Intelligent inspection system for thermal power generating unit

Abstract

The invention discloses an intelligent inspection system for a thermal power generating unit, and relates to the technical field of inspection, the intelligent inspection system comprises a production analysis module, a fault inspection module and a daily inspection module, whether the thermal power generating unit breaks down or not is judged by analyzing production data, and abnormal equipment is obtained by analyzing equipment efficiency information; the fault patrol data of each abnormal device is analyzed to obtain each fault device and each suspected fault device, the unmanned aerial vehicle is used to collect the unmanned aerial vehicle patrol information of each suspected fault device, the unmanned aerial vehicle patrol information is analyzed to distinguish the fault device in each suspected fault device from the normal device, and the fault device in each suspected fault device is obtained. Therefore, the fault equipment is obtained, the fault frequency of each equipment is recorded, the fault frequency of each equipment of the thermal power generating unit is analyzed, the daily inspection frequency of the thermal power generating unit is obtained, the inspection efficiency is improved, and the accuracy of the inspection result is improved.

Description

Technical Field

[0001] The present invention relates to the field of inspection technology, and in particular to an intelligent inspection system for thermal power units. Background Art

[0002] In the electric power industry, thermal power units are core power production equipment, and their operational stability and safety have a decisive impact on the overall efficiency of the power system. The inspection of traditional thermal power units is mainly completed manually. This method is inefficient and cannot ensure timely detection of all potential fault risks, increasing the possibility of equipment failure and maintenance costs. It is necessary to develop a new type of intelligent inspection system for thermal power units. The system should have a higher level of intelligence and automation to improve the accuracy and timeliness of fault detection, ensure the safe and stable operation of thermal power units, and reduce maintenance costs.

[0003] Existing technologies such as the invention patent application with announcement number CN115392496A disclose a smart inspection system for thermal power units, including: an inspection module, the inspection components of which collect data from the thermal power units; an inspection cloud platform: used to receive, analyze and store the data collected by the inspection components, and issue early warnings and alarms based on the analysis results of the data to achieve unified management of the thermal power units; 24-hour automatic inspection of the main equipment of the thermal power plant, and real-time collection of equipment operation status data through cameras, robots, sensor equipment, etc., and discrimination and analysis of the equipment operation status through artificial intelligence algorithms, real-time judgment of the equipment status and alarms for abnormal status, providing a basis for equipment maintenance. It reduces the maintenance costs of personnel and equipment, increases the reliability of inspections, effectively improves the work efficiency of power plant equipment inspections, and truly plays a role in reducing staff and increasing efficiency.

[0004] With respect to the above scheme, the applicant of the present invention has found that the above technology has at least the following technical problems: 1. The above scheme only performs analysis through visual inspection components, temperature inspection components, abnormal sound inspection components and odor inspection components, and may not be able to comprehensively determine whether the equipment is faulty. The above scheme does not analyze the working efficiency of each device through the properties of each device. A single evaluation may reduce the accuracy of the analysis.

[0005] 2. The above scheme does not analyze the specific inspection frequency, which increases the inspection cost and wastes the system computing power and inspection equipment energy. At the same time, the above scheme does not conduct fault inspections. When abnormalities occur in thermal power equipment, it is impossible to analyze data in time and find the faulty equipment. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide an intelligent inspection system for thermal power units.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a smart inspection system for thermal power units, comprising the following modules: a production analysis module, for collecting production data, and judging whether a fault occurs in the thermal power unit based on the production data, and performing a fault inspection if a fault occurs.

[0008] The fault inspection module is used to collect equipment efficiency information during fault inspection, analyze the equipment efficiency information, obtain each abnormal equipment, collect fault inspection data through each sensor of each abnormal equipment, analyze the fault inspection data, divide each abnormal equipment into each faulty equipment and each suspected faulty equipment, mark each faulty equipment, prompt the staff, collect drone inspection data, mark each faulty equipment in the suspected faulty equipment according to the drone inspection data, and prompt the staff.

