A multi-source heterogeneous data fusion processing method and system based on the Internet of Things

Abstract

The present application discloses a multi-source heterogeneous data fusion processing method and system based on the Internet of Things. The method includes obtaining the historical usage data of the circulating water pump of the nuclear power plant, analyzing the influence of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyzing the influence of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1; detecting the air flow and rainfall around the circulating water pump to obtain environmental meteorological data, analyzing the influence of the environmental meteorological around the circulating water pump on the water inhaled by the circulating water pump based on the environmental meteorological data to obtain the environmental impact weight ratio p2; detecting the animals at the inlet of the circulating water pump to obtain animal data, and analyzing the influence of the animals at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the animal data to obtain the animal impact coefficient CTH. The present application has the effect of improving the accuracy of fusion processing of multi-source heterogeneous data.

Description

Technical Field

[0001] The present application relates to the technical field of data fusion, and in particular to a multi-source heterogeneous data fusion processing method and system based on the Internet of Things. Background Art

[0002] At present, nuclear power plants are experiencing a large number of aquatic organisms invading, causing the circulating water pump to trip and triggering the reactor to automatically shut down. Environmental changes and severe weather also bring a series of threats to the safety of nuclear power plants. Multi-source heterogeneous data fusion refers to the integration and fusion of data from different data sources, different structures, and different types to extract more complete, accurate, and useful information. Therefore, it is very important to fuse and process the multi-source heterogeneous data of the circulating water pump of a nuclear power plant.

[0003] The existing multi-source heterogeneous data fusion processing system collects various data of the nuclear power plant's circulating water pump based on various sensors, and then analyzes whether the nuclear power plant's circulating water pump is dangerous based on the various data. However, when the existing multi-source heterogeneous data fusion processing system analyzes the impact of animals and plants around the circulating water pump on the blockage of the circulating water pump, it does not make real-time adjustments to the animal detection area and the plant detection area according to the surrounding environment of the circulating water pump. The accuracy of the multi-source heterogeneous data fusion processing of the nuclear power plant's circulating water pump is low, and there is room for improvement. Summary of the invention

[0004] In order to improve the accuracy of fusion processing of multi-source heterogeneous data, the present application provides a multi-source heterogeneous data fusion processing method and system based on the Internet of Things.

[0005] In the first aspect, the present application provides a multi-source heterogeneous data fusion processing method based on the Internet of Things, which adopts the following technical solutions:

[0006] A multi-source heterogeneous data fusion processing method based on the Internet of Things includes the following steps:

[0007] Obtain the historical usage data of the circulating water pump of the nuclear power plant, analyze the impact of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyze the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1;

[0008] The air flow and rainfall conditions around the circulating water pump are detected to obtain environmental meteorological data, and the influence of the environmental meteorological conditions around the circulating water pump on the water sucked by the circulating water pump is analyzed based on the environmental meteorological data to obtain the environmental impact weight ratio p2;

[0009] The animals at the inlet of the circulating water pump are detected to obtain animal data, and the influence of the animals at the inlet of the circulating water pump on the blockage failure of the circulating water pump is analyzed based on the animal data to obtain the animal influence coefficient CTH;

[0010] The plants at the inlet of the circulating water pump are detected to obtain plant data, and the influence of the plants at the inlet of the circulating water pump on the blockage of the circulating water pump is analyzed based on the plant data to obtain the plant influence coefficient DYR;

[0011] The man-made garbage at the inlet of the circulating water pump is detected to obtain the man-made garbage data, and based on the man-made garbage data, the influence of the man-made garbage at the inlet of the circulating water pump on the blockage failure of the circulating water pump is analyzed to obtain the man-made garbage influence coefficient EIL;

[0012] Based on the historical impact weight ratio p1, environmental impact weight ratio p2, animal impact coefficient CTH, plant impact coefficient DYR and human waste impact coefficient EIL, the disaster risk coefficient OST of the circulating water pump is obtained;

[0013] Based on the disaster risk coefficient OST, it is determined whether to issue a disaster risk alarm for the circulating water pump. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal, and the staff conducts inspections on the circulating water pump.

[0014] By adopting the above technical scheme, the historical use of the circulating water pump is analyzed to determine the impact of the circulating water pump blockage failure to obtain the historical use weight ratio p1, the impact of the surrounding meteorological environment of the circulating water pump on the circulating water pump suction water is analyzed to obtain the environmental impact weight ratio p2, the impact of animals at the inlet of the circulating water pump on the circulating water pump blockage failure is analyzed to obtain the animal impact coefficient CTH, the impact of plants at the inlet of the circulating water pump on the circulating water pump blockage failure is analyzed to obtain the plant impact coefficient DYR, the impact of man-made garbage at the inlet of the circulating water pump on the circulating water pump blockage failure is analyzed to obtain the man-made garbage impact coefficient EIL, and the comprehensive historical impact weight ratio p 1. The environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL are used to obtain the disaster risk coefficient OST of the circulating water pump. Based on the disaster risk coefficient OST, it is determined whether to perform a disaster risk alarm operation on the circulating water pump. The historical use of the circulating water pump, the environmental meteorology around the circulating water pump, the impact of animals and plants at the inlet of the circulating water pump, and the impact of human garbage around the circulating water pump are integrated to determine whether the circulating water pump has a disaster risk. If there is a disaster risk, an alarm operation is performed, which improves the accuracy of the fusion processing of multi-source heterogeneous data and improves the operation safety of the circulating water pump.

[0015] Preferably, historical usage data of a circulating water pump of a nuclear power plant is obtained, wherein the historical usage data includes historical service life data AR, historical failure number data AC, and historical failure average degree data AE;

[0016] According to the historical fault frequency data AC and the historical fault average degree data AE, the historical fault correlation function AD = a1 × AC × AE is used to calculate and analyze the influence of historical fault conditions on the aging of the circulating water pump to obtain the historical fault aging coefficient AD, where a1 is a proportional factor and is greater than 0;

[0017] According to the historical service life data AR and the historical failure aging coefficient AD, the historical service correlation function q = a2 × AR + a3 × AD is used to calculate and analyze the influence of the historical service of the circulating water pump on the aging of the circulating water pump to obtain the historical service aging influence coefficient q, where a2 and a3 are proportional factors and are both greater than 0;

[0018] Based on the historical usage aging influence coefficient q, the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump is analyzed to obtain the historical usage weight ratio p1, among which the historical usage weight ratio p1 is positively correlated with the historical usage aging influence coefficient q.

[0019] By adopting the above technical scheme, the historical fault aging coefficient AD is obtained according to the historical fault number data AC and the historical fault average degree data AE, thereby improving the detection accuracy of the influence of the historical fault conditions of the circulating water pump on the aging of the circulating water pump, and the historical use aging influence coefficient q is obtained according to the historical service life data AR and the historical fault aging coefficient AD, thereby improving the detection accuracy of the influence of the historical use of the circulating water pump on the aging of the circulating water pump, and the historical use weight ratio p1 is obtained based on the historical use aging influence coefficient q, thereby improving the detection accuracy of the influence of the historical use of the circulating water pump on the blockage of the circulating water pump, and improving the accuracy of fusion processing of multi-source heterogeneous data.

