helps to evaluate the scope and potential impact of dust pollution; the cargo humidity can understand its inhibitory effect on dust emissions; the PM10 concentration directly reflects the degree of dust pollution. Therefore, through the multi-sensor fusion algorithm, real-time monitoring and early warning of DPI can be achieved.

[0031] Port area noise impact index analysis unit: By setting k noise sensors within the specified operation area of the machinery, the average noise level of each operation machinery in the port area is obtained by averaging the k noises , obtaining the port area operation noise , expression: ; where, m represents the number of operation machineries in the port area, represents the operation duration of the i-th machinery monitoring period.

[0032] The average traffic noise Ntra in the port area is obtained through the noise monitoring stations set in the port traffic area. The expression is: Ntra = S(Nk1) / k1 Where, k1 represents the number of noise sampling points in the port traffic area, and S(Nk1) represents the sum of the noise levels of k1 noise sampling points in the port traffic area.

[0033] The average background noise Nbac in the port area is obtained through the noise monitoring stations set in the non-operating area and non-traffic area. The expression is: Nbac = S(Nk2) / k2 Where, k2 represents the number of noise sampling points in the non-operating area and non-traffic area of the port area, and S(Nk2) represents the sum of the noise levels of k2 noise sampling points in the non-operating area and non-traffic area of the port area.

[0034] The operating area refers to the area where all mechanical operations in the port area are specified, and the traffic area refers to all route areas for the machinery from entering the port area to the operating area; finally, through big data analysis technology, the port noise pollution monitoring index NPI is obtained. The expression is: ; Where, represents the maximum noise value specified for the bulk cargo port area.

[0035] By monitoring the port traffic noise, port operation noise and port background noise, and using the fusion algorithm for integration and analysis, it can more accurately reflect the noise situation in the port area. In the intelligent environmental noise monitoring of the port area, it helps to timely detect and control the noise pollution sources and reduce the impact of noise on the surrounding environment and personnel.

[0036] Port air pollution comprehensive index analysis unit: Based on the historical data of air pollutant concentrations in the bulk cargo port area, the air pollutant concentration data CD of the target bulk cargo port area is determined. , N represents the number of types of air pollutants in the target bulk cargo port area. represents the concentration of the i-th type of air pollutant. After standardization processing, the concentration units are consistent. The air pollutant concentrations such as SO2 concentration, NO2 concentration, O3 concentration, etc. are obtained, and the air pollution index API(Ci) of each pollutant is obtained. The expression is: ; Where, represents the concentration limit greater than or equal to C. represents the concentration limit less than or equal to C. I_h represents the air pollution index limit corresponding to and I_l represents the air pollution index limit corresponding to The air pollution index limit; finally, through big data analysis technology, the comprehensive air pollution monitoring index AQCI of the port area is obtained, and the expression is: ; Among them, represents the weight of the air pollution index of the i-th air pollutant. For example, 、 etc.

[0037] Through various air quality monitoring data such as sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) in the port area, the multi-sensor fusion algorithm can comprehensively and accurately reflect the air quality status of the port area and promptly detect abnormal air quality.

[0038] Port area water resource utilization rate index analysis unit: During the monitoring period, the water consumption in the port area during the monitoring period is collected in real time through a water volume sensor .

[0039] The cargo throughput of the port area is obtained through the port area logistics system .

[0040] The rainwater collection volume cv and the reused wastewater volume wv are collected through the sensors of the port area rainwater collection system and the wastewater treatment system, and the rainwater collection utilization rate of the port area and the wastewater treatment reuse rate of the port area are obtained respectively. =cv / CV, where CV represents the total rainfall obtained according to meteorological data. =wv / WV, where WV represents the total wastewater volume obtained through the wastewater discharge records of the port area.

[0041] Through big data analysis technology, the water resource utilization efficiency monitoring index WUEI of the port area is obtained, and the expression is: ; When problems such as abnormal increase in water consumption in the port area, decrease in rainwater collection utilization rate, or insufficient wastewater treatment reuse rate are detected, the system can automatically trigger the early warning mechanism.

[0042] The data fusion module calculates the comprehensive monitoring index according to the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index; Through the multi-sensor fusion algorithm, the monitoring indexes of the environment of the bulk cargo port area obtained by the data analysis module are fused, which helps to improve the accuracy and reliability of the data, realize the precise identification and positioning of environmental pollution sources, and transmit the fusion result to the integrated data evaluation module of the bulk cargo port area environment. The fusion model is as follows, and the expression is: ; Among them, EMCI represents the comprehensive index of intelligent environmental monitoring in the bulk cargo port area, DPI represents the monitoring index of cargo dust pollution in the port area, WUEI represents the monitoring index of water resource utilization efficiency in the port area, NPI represents the monitoring index of noise pollution in the port area, and AQCI represents the comprehensive monitoring index of air pollution in the port area.