[0009] The daily inspection module is used to analyze the fault history records in the database, obtain the abnormal index of the thermal power unit, and then set the daily inspection frequency.

[0010] Preferably, the equipment efficiency information is analyzed, and the specific analysis process is as follows: the historical combustion efficiency of each combustion device when the combustion device is normal is obtained from the database, and the maximum and minimum values ​​of the historical combustion efficiency of each combustion device when normal are set as the upper and lower limits of the standard combustion efficiency range, respectively, so as to set the standard combustion efficiency range of each combustion device. If the combustion efficiency of a certain combustion device does not belong to the standard combustion efficiency range, it indicates that the combustion device is an abnormal device. Similarly, each abnormal device in each water vapor device is obtained.

[0011] The historical rotor speeds and historical electric energy generation rates of each power generation equipment when the power generation equipment is normal are obtained from the database, and the historical maximum and minimum electric energy generation rates of each historical rotor speed are recorded as the upper and lower limits of the normal electric energy generation rate range, respectively. The normal electric energy generation rate range of each historical rotor speed is set accordingly, and then the normal electric energy generation rate range of each power generation equipment is obtained according to the rotor speed of each power generation equipment. If the electric energy generation rate of a certain power generation equipment does not belong to the normal electric energy generation rate range of the power generation equipment, it indicates that the power generation equipment is an abnormal equipment.

[0012] Preferably, the fault inspection data is analyzed, and the specific analysis process is as follows: the fault inspection data includes characteristic information of temperature, pressure and sound of each collection point of each abnormal device, and the temperatures and pressures of each collection point when each device is normal are obtained from the database, and the maximum and minimum temperatures of each collection point when each device is normal are set as the upper and lower limits of the normal temperature range, so as to set the normal temperature range of each collection point of each device, and similarly, set the normal pressure range of each collection point of each device.

[0013] The characteristic information of each abnormal sound of each collection point of each device is obtained from the database. If the temperature of a collection point of an abnormal device does not belong to the normal temperature range of the collection point of the abnormal device, the pressure does not belong to the normal pressure range of the collection point of the abnormal device, or the characteristic information of the sound corresponds to the characteristic information of a certain abnormal sound of the collection point of the abnormal device, it indicates that the collection point of the abnormal device is abnormal.

[0014] If any collection point of an abnormal device is abnormal, it indicates that the abnormal device is a faulty device. If all collection points of an abnormal device are normal, it indicates that the abnormal device is a suspected faulty device.

[0015] Preferably, the drone inspection data is analyzed, and the specific analysis process is as follows: the ambient temperature of each inspection point and the concentration of each abnormal gas when each device is normal are obtained from the database, and the maximum and minimum values ​​of the ambient temperature of each inspection point when each device is normal are set as the upper and lower limits of the normal ambient temperature range, respectively, so as to set the normal ambient temperature range of each inspection point of each device. Similarly, the normal concentration range of each abnormal gas at each inspection point of each device is obtained.

[0016] The characteristic information of abnormal environmental sounds at each inspection point of each device is obtained from the database. If the ambient temperature of a certain inspection point of a suspected faulty device does not belong to the normal ambient temperature range of the inspection point of the suspected faulty device, the abnormal gas concentration does not belong to the normal range of the abnormal gas concentration at the inspection point of the suspected faulty device, or the characteristic information of the environmental sounds corresponds to and matches the characteristic information of the abnormal environmental sounds at the inspection point of the suspected faulty device, it indicates that the inspection point of the suspected faulty device is abnormal.

[0017] If any inspection point of a suspected faulty device is abnormal, it indicates that the suspected faulty device is a faulty device. If all inspection points of a suspected faulty device are normal, it indicates that the suspected faulty device is a normal device.

[0018] Preferably, the daily inspection frequency is set, and the specific setting process is as follows: the average value is calculated for each historical production capacity of each fault index in the database to obtain the standard production capacity of each fault index, and the standard production capacity of the thermal power unit is obtained according to the fault index of the thermal power unit. If the standard production capacity of the thermal power unit is less than the preset production capacity, the thermal power unit is overhauled.