[0020] Preferably, the environmental meteorological data includes air flow data and rainfall data L;

[0021] The air environment around the circulating water pump is detected based on a meteorological instrument to obtain air flow data, wherein the air flow data includes wind force data f, wind direction data and wind speed data v;

[0022] Based on the wind direction data, the angle between the wind direction of the air around the circulating water pump and the suction at the inlet of the circulating water pump is analyzed to obtain the wind direction angle θ;

[0023] According to the wind force data f, wind speed data v and wind direction angle θ, the influence of air flow on the water suction of the circulating water pump is calculated and analyzed based on the air flow correlation function BC=b1×v×f×cosθ, and the air flow influence coefficient BC is obtained, where b1 is a proportional factor and is greater than 0;

[0024] Based on the meteorological instrument, whether there is rainfall around the circulating water pump is detected to obtain the rain impact coefficient BR. If there is no rainfall, the rain environmental impact coefficient BR is 0. If there is rainfall, the rainfall data L is obtained;

[0025] According to the rainfall data L, wind force data f, wind speed data v and wind direction angle θ, the rainfall correlation function BR = b2 × L × v × f × cosθ is used to calculate and analyze the influence of rainwater falling at the inlet of the circulating water pump on the water sucked by the circulating water pump when rainfall occurs in the circulating water pump, and the rainfall influence coefficient BR is obtained, where b2 is a proportional factor and is greater than 0;

[0026] The comprehensive air flow influence coefficient BC and the rainfall influence coefficient BR are calculated based on the meteorological environment correlation function p2=b3×BC+b4×BR to obtain the environmental impact weight ratio p2, where b3 and b4 are proportional factors and are both greater than 0.

[0027] By adopting the above technical scheme, the air flow influence coefficient BC is obtained by analyzing the influence of air flow on the water suction of the circulating water pump according to the wind force data f, wind direction data and wind speed data v. The rainfall influence coefficient BR is obtained by analyzing the influence of rainwater falling at the inlet of the circulating water pump on the water suction of the circulating water pump when it rains according to the rainfall data L, wind force data f, wind direction data and wind speed data v. The air flow influence coefficient BC and the rainfall influence coefficient BR are combined to obtain the environmental impact weight ratio, which integrates the influence of air flow around the circulating water pump and rainfall on the water suction of the circulating water pump, thereby improving the accuracy of fusion processing of multi-source heterogeneous data.

[0028] Preferably, the suction force data at the inlet of the circulating water pump is obtained, and the animal prediction area is obtained based on the suction force data at the inlet of the circulating water pump;

[0029] The animal prediction area is adjusted based on the air flow data to obtain the animal detection area, the direction of adjusting the animal prediction area is determined based on the wind direction data to obtain adjustment direction information, the degree of adjustment of the animal prediction area is determined based on the wind force data f and the wind speed data v to obtain adjustment degree information, and the animal prediction area is adjusted based on the adjustment direction information and the adjustment degree information to obtain the animal detection area;

[0030] Detecting animals in the animal detection area to obtain animal data, the animal data including animal volume data, animal type data, animal activity frequency data, and animal skin smoothness data;

[0031] Based on the animal volume data, the influence of the animal volume on the animal's escape after the animal mistakenly enters the suction pipe of the circulating water pump is analyzed to obtain the first escape coefficient CD, wherein the first escape coefficient CD is negatively correlated with the animal volume data;

[0032] The activity level of the animals that strayed into the water suction pipe of the circulating water pump is analyzed based on the animal type data and the animal activity frequency data to obtain activity level data, wherein the animal activity frequency data is positively correlated with the activity level data; and the second escape coefficient CE is obtained based on the activity level data, wherein the second escape coefficient CE is positively correlated with the activity level data;

[0033] The third escape coefficient CT is obtained by analyzing the data of animal skin smoothness, wherein the third escape coefficient CT is positively correlated with the data of animal skin smoothness;

[0034] Comprehensive first escape coefficient CD, second escape coefficient CE and third escape coefficient CT, based on animal blockage correlation function The animal impact coefficient CTH is calculated, where c1, c2, and c3 are proportional factors and are all greater than 0.

[0035] By adopting the above technical scheme, the animal prediction area is determined according to the suction size data at the inlet of the circulating water pump, and the animal prediction area is adjusted according to the air flow data to obtain the animal detection area, thereby improving the accuracy of animal detection in the case of animal blockage of the circulating water pump, and obtaining the first escape coefficient CD according to the animal volume data, the second escape coefficient CE according to the animal type data and the animal activity frequency data, and the third escape coefficient CT according to the animal skin smoothness data. The animal influence coefficient CTH is obtained by combining the first escape coefficient CD, the second escape coefficient CE and the third escape coefficient CT, thereby improving the detection accuracy of the impact of animal blockage of the circulating water pipe, integrating the impact of animal volume, animal type, animal activity frequency and animal skin smoothness on animal blockage of the circulating water pump, and improving the accuracy of fusion processing of multi-source heterogeneous data.

[0036] Preferably, the predicted plant area is obtained based on the suction force data at the inlet of the circulating water pump;

[0037] The temperature at the inlet of the circulating water pump is detected by the temperature sensor to obtain water area temperature data, and the water area temperature data is compared with the preset water area standard temperature data to obtain a water area temperature comparison result. Based on the water area temperature comparison result, the influence of the temperature at the inlet of the circulating water pump on the growth of plants at the inlet of the circulating water pump is analyzed to obtain the temperature influence coefficient DQ of plants, wherein the greater the difference in the water area temperature comparison result, the greater the temperature influence coefficient DQ of plants;

[0038] The water area illumination data is obtained by detecting the illumination at the inlet of the circulating water pump based on the illumination sensor, and the water area illumination data is compared with the preset water area standard illumination data to obtain the water area illumination comparison result. Based on the water area illumination comparison result, the influence of the illumination at the inlet of the circulating water pump on the growth of plants at the inlet of the circulating water pump is analyzed to obtain the illumination influence coefficient DG of plants, wherein the greater the difference in the water area illumination comparison result, the greater the illumination influence coefficient DG of plants;

[0039] According to the influence of temperature on plant coefficient DQ and light on plant coefficient DG, based on plant area correlation function The plant growth coefficient DY is calculated, wherein d1 and d2 are proportional factors and are both greater than 0. The plant prediction area is adjusted based on the plant growth coefficient DY to obtain the plant detection area. The larger the plant growth coefficient DY is, the higher the degree of adjustment of the plant prediction area is.