[0043] The evaluation module compares the comprehensive detection index with the preset threshold, and determines the abnormal situation of environmental monitoring according to the comparison result.

[0044] Compare the comprehensive index of intelligent environmental monitoring in the bulk cargo port area obtained by the fusion module with the threshold. When the comparison result is less than the set threshold, then compare each monitoring index of the environment in the bulk cargo port area obtained by the data analysis module with the corresponding threshold respectively. According to the abnormal comparison result, automatically send a warning signal to the human-computer interaction module of intelligent environmental monitoring in the bulk cargo port area.

[0045] The comprehensive index of intelligent environmental monitoring in the bulk cargo port area is monitored in real time through the multi-sensor fusion algorithm. Its monitoring model, expression: EPC = exp(EMCI - EMCI 0 ) Among them, EMCI 0 represents the preset threshold of the comprehensive monitoring index, EPC represents the pollution coefficient of the comprehensive index of intelligent environmental monitoring in the bulk cargo port area. If EPC < 1, it indicates that the intelligent environmental monitoring in the bulk cargo port area is abnormal, and an alarm signal is automatically sent to the human-computer interaction module of intelligent environmental monitoring in the bulk cargo port area; otherwise, it indicates normal.

[0046] Based on the warning signal, if the monitoring index DPI of cargo dust pollution in the port area is greater than the threshold DPI0, it indicates that the cargo dust pollution in the bulk cargo port area is abnormal, otherwise it indicates normal.

[0047] If the monitoring index NPI of noise pollution in the port area is greater than the threshold NPI0, it indicates that the noise pollution in the bulk cargo port area is abnormal, otherwise it indicates normal.

[0048] If the comprehensive monitoring index AQCI of air pollution in the port area is greater than the threshold AQCI0, it indicates that the air pollution in the bulk cargo port area is abnormal, otherwise it indicates normal.

[0049] If the monitoring index WUEI of water resource utilization efficiency in the port area is less than the threshold WUEI0, it indicates that the water resource utilization efficiency in the bulk cargo port area is abnormal, otherwise it indicates normal.

[0050] Human-computer interaction module for intelligent environmental monitoring in the bulk cargo port area: It is used to receive the warning signal of the environmental integration data evaluation module in the bulk cargo port area for human-computer interaction, prompt the administrator to take measures in time, and accurately identify and locate the environmental pollution sources according to the warning signal. For example, according to the changes in wind speed and the surface area of the yard, the frequency and intensity of sprinkler dust suppression can be adjusted; optimize the number of operating machines and operating time to reduce the noise level; process and analyze the data from each sensor in real time to detect abnormal air quality in time; optimize the allocation of port water resource monitoring resources to improve the monitoring efficiency.

[0051] This application also provides a method for monitoring the environment of a bulk cargo port area based on a multi-sensor fusion algorithm, including: Monitor the environmental data of the port area in real time; Obtain the surface area, average wind speed, humidity data of the yard in the bulk cargo port area and the concentration of PM10 in the air in the environmental data, and calculate the dust pollution index; Obtain the port operation noise, average port traffic noise and average port background noise in the environmental data, and calculate the noise pollution index; Obtain the historical data of air pollutant concentration, obtain the air pollutant concentration data in the environmental data, calculate the air pollution index of each pollutant; calculate the comprehensive air pollution index according to the air pollution index; Obtain the water consumption, cargo throughput, rainwater collection volume, and recycled wastewater treatment volume in the environmental data, calculate the rainwater collection and utilization rate and the recycled wastewater treatment rate respectively, and obtain the water resource utilization efficiency index according to the rainwater collection and utilization rate and the recycled wastewater treatment rate; Calculate the comprehensive monitoring index according to the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index; Compare the comprehensive detection index with a preset threshold, and determine the abnormal situation of environmental monitoring according to the comparison result.