[0019] If the thermal power unit is not under maintenance, the daily inspection frequencies corresponding to the fault indexes are obtained from the database, and the daily inspection frequencies of the thermal power unit are obtained according to the fault indexes of the thermal power unit.

[0020] If the thermal power plant is under maintenance, the daily inspection frequency after maintenance corresponding to each fault index obtained in the database is obtained, and the daily inspection frequency of the thermal power plant is obtained according to the fault index of the thermal power plant.

[0021] The beneficial effects of the present invention are: 1. The present invention collects production data through the production analysis module to determine whether a fault occurs in the thermal power unit. If a fault occurs, a fault inspection is performed, and then the abnormal equipment is found through the equipment efficiency information of the fault inspection module, and the fault inspection data is analyzed to determine whether the abnormal equipment is a fault data. If it is not a fault, it is recorded as a suspected fault, and then the drone inspection data is used to further confirm whether the suspected fault is a fault. At the same time, the abnormal index of the thermal power unit is analyzed through the fault history record, and the daily inspection frequency is set. The present invention analyzes whether the equipment is faulty by analyzing the working efficiency of each equipment, the fault inspection data and the drone collection data analysis at three levels, thereby increasing the accuracy of the analysis results.

[0022] 2. The present invention analyzes the production data of thermal power units in real time. When abnormalities occur in the production of thermal power units, inspections are carried out, thereby increasing the effectiveness of inspection data, increasing production safety, and reducing production waste. At the same time, the frequency of daily inspections is set according to the fault history data of each device, thereby reducing the cost of daily inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] according to Figure 1 As shown, the present invention provides a smart inspection system for thermal power units, comprising the following modules: a production analysis module, a fault inspection module, a daily inspection module and a database.

[0027] The fault inspection module is connected with the production analysis module, the daily inspection module and the database.

[0028] The production analysis module is used to collect production data and determine whether the thermal power unit has a fault based on the production data. If a fault occurs, a fault inspection is performed.

[0029] In a specific embodiment, the specific collection process of the production data is as follows: obtaining the current fuel consumption rate from the fuel usage record of the warehouse, and obtaining the current electricity production rate from the electricity output record.

[0030] In a specific embodiment, the determination of whether a thermal power unit has a fault is carried out as follows: the production data includes a current fuel consumption rate and a current electricity production rate, and each historical electricity production rate under each historical fuel consumption rate is obtained from a database, and then each historical electricity production rate under the current fuel consumption rate is obtained, and the maximum and minimum values ​​of the historical electricity production rates under the current fuel consumption rate are respectively recorded as the upper and lower limits of the current electricity preset production rate range, so as to set the current electricity preset production rate range. If the current electricity production rate belongs to the current electricity preset production rate range, it indicates that the current electricity production is normal. Conversely, if the current electricity production rate does not belong to the current electricity preset production rate range, it indicates that the current electricity production is abnormal and a fault inspection is required.

[0031] The fault inspection module is used to collect equipment efficiency information during fault inspection, analyze the equipment efficiency information, obtain each abnormal equipment, collect fault inspection data through each sensor of each abnormal equipment, analyze the fault inspection data, divide each abnormal equipment into each faulty equipment and each suspected faulty equipment, mark each faulty equipment, prompt the staff, collect drone inspection data, mark each faulty equipment in the suspected faulty equipment according to the drone inspection data, and prompt the staff.

[0032] In a specific embodiment, the equipment efficiency information is collected, and the specific collection process is as follows: the equipment efficiency information includes the combustion efficiency of each combustion device, the water vapor efficiency of each water vapor device, the rotor speed of each power generation device and the power generation rate of each power generation device.

[0033] It should be noted that a thermal power unit contains a combustion system, a water-steam system and a power generation system, and the various equipment of a thermal power unit are divided into combustion equipment, water-steam equipment and power generation equipment.