[0040] Detecting plants in a plant detection area to obtain plant data, wherein the plant data includes plant leaf area data, plant leaf thickness data, and plant type data;

[0041] Based on the plant leaf area data, the influence of the plant leaf area on the blockage of the circulating water pump is analyzed to obtain the plant leaf area influence coefficient DP;

[0042] Based on the plant leaf thickness data, the influence of plant leaf thickness on the blockage of the circulating water pump is analyzed to obtain the plant leaf thickness influence coefficient DH;

[0043] Based on the plant type data, the entanglement ability of various types of plants around the circulating water pump to objects is obtained to obtain plant entanglement data. Based on the plant entanglement performance, the influence of the plant entanglement ability on the blockage failure of the circulating water pump is analyzed to obtain the plant entanglement influence coefficient DF;

[0044] The plant influence coefficient DYR is calculated based on the plant blockage correlation function DYR=d3×DP+d4×DH+d5×DF according to the plant leaf area influence coefficient DP, the plant leaf thickness influence coefficient DH and the plant entanglement influence coefficient DF, where d3, d4 and d5 are proportional factors and are all greater than 0.

[0045] By adopting the above technical scheme, the plant prediction area is obtained according to the suction size data at the inlet of the circulating water pump, the plant growth condition around the circulating water pump is analyzed according to the temperature at the inlet of the circulating water pump and the light at the inlet of the circulating water pump to obtain the plant growth coefficient DY, the plant prediction area is adjusted according to the plant growth coefficient DY to obtain the plant detection area, thereby improving the accuracy of plant detection in the case where the circulating water pump is blocked by plants, the plant leaf area influence coefficient DP is obtained according to the plant leaf area data, the plant leaf thickness influence coefficient DH is obtained according to the plant leaf thickness data, the plant entanglement ability is analyzed according to the plant type data to obtain the plant entanglement influence coefficient DF, and the plant influence coefficient DYR is obtained by comprehensively considering the plant leaf area influence coefficient DP, the plant leaf thickness influence coefficient DH and the plant entanglement influence coefficient DF, thereby improving the detection accuracy of the impact of plants on the blockage of the circulating water pipe, integrating the influence of the plant leaf area, plant leaf thickness and plant entanglement ability on the plant blocking the circulating water pump, and improving the accuracy of the fusion processing of multi-source heterogeneous data.

[0046] Preferably, the man-made garbage at the inlet of the circulating water pump is detected to obtain man-made garbage data, wherein the man-made garbage data includes man-made garbage quantity data EN and man-made garbage volume data ET;

[0047] Based on the man-made garbage quantity data EN and the man-made garbage volume data ET, the man-made garbage impact coefficient EIL is calculated based on the man-made garbage blockage correlation function EIL=e1×EN+e2×ET, where e1 and e2 are proportional factors and are both greater than 0.

[0048] By adopting the above technical scheme, the man-made garbage at the inlet of the circulating water pump is detected, and the man-made garbage quantity data EN and the man-made garbage volume data ET are integrated to obtain the man-made garbage influence coefficient EIL, thereby improving the detection accuracy of the impact of man-made garbage around the circulating water pump on the blockage of the circulating water pump, thereby improving the accuracy of the fusion processing of multi-source heterogeneous data.

[0049] Preferably, the disaster risk coefficient OST is calculated based on the disaster risk association function OST=p1×p2×(ω1×CTH+ω2×DYR+ω3×EIL) by comprehensively considering the historical impact weight ratio p1, the environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL, where ω1, ω2 and ω3 are proportional factors and are all greater than 0.

[0050] By adopting the above technical scheme, the disaster risk coefficient OST is obtained by comprehensively considering the historical impact weight ratio p1, environmental impact weight ratio p2, animal impact coefficient CTH, plant impact coefficient DYR and human garbage impact coefficient EIL, thereby improving the accuracy of the disaster risk coefficient OST.

[0051] Preferably, the disaster risk coefficient OST is compared with a preset disaster risk coefficient threshold to obtain a disaster risk comparison result;

[0052] Based on the disaster risk comparison result, if the disaster risk coefficient OST is greater than or equal to the preset disaster risk coefficient threshold, a disaster risk alarm operation is performed; if the disaster risk coefficient OST is less than the preset disaster risk coefficient threshold, no disaster risk alarm operation is required;

[0053] If only risk alarm operation is performed, the disaster risk factor OST will be sent to the user terminal based on the wireless communication module, and the staff will check whether the circulating water pump is blocked. If blocked, it will be repaired and unblocked.

[0054] By adopting the above technical scheme, the disaster risk coefficient OST is compared with the preset disaster risk coefficient threshold. If the disaster risk coefficient OST is greater than or equal to the preset disaster risk coefficient threshold, a disaster risk alarm operation is performed, and the disaster risk coefficient OST is sent to the user terminal. The staff detects the circulating water pump to determine whether there is a blockage. If there is a blockage, maintenance and unblocking work are carried out, thereby improving the safety of the circulating water pump when a disaster risk occurs.

[0055] In the second aspect, the present application provides a multi-source heterogeneous data fusion processing system based on the Internet of Things, which adopts the following technical solutions:

[0056] A multi-source heterogeneous data fusion processing system based on the Internet of Things, comprising:

[0057] The historical usage impact analysis module is configured to obtain the historical usage data of the circulating water pump of the nuclear power plant, analyze the impact of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyze the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1;

[0058] The environmental impact analysis module is configured to detect the air flow and rainfall conditions around the circulating water pump to obtain environmental meteorological data, and analyze the impact of the environmental meteorological conditions around the circulating water pump on the water sucked by the circulating water pump based on the environmental meteorological data to obtain an environmental impact weight ratio p2;

[0059] An animal blockage analysis module is configured to detect animals at the inlet of the circulating water pump to obtain animal data, and analyze the influence of animals at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the animal data to obtain an animal influence coefficient CTH;

[0060] The plant blockage analysis module is configured to detect the plants at the inlet of the circulating water pump to obtain plant data, and analyze the influence of the plants at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the plant data to obtain the plant influence coefficient DYR;

[0061] The man-made garbage blockage analysis module is configured to detect the man-made garbage at the inlet of the circulating water pump to obtain man-made garbage data, and analyze the influence of the man-made garbage at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the man-made garbage data to obtain the man-made garbage influence coefficient EIL;

[0062] The disaster risk analysis module is configured to obtain the disaster risk coefficient OST of the circulating water pump based on the historical impact weight ratio p1, the environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL;

[0063] The alarm detection and dredging module is configured to determine whether to issue a disaster risk alarm for the circulating water pump based on the disaster risk coefficient OST. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal, and the staff will detect the circulating water pump.