[0052] The above are only the preferred embodiments of this application and are not used to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. A bulk cargo port environment monitoring system based on multi-sensor fusion algorithm, characterized in that: include: Sensor monitoring network, data acquisition module, data analysis module, data fusion module and data evaluation module; The sensor monitoring network monitors the port area environmental data in real time; The data collection module collects the environmental data monitored by the sensor monitoring network; The data analysis module obtains the surface area, average wind speed, humidity data and PM10 concentration in the air of the bulk cargo port yard in the environmental data, and calculates the dust pollution index; obtains the port operation noise, the port average traffic noise and the port average background noise in the environmental data, and calculates the noise pollution index; The historical data of air pollutant concentrations obtained are obtained, the air pollutant concentration data in the environmental data are obtained, and the air pollution index of each pollutant is calculated; the comprehensive air pollution index is calculated based on the air pollution index; the water consumption, cargo throughput, rainwater collection volume, and wastewater treatment and reuse volume in the environmental data are obtained, and the rainwater collection and reuse rate and the wastewater treatment and reuse rate are calculated respectively, and the water resource utilization efficiency index is obtained based on the rainwater collection and reuse rate and the wastewater treatment and reuse rate; The data fusion module calculates a comprehensive monitoring index based on the dust pollution index, the noise pollution index, the comprehensive air pollution index, and the water resource utilization efficiency index; The evaluation module compares the comprehensive detection index with a preset threshold value and determines the abnormality of environmental monitoring based on the comparison result.

2. A bulk cargo port environment monitoring system based on multi-sensor fusion algorithm according to claim 1, characterized in that: The sensor monitoring network includes: a wind speed sensor, a humidity sensor, a PM10 sensor, a noise sensor, an air quality sensor and a water volume sensor.

3. A bulk cargo port environment monitoring system based on multi-sensor fusion algorithm according to claim 1, characterized in that: The surface area of ​​the bulk cargo port yard is obtained by using drones.

4. A bulk cargo port environment monitoring system based on multi-sensor fusion algorithm according to claim 1, characterized in that: According to the dust pollution index, noise pollution index, air pollution comprehensive index and water resource utilization efficiency index, a comprehensive monitoring index is calculated, and the expression is: ; Among them, EMCI represents the comprehensive monitoring index, DPI represents the dust pollution index, WUEI represents the water resource utilization efficiency index, NPI represents the noise pollution index, and AQCI represents the comprehensive air pollution index.

5. The bulk cargo port environment monitoring system based on multi-sensor fusion algorithm according to claim 1 is characterized in that: Compare the comprehensive detection index with the preset threshold, and determine the abnormal situation of environmental monitoring based on the comparison result. The expression is: EPC=exp(EMCI-EMCI0) Among them, EMCI0 represents the preset threshold of the comprehensive monitoring index. If EPC < 1, the environmental monitoring is abnormal.

6. A bulk cargo port environment monitoring method based on multi-sensor fusion algorithm, characterized in that: include: Real-time monitoring of port area environmental data; Obtain the surface area, average wind speed, humidity data and PM10 concentration in the air of the bulk cargo port yard in the environmental data, and calculate the dust pollution index; Obtaining the port area operation noise, the port area average traffic noise and the port area average background noise in the environmental data, and calculating the noise pollution index; The obtained historical data of air pollutant concentrations are used to obtain the air pollutant concentration data in the environmental data, and the air pollution index of each pollutant is calculated; and the comprehensive air pollution index is calculated based on the air pollution index; Obtaining water consumption, cargo throughput, rainwater collection, and wastewater treatment and reuse in the environmental data, respectively calculating the rainwater collection and reuse rate and the wastewater treatment and reuse rate, and obtaining a water resource utilization efficiency index based on the rainwater collection and reuse rate and the wastewater treatment and reuse rate; A comprehensive monitoring index is calculated based on the dust pollution index, noise pollution index, comprehensive air pollution index, and water resource utilization efficiency index; The comprehensive detection index is compared with the preset threshold, and the environmental monitoring abnormality is determined based on the comparison result.

7. A bulk cargo port environment monitoring method based on a multi-sensor fusion algorithm according to claim 6, characterized in that: Sensors for real-time monitoring of port environmental data include: wind speed sensor, humidity sensor, PM10 sensor, noise sensor, air quality sensor and water volume sensor.

8. A bulk cargo port environment monitoring method based on a multi-sensor fusion algorithm according to claim 6, characterized in that: The surface area of ​​the bulk cargo port yard is obtained by using drones.

9. The method for monitoring the environment of bulk cargo ports based on a multi-sensor fusion algorithm according to claim 6, characterized in that: According to the dust pollution index, noise pollution index, air pollution comprehensive index and water resource utilization efficiency index, a comprehensive monitoring index is calculated, and the expression is: ; Among them, EMCI represents the comprehensive monitoring index, DPI represents the dust pollution index, WUEI represents the water resource utilization efficiency index, NPI represents the noise pollution index, and AQCI represents the comprehensive air pollution index.

10. A bulk cargo port environment monitoring method based on multi-sensor fusion algorithm according to claim 6, characterized in that: Compare the comprehensive detection index with the preset threshold, and determine the abnormal situation of environmental monitoring based on the comparison result. The expression is: EPC=exp(EMCI-EMCI0) Among them, if EPC < 1, the environmental monitoring is abnormal.

Images