[0034] The fuel combustion rate J of each combustion equipment is obtained through the sensors of each combustion equipment a , water vapor generation rate D a and the water vapor temperature T a ′, substitute the fuel combustion rate, water vapor generation rate and water vapor temperature of each combustion equipment into the combustion efficiency calculation formula The combustion efficiency E of combustion equipment a is obtained a, a is the combustion equipment number, a=1,2......i, i>2, T′ is the basic steam temperature in the database, d is the specific heat capacity of steam in the database, is the calorific value of the fuel in the database.

[0035] The inflow water vapor temperature T″ of each water vapor device is obtained through the sensors of each water vapor device b , Exhaust water vapor temperature T″′ b , Exhaust water vapor flow rate H b and turbine blade speed X′ b , substitute the inlet steam temperature, outlet steam temperature, exhaust steam flow rate and turbine blade speed of each steam device into the steam efficiency calculation formula Get the water vapor efficiency F of each water vapor equipment b , b=1,2...j,j>2,θ′ b It is the mechanical energy required for the turbine blades of each steam equipment to rotate one circle.

[0036] The rotor speed of each power generation device and the power generation rate of each power generation device are acquired through sensors of each power generation device.

[0037] In a specific embodiment, the equipment efficiency information is analyzed, and the specific analysis process is as follows: the historical combustion efficiency of each combustion device when the combustion device is normal is obtained from the database, and the maximum and minimum values ​​of the historical combustion efficiency of each combustion device when normal are set as the upper and lower limits of the standard combustion efficiency range, respectively, so as to set the standard combustion efficiency range of each combustion device. If the combustion efficiency of a combustion device does not belong to the standard combustion efficiency range, it indicates that the combustion device is an abnormal device. Similarly, each abnormal device in each water vapor device is obtained.

[0038] The historical rotor speeds and historical electric energy generation rates of each power generation equipment when the power generation equipment is normal are obtained from the database, and the historical maximum and minimum electric energy generation rates of each historical rotor speed are recorded as the upper and lower limits of the normal electric energy generation rate range, respectively. The normal electric energy generation rate range of each historical rotor speed is set accordingly, and then the normal electric energy generation rate range of each power generation equipment is obtained according to the rotor speed of each power generation equipment. If the electric energy generation rate of a certain power generation equipment does not belong to the normal electric energy generation rate range of the power generation equipment, it indicates that the power generation equipment is an abnormal equipment.

[0039] It should be noted that, when a thermal power unit fails, if it is analyzed that all devices are not abnormal devices, then all devices are marked as abnormal devices.

[0040] In a specific embodiment, the fault inspection data is collected, and the specific collection process is as follows: activate the temperature sensor, pressure sensor and sound characteristic information collector of each abnormal device, and collect the characteristic information of temperature, pressure and sound of each collection point of each abnormal device through the temperature sensor, pressure sensor and sound characteristic information collector.

[0041] It should be noted that sound feature information includes but is not limited to the frequency, pitch, loudness, length, suddenness, decay rate and energy of the sound. The more types of sound feature information collected, the more accurate the analysis result.

[0042] In a specific embodiment, the fault inspection data is analyzed, and the specific analysis process is as follows: the fault inspection data includes characteristic information of temperature, pressure and sound of each collection point of each abnormal device, and the temperature and pressure of each collection point of each device when it is normal are obtained from the database, and the maximum and minimum temperatures of each collection point of each device when it is normal are set as the upper and lower limits of the normal temperature range, so as to set the normal temperature range of each collection point of each device. Similarly, the normal pressure range of each collection point of each device is set.

[0043] The characteristic information of each abnormal sound of each collection point of each device is obtained from the database. If the temperature of a collection point of an abnormal device does not belong to the normal temperature range of the collection point of the abnormal device, the pressure does not belong to the normal pressure range of the collection point of the abnormal device, or the characteristic information of the sound corresponds to the characteristic information of a certain abnormal sound of the collection point of the abnormal device, it indicates that the collection point of the abnormal device is abnormal.