[0064] In summary, the present application includes at least one of the following beneficial technical effects:

[0065] 1. The historical usage weight ratio p1 is obtained by analyzing the impact of the historical usage of the circulating water pump on the blockage of the circulating water pump. The environmental impact weight ratio p2 is obtained by analyzing the impact of the surrounding meteorological conditions of the circulating water pump on the water inhaled by the circulating water pump. The animal impact coefficient CTH is obtained by analyzing the impact of the animals at the inlet of the circulating water pump on the blockage of the circulating water pump. The plant impact coefficient DYR is obtained by analyzing the impact of the man-made garbage at the inlet of the circulating water pump on the blockage of the circulating water pump. The man-made garbage impact coefficient EIL is obtained by combining the historical impact weight ratio p1, the environmental The disaster risk coefficient OST of the circulating water pump is obtained by the impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL. Based on the disaster risk coefficient OST, it is determined whether to perform a disaster risk alarm operation on the circulating water pump. The historical use of the circulating water pump, the environmental meteorology around the circulating water pump, the impact of animals and plants at the inlet of the circulating water pump, and the impact of human garbage around the circulating water pump are integrated to determine whether the circulating water pump has a disaster risk. If there is a disaster risk, an alarm operation is performed, which improves the accuracy of the fusion processing of multi-source heterogeneous data and improves the operation safety of the circulating water pump.

[0066] 2. The plant prediction area is obtained by using the suction data at the inlet of the circulating water pump. The plant growth around the circulating water pump is analyzed according to the temperature at the inlet of the circulating water pump and the light at the inlet of the circulating water pump to obtain the plant growth coefficient DY. The plant prediction area is adjusted according to the plant growth coefficient DY to obtain the plant detection area, which improves the accuracy of plant detection in the case of plant blockage of the circulating water pump. The plant leaf area influence coefficient DP is obtained according to the plant leaf area data, and the plant leaf thickness influence coefficient DH is obtained according to the plant leaf thickness data. The plant entanglement ability is analyzed according to the plant type data to obtain the plant entanglement influence coefficient DF. The plant influence coefficient DYR is obtained by combining the plant leaf area influence coefficient DP, the plant leaf thickness influence coefficient DH and the plant entanglement influence coefficient DF, which improves the detection accuracy of the impact of plants on the blockage of the circulating water pipe, integrates the influence of plant leaf area, plant leaf thickness and plant entanglement ability on the plant blocking the circulating water pump, and further improves the accuracy of fusion processing of multi-source heterogeneous data;

[0067] 3. By comparing the disaster risk coefficient OST with the preset disaster risk coefficient threshold, if the disaster risk coefficient OST is greater than or equal to the preset disaster risk coefficient threshold, a disaster risk alarm operation is performed, and the disaster risk coefficient OST is sent to the user terminal. The staff will detect the circulating water pump to determine whether it is blocked. If blockage occurs, maintenance and unblocking work will be carried out, further improving the safety of the circulating water pump when disaster risks occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of a multi-source heterogeneous data fusion processing method based on the Internet of Things, which is mainly embodied in this embodiment;

[0069] Figure 2 This is a module diagram of a multi-source heterogeneous data fusion processing system based on the Internet of Things, which is mainly embodied in this embodiment.

[0070] Figure numerals: 1. Historical usage impact analysis module; 2. Environmental impact analysis module; 3. Animal blockage analysis module; 4. Plant blockage analysis module; 5. Man-made garbage blockage analysis module; 6. Disaster risk analysis module; 7. Alarm detection and dredging module. DETAILED DESCRIPTION

[0071] The present application is further described in detail below in conjunction with the accompanying drawings.

[0072] The embodiment of the present application discloses a multi-source heterogeneous data fusion processing method based on the Internet of Things.

[0073] The multi-source heterogeneous data fusion processing method based on the Internet of Things includes the following steps:

[0074] Reference Figure 1 Step S1, obtain the historical usage data of the circulating water pump of the nuclear power plant, analyze the impact of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyze the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1. Step S1 specifically includes the following sub-steps:

[0075] Step S11, obtaining historical usage data of the circulating water pump of the nuclear power plant, the historical usage data including historical service life data AR, historical failure number data AC and historical failure average degree data AE.

[0076] Step S12, according to the historical fault frequency data AC and the historical fault average degree data AE, based on the historical fault correlation function AD=a1×AC×AE, calculate and analyze the impact of historical fault conditions on the aging of the circulating water pump to obtain the historical fault aging coefficient AD, where a1 is a proportional factor and is greater than 0.

[0077] Step S13, according to the historical service life data AR and the historical failure aging coefficient AD, based on the historical use correlation function q=a2×AR+a3×AD, calculate and analyze the impact of the historical use of the circulating water pump on the aging of the circulating water pump to obtain the historical use aging impact coefficient q, wherein a2 and a3 are proportional factors and are both greater than 0.

[0078] Step S14, analyzing the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump based on the historical usage aging influence coefficient q to obtain a historical usage weight ratio p1, wherein the historical usage weight ratio p1 is positively correlated with the historical usage aging influence coefficient q.

[0079] In specific application, the historical fault aging coefficient AD is obtained through the historical failure number data AC and the historical failure average degree data AE, which improves the detection accuracy of the impact of the historical failure of the circulating water pump on the aging of the circulating water pump. The historical use aging influence coefficient q is obtained according to the historical service life data AR and the historical failure aging coefficient AD, which improves the detection accuracy of the impact of the historical use of the circulating water pump on the aging of the circulating water pump. Based on the historical use aging influence coefficient q, the historical use weight ratio p1 is obtained, which improves the detection accuracy of the impact of the historical use of the circulating water pump on the blockage of the circulating water pump, and improves the accuracy of the fusion processing of multi-source heterogeneous data.

[0080] Reference Figure 1 Step S2, detect the air flow and rainfall conditions around the circulating water pump to obtain environmental meteorological data, and analyze the impact of the ambient weather around the circulating water pump on the water sucked by the circulating water pump based on the environmental meteorological data to obtain the environmental impact weight ratio p2. Step S2 specifically includes the following sub-steps:

[0081] Step S21: the environmental meteorological data includes air flow data and rainfall data L.

[0082] Step S22: Detect the air environment around the circulating water pump using a meteorological instrument to obtain air flow data, where the air flow data includes wind force data f, wind direction data, and wind speed data v.

[0083] Step S23, analyzing the angle between the wind direction of the air around the circulating water pump and the suction at the inlet of the circulating water pump based on the wind direction data to obtain the wind direction angle θ.

[0084] Step S24, according to the wind force data f, wind speed data v and wind direction angle θ, based on the air flow correlation function BC=b1×v×f×cosθ, calculate and analyze the influence of air flow on the water suction of the circulating water pump to obtain the air flow influence coefficient BC, where b1 is the proportional factor and is greater than 0.

[0085] Step S25, based on the meteorological instrument, detect whether there is rainfall around the circulating water pump to obtain the rain impact coefficient BR. If there is no rainfall, the rain environment impact coefficient BR is 0. If there is rainfall, the rainfall data L is obtained.

[0086] Step S26, according to the rainfall data L, wind force data f, wind speed data v and wind direction angle θ, based on the rainfall correlation function BR=b2×L×v×f×cosθ, calculate and analyze the influence of rainwater falling at the inlet of the circulating water pump on the water sucked by the circulating water pump when rainfall occurs in the circulating water pump, and obtain the rainfall influence coefficient BR, where b2 is the proportional factor and is greater than 0.