[0044] It should be noted that the characteristic information of the sound of an abnormal device matches the characteristic information of an abnormal sound at the collection point of the abnormal device, indicating that the data of various characteristic information of the sound are equal to the data of various characteristic information of the abnormal sound.

[0045] If any collection point of an abnormal device is abnormal, it indicates that the abnormal device is a faulty device. If all collection points of an abnormal device are normal, it indicates that the abnormal device is a suspected faulty device.

[0046] In a specific embodiment, the drone inspection data is collected, and the specific collection process is as follows: the drone inspection data includes the ambient temperature of each inspection point of each suspected faulty device, the concentration of each abnormal gas and the characteristic data of the ambient sound, the historical number of failures of each suspected faulty device is obtained from the database, and the drone inspects each suspected faulty device in the order of the historical number of failures of each suspected faulty device from large to small, and the ambient temperature of each inspection point of each suspected faulty device, the concentration of each abnormal gas and the characteristic data of the ambient sound are inspected through the temperature, gas and sound sensors of the drone.

[0047] It should be noted that by setting the inspection order of drones, the priority of inspection of equipment with high failure rates is increased, the possibility of inspecting faulty equipment first is increased, the time to inspect faulty equipment is reduced, and the energy waste caused by faulty equipment is reduced.

[0048] In a specific embodiment, the drone inspection data is analyzed, and the specific analysis process is as follows: the ambient temperature of each inspection point and the concentration of each abnormal gas when each device is normal are obtained from the database, and the maximum and minimum values ​​of the ambient temperature of each inspection point when each device is normal are set as the upper and lower limits of the normal ambient temperature range, respectively, so as to set the normal ambient temperature range of each inspection point of each device. Similarly, the normal concentration range of each abnormal gas at each inspection point of each device is obtained.

[0049] The characteristic information of abnormal environmental sounds at each inspection point of each device is obtained from the database. If the ambient temperature of a certain inspection point of a suspected faulty device does not belong to the normal ambient temperature range of the inspection point of the suspected faulty device, the abnormal gas concentration does not belong to the normal range of the abnormal gas concentration at the inspection point of the suspected faulty device, or the characteristic information of the environmental sounds corresponds to and matches the characteristic information of the abnormal environmental sounds at the inspection point of the suspected faulty device, it indicates that the inspection point of the suspected faulty device is abnormal.

[0050] If any inspection point of a suspected faulty device is abnormal, it indicates that the suspected faulty device is a faulty device. If all inspection points of a suspected faulty device are normal, it indicates that the suspected faulty device is a normal device.

[0051] The daily inspection module is used to analyze the fault history records in the database, obtain the abnormal index of the thermal power unit, and then set the daily inspection frequency.

[0052] In a specific embodiment, the fault history records in the database are analyzed, and the specific analysis process is as follows: the fault history records in the database include the number of faults of each combustion device A a 、Number of failures of each water vapor equipment B b and the number of failures of each power generation equipment C c , c is the number of the power generation equipment, c=1,2......k, k>2, substitute the fault history records in the database into the fault index calculation formula The failure index α of the thermal power unit is obtained, A′, B′ and C′ are the preset standard combustion equipment failure times, standard steam equipment failure times and standard power generation equipment failure times, ε a ,φ b and are the preset weight factors of each combustion equipment, each water vapor equipment and each power generation equipment, φb >0 and η 1 , η 2 and η 3 are the preset weight factors of combustion equipment failure, steam equipment failure and power generation equipment failure, respectively. 1 >0,η 2 >0,η 3 >0,η 1 +η 2 +η 3 =1.