[0087] Step S27, comprehensively considering the air flow influence coefficient BC and the rainfall influence coefficient BR, and calculating based on the meteorological environment correlation function p2=b3×BC+b4×BR to obtain the environmental influence weight ratio p2, wherein b3 and b4 are proportional factors and are both greater than 0.

[0088] In specific application, the air flow influence coefficient BC is obtained by analyzing the influence of air flow on the suction of water by the circulating water pump through wind force data f, wind direction data and wind speed data v. The rainfall influence coefficient BR is obtained by analyzing the influence of rainwater falling at the inlet of the circulating water pump when it rains on the suction of water by the circulating water pump through rainfall data L, wind force data f, wind direction data and wind speed data v. The environmental impact weight ratio is obtained by combining the air flow influence coefficient BC and the rainfall influence coefficient BR, which integrates the influence of air flow around the circulating water pump and rainfall on the suction of water by the circulating water pump, thereby improving the accuracy of fusion processing of multi-source heterogeneous data.

[0089] Reference Figure 1In step S3, the animals at the inlet of the circulating water pump are detected to obtain animal data, and the influence of the animals at the inlet of the circulating water pump on the blockage of the circulating water pump is analyzed based on the animal data to obtain the animal influence coefficient CTH. Step S3 specifically includes the following sub-steps:

[0090] Step S31, obtaining suction force data at the inlet of the circulating water pump, and obtaining the animal prediction area based on the suction force data at the inlet of the circulating water pump.

[0091] Step S32, adjusting the animal prediction area based on the air flow data to obtain the animal detection area, determining the direction of adjustment of the animal prediction area based on the wind direction data to obtain adjustment direction information, determining the degree of adjustment of the animal prediction area based on the wind force data f and the wind speed data v to obtain adjustment degree information, and adjusting the animal prediction area based on the comprehensive adjustment direction information and adjustment degree information to obtain the animal detection area.

[0092] Step S33, detecting the animals in the animal detection area to obtain animal data, the animal data including animal volume data, animal type data, animal activity frequency data and animal skin smoothness data.

[0093] Step S34, analyzing the effect of the animal volume on the animal's escape after the animal mistakenly enters the suction pipe of the circulating water pump based on the animal volume data to obtain a first escape coefficient CD, wherein the first escape coefficient CD is negatively correlated with the animal volume data.

[0094] Step S35, analyzing the activity level of the animal that has mistakenly entered the suction pipe of the circulating water pump based on the animal type data and the animal activity frequency data to obtain activity level data, wherein the animal activity frequency data is positively correlated with the activity level data; and obtaining a second escape coefficient CE based on the activity level data, wherein the second escape coefficient CE is positively correlated with the activity level data.

[0095] Step S36, analyzing the animal skin smoothness data to obtain a third escape coefficient CT, wherein the third escape coefficient CT is positively correlated with the animal skin smoothness data.

[0096] Step S37, combining the first escape coefficient CD, the second escape coefficient CE and the third escape coefficient CT, based on the animal blocking correlation function The animal impact coefficient CTH is calculated, where c1, c2, and c3 are proportional factors and are all greater than 0.

[0097] In specific application, the animal prediction area is determined by the suction size data at the circulating water pump inlet, and the animal prediction area is adjusted according to the air flow data to obtain the animal detection area, which improves the accuracy of animal detection in the case of animal blockage of the circulating water pump. The first escape coefficient CD is obtained according to the animal volume data, the second escape coefficient CE is obtained according to the animal type data and the animal activity frequency data, and the third escape coefficient CT is obtained according to the animal skin smoothness data. The animal influence coefficient CTH is obtained by combining the first escape coefficient CD, the second escape coefficient CE and the third escape coefficient CT, which improves the detection accuracy of the impact of animal blockage on the circulating water pipe, integrates the impact of animal volume, animal type, animal activity frequency and animal skin smoothness on animal blockage of the circulating water pump, and improves the accuracy of fusion processing of multi-source heterogeneous data.

[0098] Reference Figure 1 In step S4, the plants at the inlet of the circulating water pump are detected to obtain plant data, and the influence of the plants at the inlet of the circulating water pump on the blockage of the circulating water pump is analyzed based on the plant data to obtain the plant influence coefficient DYR. Step S4 specifically includes the following sub-steps:

[0099] Step S41, obtaining a plant prediction area based on the suction force data at the inlet of the circulating water pump.

[0100] Step S42, based on the temperature sensor detecting the temperature at the inlet of the circulating water pump to obtain water area temperature data, the water area temperature data is compared with the preset water area standard temperature data to obtain a water area temperature comparison result, and based on the water area temperature comparison result, the influence of the temperature at the inlet of the circulating water pump on the growth of plants at the inlet of the circulating water pump is analyzed to obtain the temperature influence on plant coefficient DQ, wherein the greater the difference in the water area temperature comparison result, the greater the temperature influence on plant coefficient DQ.

[0101] Step S43, based on the light sensor detecting the light at the inlet of the circulating water pump to obtain the water area light data, compare the water area light data with the preset water area standard light data to obtain the water area light comparison result, and based on the water area light comparison result, analyze the influence of the light at the inlet of the circulating water pump on the growth of plants at the inlet of the circulating water pump to obtain the light influence on plants coefficient DG, wherein the greater the difference in the water area light comparison result, the greater the light influence on plants coefficient DG.

[0102] Step S44, based on the plant area correlation function, according to the temperature influence plant coefficient DQ and the light influence plant coefficient DG The plant growth coefficient DY is calculated, wherein d1 and d2 are proportional factors and are both greater than 0. The plant prediction area is adjusted based on the plant growth coefficient DY to obtain the plant detection area, wherein the larger the plant growth coefficient DY is, the higher the degree of adjustment of the plant prediction area is.

[0103] Step S45, detecting the plants in the plant detection area to obtain plant data, the plant data including plant leaf area data, plant leaf thickness data and plant type data.

[0104] Step S46, analyzing the influence of the plant leaf area on the blockage of the circulating water pump based on the plant leaf area data to obtain the plant leaf area influence coefficient DP.

[0105] Step S47, analyzing the influence of the plant leaf thickness on the blockage of the circulating water pump based on the plant leaf thickness data to obtain the plant leaf thickness influence coefficient DH.

[0106] Step S48, obtaining the entanglement ability of various types of plants around the circulating water pump to objects based on the plant type data to obtain plant entanglement data, and analyzing the influence of the entanglement ability of the plants on the blockage failure of the circulating water pump based on the plant entanglement performance to obtain the plant entanglement influence coefficient DF.

[0107] Step S49, comprehensively considering the plant leaf area influence coefficient DP, the plant leaf thickness influence coefficient DH and the plant entanglement influence coefficient DF, and calculating the plant influence coefficient DYR based on the plant blocking correlation function DYR=d3×DP+d4×DH+d5×DF, wherein d3, d4 and d5 are proportional factors and are all greater than 0.