[0053] It should be noted that the preset standard combustion equipment failure times, standard steam equipment failure times and standard power generation equipment failure times are the average values ​​of the combustion equipment failure times, the steam equipment failure times and the power generation equipment failure times in the database, respectively. The maximum combustion rate J of each combustion equipment is obtained from the database. amax , Get the maximum value X′ of the turbine blade speed of each steam equipment from the database bmax and the mechanical energy θ′ required for the turbine blades of each water-steam equipment to rotate one circle b , Get the maximum power generation rate W of each power generation equipment from the database c , Obtain the maximum number of combustion equipment failures R, the maximum number of steam equipment failures S, and the maximum number of power generation equipment failures T during each adjacent maintenance period from the database.

[0054] In a specific embodiment, the daily inspection frequency is set, and the specific setting process is as follows: the average value is calculated for each historical production capacity of each fault index in the database to obtain the standard production capacity of each fault index, and the standard production capacity of the thermal power unit is obtained according to the fault index of the thermal power unit. If the standard production capacity of the thermal power unit is less than the preset production capacity, the thermal power unit is overhauled.

[0055] It should be noted that the preset production capacity is set by the staff and can be ten megawatts or one gigawatt, etc.

[0056] If the thermal power unit is not under maintenance, the daily inspection frequencies corresponding to the fault indexes are obtained from the database, and the daily inspection frequencies of the thermal power unit are obtained according to the fault indexes of the thermal power unit.

[0057] It should be noted that the average duration of each failure interval of each device with each fault index in the database is calculated to obtain the standard interval duration of equipment failure corresponding to each fault index, and the inverse of the standard interval duration of equipment failure corresponding to each fault index is recorded as the daily inspection frequency corresponding to each fault index.

[0058] If the thermal power plant is under maintenance, the daily inspection frequency after maintenance corresponding to each fault index obtained in the database is obtained, and the daily inspection frequency of the thermal power plant is obtained according to the fault index of the thermal power plant.

[0059] It should be noted that the average value of the shortest failure duration after maintenance of each device with each fault index in the database is calculated to obtain the standard duration of failure after maintenance of the equipment corresponding to each fault index. The inverse of the standard duration of failure after maintenance of the equipment corresponding to each fault index is recorded as the daily inspection frequency after maintenance corresponding to each fault index. When a device fails after maintenance, it indicates that the equipment is not counted as repaired equipment.

[0060] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A smart inspection system for thermal power units, characterized in that: Includes the following modules: The production analysis module is used to collect production data and determine whether the thermal power unit has a fault based on the production data. If a fault occurs, a fault inspection is performed; The fault inspection module is used to collect equipment efficiency information during fault inspection, analyze the equipment efficiency information, obtain each abnormal equipment, collect fault inspection data through each sensor of each abnormal equipment, analyze the fault inspection data, divide each abnormal equipment into each faulty equipment and each suspected faulty equipment, mark each faulty equipment, prompt the staff, collect drone inspection data, mark each faulty equipment in each suspected faulty equipment according to the drone inspection data, and prompt the staff; The daily inspection module is used to analyze the fault history records in the database, obtain the abnormal index of the thermal power unit, and then set the daily inspection frequency.

2. According to claim 1, a smart inspection system for thermal power units is characterized in that: The specific process of judging whether a thermal power unit fails is as follows: The production data includes the current fuel consumption rate and the current electricity production rate. The historical electricity production rates under each historical fuel consumption rate are obtained from the database, and then the historical electricity production rates under the current fuel consumption rate are obtained. The maximum and minimum values ​​of the historical electricity production rates under the current fuel consumption rate are respectively recorded as the upper and lower limits of the current electricity preset production rate range, so as to set the current electricity preset production rate range. If the current electricity production rate belongs to the current electricity preset production rate range, it indicates that the current electricity production is normal. Otherwise, if the current electricity production rate does not belong to the current electricity preset production rate range, it indicates that the current electricity production is abnormal and requires fault inspection.

3. The intelligent inspection system for thermal power units according to claim 1 is characterized in that: The specific collection process of the equipment efficiency information is as follows: The equipment efficiency information includes the combustion efficiency of each combustion equipment, the water vapor efficiency of each water vapor equipment, the rotor speed of each power generation equipment and the power generation rate of each power generation equipment; The fuel combustion rate, water vapor generation rate and water vapor temperature of each combustion device are obtained through sensors of each combustion device, and the fuel combustion rate, water vapor generation rate and water vapor temperature of each combustion device are substituted into a combustion efficiency calculation formula to obtain the combustion efficiency of each combustion device; The inlet water vapor temperature, outlet water vapor temperature, exhaust water vapor flow rate and turbine blade speed of each water vapor device are obtained through sensors of each water vapor device, and the inlet water vapor temperature, outlet water vapor temperature, exhaust water vapor flow rate and turbine blade speed of each water vapor device are substituted into the water vapor efficiency calculation formula to obtain the water vapor efficiency of each water vapor device; The rotor speed and power generation rate of each power generation device are acquired through sensors of each power generation device.

4. A smart inspection system for thermal power units according to claim 3, characterized in that: The equipment efficiency information is analyzed, and the specific analysis process is as follows: Obtain the historical combustion efficiency of each combustion device when the combustion device is normal from the database, set the maximum and minimum historical combustion efficiency of each combustion device when normal as the upper limit and lower limit of the standard combustion efficiency range, and set the standard combustion efficiency range of each combustion device. If the combustion efficiency of a combustion device does not belong to the standard combustion efficiency range, it indicates that the combustion device is an abnormal device. Similarly, obtain the abnormal devices in each water vapor device; The historical rotor speeds and historical electric energy generation rates of each power generation equipment when the power generation equipment is normal are obtained from the database, and the historical maximum and minimum electric energy generation rates of each historical rotor speed are recorded as the upper and lower limits of the normal electric energy generation rate range, respectively. The normal electric energy generation rate range of each historical rotor speed is set accordingly, and then the normal electric energy generation rate range of each power generation equipment is obtained according to the rotor speed of each power generation equipment. If the electric energy generation rate of a certain power generation equipment does not belong to the normal electric energy generation rate range of the power generation equipment, it indicates that the power generation equipment is an abnormal equipment.

5. The intelligent inspection system for thermal power units according to claim 1 is characterized in that: The fault inspection data is analyzed, and the specific analysis process is as follows: The fault inspection data includes the characteristic information of temperature, pressure and sound of each collection point of each abnormal device. The temperature and pressure of each collection point of each device when it is normal are obtained from the database. The maximum and minimum temperature of each collection point of each device when it is normal are set as the upper and lower limits of the normal temperature range, so as to set the normal temperature range of each collection point of each device. Similarly, the normal pressure range of each collection point of each device is set. Acquire characteristic information of each abnormal sound of each collection point of each device from the database. If the temperature of a collection point of an abnormal device does not belong to the normal temperature range of the collection point of the abnormal device, the pressure does not belong to the normal pressure range of the collection point of the abnormal device, or the characteristic information of the sound corresponds to the characteristic information of a certain abnormal sound of the collection point of the abnormal device, it indicates that the collection point of the abnormal device is abnormal; If any collection point of an abnormal device is abnormal, it indicates that the abnormal device is a faulty device. If all collection points of an abnormal device are normal, it indicates that the abnormal device is a suspected faulty device.

6. A smart inspection system for thermal power units according to claim 4, characterized in that: The specific collection process of collecting drone inspection data is as follows: The drone inspection data includes the ambient temperature of each inspection point of each suspected faulty device, the concentration of each abnormal gas and the characteristic data of the ambient sound. The historical number of failures of each suspected faulty device is obtained from the database. The drone inspects each suspected faulty device in order of the historical number of failures of each suspected faulty device from large to small. The drone uses the temperature, gas and sound sensors to inspect the ambient temperature of each inspection point of each suspected faulty device, the concentration of each abnormal gas and the characteristic data of the ambient sound.