[0108] In specific application, the plant prediction area is obtained through the suction size data at the inlet of the circulating water pump, the plant growth conditions around the circulating water pump are analyzed according to the temperature at the inlet of the circulating water pump and the light at the inlet of the circulating water pump to obtain the plant growth coefficient DY, and the plant prediction area is adjusted according to the plant growth coefficient DY to obtain the plant detection area, which improves the accuracy of plant detection in the case of plant blockage of the circulating water pump, the plant leaf area influence coefficient DP is obtained according to the plant leaf area data, the plant leaf thickness influence coefficient DH is obtained according to the plant leaf thickness data, the plant entanglement ability is analyzed according to the plant type data to obtain the plant entanglement influence coefficient DF, and the plant influence coefficient DYR is obtained by combining the plant leaf area influence coefficient DP, the plant leaf thickness influence coefficient DH and the plant entanglement influence coefficient DF, which improves the detection accuracy of the impact of plants on the blockage of the circulating water pipe, integrates the influence of plant leaf area, plant leaf thickness and plant entanglement ability on the blockage of the circulating water pump by plants, and improves the accuracy of fusion processing of multi-source heterogeneous data.

[0109] Reference Figure 1 In step S5, the man-made garbage at the inlet of the circulating water pump is detected to obtain man-made garbage data, and the influence of the man-made garbage at the inlet of the circulating water pump on the blockage of the circulating water pump is analyzed based on the man-made garbage data to obtain the man-made garbage influence coefficient EIL. Step S5 specifically includes the following sub-steps:

[0110] Step S51, detecting the man-made garbage at the inlet of the circulating water pump to obtain man-made garbage data, wherein the man-made garbage data includes man-made garbage quantity data EN and man-made garbage volume data ET.

[0111] Step S52, integrating the man-made garbage quantity data EN and the man-made garbage volume data ET, and calculating the man-made garbage impact coefficient EIL based on the man-made garbage blockage correlation function EIL=e1×EN+e2×ET, wherein e1 and e2 are proportional factors and are both greater than 0.

[0112] In specific applications, by detecting the man-made garbage at the inlet of the circulating water pump, the man-made garbage quantity data EN and the man-made garbage volume data ET are integrated to obtain the man-made garbage impact coefficient EIL, which improves the detection accuracy of the impact of man-made garbage around the circulating water pump on the blockage of the circulating water pump, thereby improving the accuracy of the fusion processing of multi-source heterogeneous data.

[0113] Reference Figure 1 Step S6, based on the historical impact weight ratio p1, the environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL, the disaster risk coefficient OST of the circulating water pump is obtained. Step S6 specifically includes:

[0114] Based on the comprehensive historical impact weight ratio p1, environmental impact weight ratio p2, animal impact coefficient CTH, plant impact coefficient DYR and human garbage impact coefficient EIL, the disaster risk coefficient OST is calculated based on the disaster risk association function OST=p1×p2×(ω1×CTH+ω2×DYR+ω3×EIL), where ω1, ω2, and ω3 are proportional factors and are all greater than 0.

[0115] In specific application, the disaster risk coefficient OST is obtained by comprehensively considering the historical impact weight ratio p1, environmental impact weight ratio p2, animal impact coefficient CTH, plant impact coefficient DYR and human garbage impact coefficient EIL, which improves the accuracy of the disaster risk coefficient OST.

[0116] Reference Figure 1 , step S7, based on the disaster risk coefficient OST, determine whether to issue a disaster risk alarm to the circulating water pump. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal, and the staff performs a test on the circulating water pump. Step S7 specifically includes the following sub-steps:

[0117] Step S71, comparing the disaster risk coefficient OST with a preset disaster risk coefficient threshold to obtain a disaster risk comparison result.

[0118] Step S72, based on the disaster risk comparison result, if the disaster risk coefficient OST is greater than or equal to the preset disaster risk coefficient threshold, a disaster risk alarm operation is performed, that is, a voice broadcast is performed to remind the staff that the disaster risk is high; if the disaster risk coefficient OST is less than the preset disaster risk coefficient threshold, there is no need to perform a disaster risk alarm operation.

[0119] Step S73, if only risk alarm operation is performed, the disaster risk factor OST is sent to the user terminal based on the wireless communication module, and the staff detects whether the circulating water pump is blocked, and clears it if it is blocked. It should be noted that the wireless communication module in the embodiment of the present application refers to a wireless communication module based on wireless Bluetooth technology.

[0120] In specific application, the disaster risk coefficient OST is compared with the preset disaster risk coefficient threshold. If the disaster risk coefficient OST is greater than or equal to the preset disaster risk coefficient threshold, a disaster risk alarm operation is performed, and the disaster risk coefficient OST is sent to the user terminal. The staff will check the circulating water pump to determine whether there is a blockage. If there is a blockage, maintenance and unblocking work will be carried out, thereby improving the safety of the circulating water pump when a disaster risk occurs.

[0121] The embodiment of the present application also discloses a multi-source heterogeneous data fusion processing system based on the Internet of Things.

[0122] Reference Figure 2 The multi-source heterogeneous data fusion processing system based on the Internet of Things includes a historical usage impact analysis module, an environmental impact analysis module, an animal blockage analysis module, a plant blockage analysis module, an artificial garbage blockage analysis module, a disaster risk analysis module, and an alarm detection and dredging module.

[0123] The historical usage impact analysis module is configured to obtain the historical usage data of the circulating water pump of the nuclear power plant, analyze the impact of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyze the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1;

[0124] The environmental impact analysis module is configured to detect the air flow and rainfall conditions around the circulating water pump to obtain environmental meteorological data, and analyze the impact of the environmental meteorological conditions around the circulating water pump on the water sucked by the circulating water pump based on the environmental meteorological data to obtain an environmental impact weight ratio p2;

[0125] An animal blockage analysis module is configured to detect animals at the inlet of the circulating water pump to obtain animal data, and analyze the influence of animals at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the animal data to obtain an animal influence coefficient CTH;

[0126] The plant blockage analysis module is configured to detect the plants at the inlet of the circulating water pump to obtain plant data, and analyze the influence of the plants at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the plant data to obtain the plant influence coefficient DYR;

[0127] The man-made garbage blockage analysis module is configured to detect the man-made garbage at the inlet of the circulating water pump to obtain man-made garbage data, and analyze the influence of the man-made garbage at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the man-made garbage data to obtain the man-made garbage influence coefficient EIL;

[0128] The disaster risk analysis module is configured to obtain the disaster risk coefficient OST of the circulating water pump based on the historical impact weight ratio p1, the environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL;

[0129] The alarm detection and dredging module is configured to determine whether to issue a disaster risk alarm for the circulating water pump based on the disaster risk coefficient OST. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal, and the staff will detect the circulating water pump.