7. A smart inspection system for thermal power units according to claim 6, characterized in that: The specific analysis process of analyzing the drone inspection data is as follows: Obtain from the database the ambient temperatures of each inspection point and the concentrations of each abnormal gas when each device is normal, set the maximum and minimum ambient temperatures of each inspection point when each device is normal as the upper and lower limits of the normal ambient temperature range, and set the normal ambient temperature range of each inspection point of each device accordingly. Similarly, obtain the normal concentration range of each abnormal gas at each inspection point of each device; Acquire characteristic information of abnormal environmental sounds at each inspection point of each device from the database. If the ambient temperature at a certain inspection point of a suspected faulty device does not belong to the normal ambient temperature range of the inspection point of the suspected faulty device, the concentration of a certain abnormal gas does not belong to the normal range of the abnormal gas concentration at the inspection point of the suspected faulty device, or the characteristic information of the environmental sounds corresponds to and matches the characteristic information of the abnormal environmental sounds at the inspection point of the suspected faulty device, it indicates that the inspection point of the suspected faulty device is abnormal; If any inspection point of a suspected faulty device is abnormal, it indicates that the suspected faulty device is a faulty device. If all inspection points of a suspected faulty device are normal, it indicates that the suspected faulty device is a normal device.

8. The intelligent inspection system for thermal power units according to claim 1, characterized in that: The fault history records in the database are analyzed, and the specific analysis process is as follows: The fault history records in the database include the number of faults of each combustion equipment A a 、Number of failures of each water vapor equipment B b and the number of failures of each power generation equipment C c , a is the combustion equipment number, a=1,2......i, i>2, b is the steam equipment number, b=1,2......j, j>2, c is the power generation equipment number, c=1,2......k, k>2, substitute the fault history records in the database into the fault index calculation formula The failure index α of the thermal power unit is obtained, A′, B′ and C′ are the preset standard combustion equipment failure times, standard steam equipment failure times and standard power generation equipment failure times, ε a ,φ b and are the preset weight factors of each combustion equipment, each water vapor equipment and the power generation equipment, ε a >0 and φ b >0 and and η1, η2 and η3 are respectively the preset weight factors of combustion equipment failure, steam equipment failure and power generation equipment failure. η1>0, η2>0, η3>0, η1+η2+η3=1.

9. A smart inspection system for thermal power units according to claim 8, characterized in that: The specific setting process of setting the daily inspection frequency is as follows: The average value is used to calculate the historical production capacity of each fault index in the database, and the standard production capacity of each fault index is obtained. According to the fault index of the thermal power unit, the standard production capacity of the thermal power unit is obtained. If the standard production capacity of the thermal power unit is less than the preset production capacity, the thermal power unit is overhauled; If the thermal power unit is not under maintenance, the daily inspection frequency corresponding to each fault index is obtained from the database, and the daily inspection frequency of the thermal power unit is obtained according to the fault index of the thermal power unit; If the thermal power plant is under maintenance, the daily inspection frequency after maintenance corresponding to each fault index obtained in the database is obtained, and the daily inspection frequency of the thermal power plant is obtained according to the fault index of the thermal power plant.

10. The intelligent inspection system for thermal power units according to claim 1, characterized in that: A database is used to store fault history records, historical electricity production rates corresponding to historical fuel consumption rates, historical combustion efficiencies of combustion equipment when the combustion equipment is normal, historical rotor speeds and historical electricity generation rates of power generation equipment when the power generation equipment is normal, temperatures of collection points of each equipment when it is normal, pressures of temperatures of each collection point of each equipment when it is normal, characteristic information of abnormal sounds of each collection point of each equipment, the number of historical failures of each suspected faulty equipment, ambient temperatures of each inspection point of each equipment when it is normal, concentrations of abnormal gases of each inspection point of each equipment when it is normal, characteristic information of abnormal ambient sounds of each inspection point of each equipment, historical production capacities of each fault index, daily inspection frequencies corresponding to each fault index, and daily inspection frequencies after maintenance corresponding to each fault index.

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