[0130] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A multi-source heterogeneous data fusion processing method based on the Internet of Things, characterized in that: The following steps are involved: Obtain the historical usage data of the circulating water pump of the nuclear power plant, analyze the impact of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyze the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1; The air flow and rainfall conditions around the circulating water pump are detected to obtain environmental meteorological data, and the influence of the environmental meteorological conditions around the circulating water pump on the water sucked by the circulating water pump is analyzed based on the environmental meteorological data to obtain the environmental impact weight ratio p2; The animals at the inlet of the circulating water pump are detected to obtain animal data, and the influence of the animals at the inlet of the circulating water pump on the blockage failure of the circulating water pump is analyzed based on the animal data to obtain the animal influence coefficient CTH; The plants at the inlet of the circulating water pump are detected to obtain plant data, and the influence of the plants at the inlet of the circulating water pump on the blockage of the circulating water pump is analyzed based on the plant data to obtain the plant influence coefficient DYR; The man-made garbage at the inlet of the circulating water pump is detected to obtain the man-made garbage data, and based on the man-made garbage data, the influence of the man-made garbage at the inlet of the circulating water pump on the blockage failure of the circulating water pump is analyzed to obtain the man-made garbage influence coefficient EIL; Based on the historical impact weight ratio p1, environmental impact weight ratio p2, animal impact coefficient CTH, plant impact coefficient DYR and human waste impact coefficient EIL, the disaster risk coefficient OST of the circulating water pump is obtained; Based on the disaster risk coefficient OST, it is determined whether to issue a disaster risk alarm for the circulating water pump. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal, and the staff conducts inspections on the circulating water pump.

2. According to the method for multi-source heterogeneous data fusion processing based on the Internet of Things in claim 1, it is characterized in that: The steps of obtaining historical usage data of a circulating water pump of a nuclear power plant, and analyzing the influence of historical usage of the circulating water pump of the nuclear power plant on the blockage failure of the circulating water pump based on the historical usage data to obtain a historical usage weight ratio p1 specifically include: Acquire historical usage data of a circulating water pump of a nuclear power plant, wherein the historical usage data includes historical service life data AR, historical failure number data AC, and historical failure average degree data AE; According to the historical fault frequency data AC and the historical fault average degree data AE, the historical fault correlation function AD = a1 × AC × AE is used to calculate and analyze the influence of historical fault conditions on the aging of the circulating water pump to obtain the historical fault aging coefficient AD, where a1 is a proportional factor and is greater than 0; According to the historical service life data AR and the historical failure aging coefficient AD, the historical service correlation function q = a2 × AR + a3 × AD is used to calculate and analyze the influence of the historical service of the circulating water pump on the aging of the circulating water pump to obtain the historical service aging influence coefficient q, where a2 and a3 are proportional factors and are both greater than 0; Based on the historical usage aging influence coefficient q, the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump is analyzed to obtain the historical usage weight ratio p1, among which the historical usage weight ratio p1 is positively correlated with the historical usage aging influence coefficient q.

3. According to the method for multi-source heterogeneous data fusion processing based on the Internet of Things in claim 2, it is characterized in that: The steps of detecting the air flow and rainfall conditions around the circulating water pump to obtain environmental meteorological data, and analyzing the influence of the environmental meteorological conditions around the circulating water pump on the water sucked by the circulating water pump based on the environmental meteorological data to obtain the environmental impact weight ratio p2 specifically include: The environmental meteorological data includes air flow data and rainfall data L; The air environment around the circulating water pump is detected based on a meteorological instrument to obtain air flow data, wherein the air flow data includes wind force data f, wind direction data and wind speed data v; Based on the wind direction data, the angle between the wind direction of the air around the circulating water pump and the suction at the inlet of the circulating water pump is analyzed to obtain the wind direction angle θ; According to the wind force data f, wind speed data v and wind direction angle θ, the influence of air flow on the water suction of the circulating water pump is calculated and analyzed based on the air flow correlation function BC=b1×v×f×cosθ, and the air flow influence coefficient BC is obtained, where b1 is a proportional factor and is greater than 0; Based on the meteorological instrument, whether there is rainfall around the circulating water pump is detected to obtain the rain impact coefficient BR. If there is no rainfall, the rain environmental impact coefficient BR is 0. If there is rainfall, the rainfall data L is obtained; According to the rainfall data L, wind force data f, wind speed data v and wind direction angle θ, the rainfall correlation function BR = b2 × L × v × f × cosθ is used to calculate and analyze the influence of rainwater falling at the inlet of the circulating water pump on the water sucked by the circulating water pump when rainfall occurs in the circulating water pump, and the rainfall influence coefficient BR is obtained, where b2 is a proportional factor and is greater than 0; The comprehensive air flow influence coefficient BC and the rainfall influence coefficient BR are calculated based on the meteorological environment correlation function p2=b3×BC+b4×BR to obtain the environmental impact weight ratio p2, where b3 and b4 are proportional factors and are both greater than 0.

4. According to the method for multi-source heterogeneous data fusion processing based on the Internet of Things according to claim 3, it is characterized in that: The steps of detecting animals at the inlet of the circulating water pump to obtain animal data, and analyzing the influence of the animals at the inlet of the circulating water pump on the blockage of the circulating water pump based on the animal data to obtain the animal influence coefficient CTH specifically include: Acquire suction data at the inlet of the circulating water pump, and obtain the animal prediction area based on the suction data at the inlet of the circulating water pump; The animal prediction area is adjusted based on the air flow data to obtain the animal detection area, the direction of adjusting the animal prediction area is determined based on the wind direction data to obtain adjustment direction information, the degree of adjustment of the animal prediction area is determined based on the wind force data f and the wind speed data v to obtain adjustment degree information, and the animal prediction area is adjusted based on the adjustment direction information and the adjustment degree information to obtain the animal detection area; Detecting animals in the animal detection area to obtain animal data, the animal data including animal volume data, animal type data, animal activity frequency data, and animal skin smoothness data; Based on the animal volume data, the influence of the animal volume on the animal's escape after the animal mistakenly enters the suction pipe of the circulating water pump is analyzed to obtain the first escape coefficient CD, wherein the first escape coefficient CD is negatively correlated with the animal volume data; The activity level of the animals that strayed into the water suction pipe of the circulating water pump is analyzed based on the animal type data and the animal activity frequency data to obtain activity level data, wherein the animal activity frequency data is positively correlated with the activity level data; and the second escape coefficient CE is obtained based on the activity level data, wherein the second escape coefficient CE is positively correlated with the activity level data; The third escape coefficient CT is obtained by analyzing the data of animal skin smoothness, wherein the third escape coefficient CT is positively correlated with the data of animal skin smoothness; Comprehensive first escape coefficient CD, second escape coefficient CE and third escape coefficient CT, based on animal blockage correlation function The animal impact coefficient CTH is calculated, where c1, c2, and c3 are proportional factors and are all greater than 0.

5. According to the method for multi-source heterogeneous data fusion processing based on the Internet of Things according to claim 4, it is characterized in that: The steps of detecting plants at the inlet of the circulating water pump to obtain plant data, and analyzing the influence of the plants at the inlet of the circulating water pump on the blockage of the circulating water pump based on the plant data to obtain the plant influence coefficient DYR specifically include: The predicted plant area is obtained based on the suction force data at the inlet of the circulating water pump; The temperature at the inlet of the circulating water pump is detected by the temperature sensor to obtain water area temperature data, and the water area temperature data is compared with the preset water area standard temperature data to obtain a water area temperature comparison result. Based on the water area temperature comparison result, the influence of the temperature at the inlet of the circulating water pump on the growth of plants at the inlet of the circulating water pump is analyzed to obtain the temperature influence coefficient DQ of plants, wherein the greater the difference in the water area temperature comparison result, the greater the temperature influence coefficient DQ of plants; The water area illumination data is obtained by detecting the illumination at the inlet of the circulating water pump based on the illumination sensor, and the water area illumination data is compared with the preset water area standard illumination data to obtain the water area illumination comparison result. Based on the water area illumination comparison result, the influence of the illumination at the inlet of the circulating water pump on the growth of plants at the inlet of the circulating water pump is analyzed to obtain the illumination influence coefficient DG of plants, wherein the greater the difference in the water area illumination comparison result, the greater the illumination influence coefficient DG of plants; According to the influence of temperature on plant coefficient DQ and light on plant coefficient DG, based on plant area correlation function The plant growth coefficient DY is calculated, wherein d1 and d2 are proportional factors and are both greater than 0. The plant prediction area is adjusted based on the plant growth coefficient DY to obtain the plant detection area. The larger the plant growth coefficient DY is, the higher the degree of adjustment of the plant prediction area is. Detecting plants in a plant detection area to obtain plant data, wherein the plant data includes plant leaf area data, plant leaf thickness data, and plant type data; Based on the plant leaf area data, the influence of the plant leaf area on the blockage of the circulating water pump is analyzed to obtain the plant leaf area influence coefficient DP; Based on the plant leaf thickness data, the influence of plant leaf thickness on the blockage of the circulating water pump is analyzed to obtain the plant leaf thickness influence coefficient DH; Based on the plant type data, the entanglement ability of various types of plants around the circulating water pump to objects is obtained to obtain plant entanglement data. Based on the plant entanglement performance, the influence of the plant entanglement ability on the blockage failure of the circulating water pump is analyzed to obtain the plant entanglement influence coefficient DF; The plant influence coefficient DYR is calculated based on the plant blockage correlation function DYR=d3×DP+d4×DH+d5×DF according to the plant leaf area influence coefficient DP, the plant leaf thickness influence coefficient DH and the plant entanglement influence coefficient DF, where d3, d4 and d5 are proportional factors and are all greater than 0.

6. The multi-source heterogeneous data fusion processing method based on the Internet of Things according to claim 5 is characterized in that: The steps of detecting the man-made garbage at the inlet of the circulating water pump to obtain the man-made garbage data, and analyzing the influence of the man-made garbage at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the man-made garbage data to obtain the man-made garbage influence coefficient EIL specifically include: Detecting the man-made garbage at the inlet of the circulating water pump to obtain man-made garbage data, wherein the man-made garbage data includes man-made garbage quantity data EN and man-made garbage volume data ET; Based on the man-made garbage quantity data EN and the man-made garbage volume data ET, the man-made garbage impact coefficient EIL is calculated based on the man-made garbage blockage correlation function EIL=e1×EN+e2×ET, where e1 and e2 are proportional factors and are both greater than 0.

7. The multi-source heterogeneous data fusion processing method based on the Internet of Things according to claim 6 is characterized in that: The steps of obtaining the disaster risk coefficient OST of the circulating water pump based on the historical impact weight ratio p1, the environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL specifically include: Based on the comprehensive historical impact weight ratio p1, environmental impact weight ratio p2, animal impact coefficient CTH, plant impact coefficient DYR and human garbage impact coefficient EIL, the disaster risk coefficient OST is calculated based on the disaster risk association function OST=p1×p2×(ω1×CTH+ω2×DYR+ω3×EIL), where ω1, ω2, and ω3 are proportional factors and are all greater than 0.

8. The multi-source heterogeneous data fusion processing method based on the Internet of Things according to claim 7 is characterized in that: Based on the disaster risk coefficient OST, it is determined whether to issue a disaster risk alarm to the circulating water pump. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal. The staff conducts the following steps to detect the circulating water pump: Compare the disaster risk coefficient OST with the preset disaster risk coefficient threshold to obtain a disaster risk comparison result; Based on the disaster risk comparison result, if the disaster risk coefficient OST is greater than or equal to the preset disaster risk coefficient threshold, a disaster risk alarm operation is performed; if the disaster risk coefficient OST is less than the preset disaster risk coefficient threshold, no disaster risk alarm operation is required; If only risk alarm operation is performed, the disaster risk factor OST will be sent to the user terminal based on the wireless communication module, and the staff will check whether the circulating water pump is blocked. If blocked, it will be repaired and unblocked.

9. A multi-source heterogeneous data fusion processing system based on the Internet of Things, characterized in that: The multi-source heterogeneous data fusion processing system based on the Internet of Things is used to implement the multi-source heterogeneous data fusion processing method based on the Internet of Things described in any one of claims 1 to 8, including: The historical usage impact analysis module is configured to obtain the historical usage data of the circulating water pump of the nuclear power plant, analyze the impact of the historical usage of the circulating water pump of the nuclear power plant on the aging of the circulating water pump based on the historical usage data, and then analyze the impact of the historical usage of the circulating water pump on the blockage failure of the circulating water pump to obtain the historical usage weight ratio p1; The environmental impact analysis module is configured to detect the air flow and rainfall conditions around the circulating water pump to obtain environmental meteorological data, and analyze the impact of the environmental meteorological conditions around the circulating water pump on the water sucked by the circulating water pump based on the environmental meteorological data to obtain an environmental impact weight ratio p2; An animal blockage analysis module is configured to detect animals at the inlet of the circulating water pump to obtain animal data, and analyze the influence of animals at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the animal data to obtain an animal influence coefficient CTH; The plant blockage analysis module is configured to detect the plants at the inlet of the circulating water pump to obtain plant data, and analyze the influence of the plants at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the plant data to obtain the plant influence coefficient DYR; The man-made garbage blockage analysis module is configured to detect the man-made garbage at the inlet of the circulating water pump to obtain man-made garbage data, and analyze the influence of the man-made garbage at the inlet of the circulating water pump on the blockage failure of the circulating water pump based on the man-made garbage data to obtain the man-made garbage influence coefficient EIL; The disaster risk analysis module is configured to obtain the disaster risk coefficient OST of the circulating water pump based on the historical impact weight ratio p1, the environmental impact weight ratio p2, the animal impact coefficient CTH, the plant impact coefficient DYR and the human garbage impact coefficient EIL; The alarm detection and dredging module is configured to determine whether to issue a disaster risk alarm for the circulating water pump based on the disaster risk coefficient OST. If a disaster risk alarm is issued, the disaster risk coefficient OST is sent to the user terminal, and the staff will detect the circulating water pump.

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