Hoist operation fault diagnosis system and method based on artificial intelligence

Abstract

The invention relates to the technical field of fault diagnosis data processing, and discloses a hoist operation fault diagnosis system and method based on artificial intelligence, and the system comprises a hoist hydraulic oil characteristic parameter obtaining module, a hoist hydraulic oil characteristic parameter state analysis module, and a hoist fault state diagnosis output module. The characteristic state parameters of hydraulic oil in the fault state of the hoist are compared with the characteristic state parameters of acidity, viscosity and water content of the hydraulic oil through an artificial intelligence recognition algorithm, the operation fault state of the hydraulic hoist is accurately diagnosed, and efficient and accurate online monitoring of the operation state of the hydraulic hoist is achieved. And the fault state diagnosis result of the hoist is pushed to the hoist fault monitoring end on line through the Internet of Things and is fed back and displayed through a display screen, so that the real-time visual feedback of the operation state diagnosis result of the hydraulic hoist is realized, and the real-time and scientific feedback of the operation fault of the hydraulic hoist is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis data processing, and in particular to a gate hoist operation fault diagnosis system and method based on artificial intelligence. Background Art

[0002] The gate hoist is a large-scale hydraulic machinery product. The gate hoist is related to the normal operation of hydraulic structures. In addition to meeting the design requirements of general lifting machinery, it is of special significance to work safely and reliably and to operate flexibly and conveniently. The gate hoist can be classified according to different characteristics: ① According to the operating power, it can be divided into manpower, electricity, and hydraulic power. ② According to the power transmission method, it can be divided into mechanical transmission and hydraulic transmission. Mechanical transmission is further divided into belt transmission, chain transmission, gear transmission and combined transmission. Hydraulic transmission can be divided into oil pressure transmission and hydraulic transmission. ③ According to the installation status of the hoist, it can be divided into fixed and mobile types. China often names the hoist in this classification. ④ According to the connection method between the gate and the hoist, it can be divided into flexible, rigid and semi-rigid connections. ⑤ According to the characteristic category of the gate, it can be divided into plane gate hoist, arc gate hoist and miter gate operating machinery, etc. Usually, the hoist is divided into screw hoist, chain hoist, winch hoist and hydraulic hoist. Among them, the hydraulic hoist is a hoist that connects the piston rod to the gate and uses liquid pressure as power to push the piston to lift the gate. The liquid is generally mineral oil, so it is often called an oil pressure hoist. High-pressure oil is transported by an oil pump through a pipeline. The hydraulic hoist has a simple body structure, small footprint, stable transmission, convenient control, high manufacturing precision, and is widely used to open and close various types of gates. The main components of the hydraulic hoist are: piston rod, hydraulic cylinder, oil supply and discharge pipeline system, and oil pump motor unit. According to the force condition of the piston rod, it is divided into a single-acting hydraulic hoist and a double-acting hydraulic hoist. If the gate can be closed by descending by its own weight, a single-acting hydraulic hoist can be selected; when the gate cannot descend smoothly by its own weight and pressure is required on the upper part of the gate body to close, a double-acting hydraulic hoist is selected. The force condition of each component of the double-acting hydraulic hoist and the hydraulic operating system are relatively complex, but the layout is relatively compact, and the additional weight required for the gate to descend can be omitted. It is mostly used for the operation of submerged high-pressure gates. Among them, the hydraulic gate hoist will denature due to the increase of impurities in the hydraulic oil and the aging of the sealing components of the hydraulic cylinder during use. The denaturation of the hydraulic oil will corrode the mechanical structure and sealing components of the hydraulic gate hoist and cause hydraulic oil leakage, thereby resulting in insufficient hydraulic pressure of the hydraulic gate hoist and inability to operate normally. The existing diagnosis of the operating status of the hydraulic gate hoist is mostly carried out through manual regular maintenance to detect the operating status of the hydraulic system of the hydraulic gate hoist. It cannot accurately diagnose the operating status of the hydraulic gate hoist based on relevant parameters such as the acidity, viscosity, and water content of the hydraulic oil, nor can it accurately and visually analyze and diagnose the fault status of the hydraulic gate hoist.

[0003] The Chinese invention patent with the publication number CN116186946B discloses a hydraulic system fault diagnosis method and system based on a diagnostic model, which adopts S1, obtains the actual parameters of the hydraulic system, and constructs a visual interactive virtual environment: S2, uses the transfer learning algorithm to construct a diagnostic model for hydraulic system fault diagnosis: S3, collects operating data in real time, and synchronizes it to the virtual environment and the diagnostic model; S4, uses the diagnostic model to perform fault diagnosis on the real-time state of the hydraulic system: S5, synchronizes the diagnostic results to the virtual environment for fault location and visual feedback: S6, retrieves the repair plan in the knowledge base based on the feedback results, and provides interactive operation recommendations. The present invention constructs a visual virtual environment for the hydraulic system, monitors the operating state of the hydraulic system in real time, and uses two rounds of data cleaning to improve data quality and algorithm efficiency, thereby improving the efficiency of the hydraulic system and the fault diagnosis system. However, the above technical solution cannot truly analyze the operating fault point of the hydraulic system through the working pressure, flow, temperature, and liquid level parameters of the hydraulic oil, which reduces the fault diagnosis results of the hydraulic system. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the problem that the existing diagnosis of the operating status of the hydraulic gate hoist is mostly carried out through manual regular maintenance to detect the operating status of the hydraulic system of the hydraulic gate hoist, and the operating status of the hydraulic gate hoist cannot be accurately diagnosed based on relevant parameters such as the acidity, viscosity, and water content of the hydraulic oil, and the fault status of the hydraulic gate hoist cannot be accurately and visually analyzed and diagnosed, the above purposes are achieved by accurately collecting hydraulic oil characteristic parameters, intelligently analyzing the state quantity of hydraulic oil characteristic parameters, accurately diagnosing and analyzing the results of hydraulic gate hoist operation fault diagnosis, and intuitively and visually feedbacking the results of hydraulic gate hoist operation fault diagnosis.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical scheme: a gate hoist operation fault diagnosis method based on artificial intelligence, the method comprising the following steps:

[0008] S1, collecting hydraulic oil acidity characteristic data, hydraulic oil viscosity characteristic data, hydraulic oil water content characteristic data;

[0009] S2, respectively performing data mean processing on the hydraulic oil acidity characteristic data, the hydraulic oil viscosity characteristic data, and the hydraulic oil water content characteristic data, measuring the hydraulic oil acidity mean, the hydraulic oil viscosity mean, and the hydraulic oil water content mean, and generating hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data;

[0010] S3, using a data search algorithm to compare the hydraulic oil acidity characteristic mean data with the hydraulic oil acidity characteristic data in different states, and analyzing the hydraulic oil acidity characteristic state data;

[0011] S4. When the analysis of the hydraulic oil acidity characteristic state data is completed, a data search algorithm is used to compare the hydraulic oil viscosity characteristic mean data with the hydraulic oil viscosity characteristic data of different states to obtain the hydraulic oil viscosity characteristic state data;

[0012] S5. When the analysis of the hydraulic oil viscosity characteristic state data is completed, a data search algorithm is used to compare the hydraulic oil water content characteristic mean data with the hydraulic oil water content characteristic data of different states to analyze the hydraulic oil water content characteristic state data;

[0013] S6, matching the hydraulic oil acidity characteristic state data, the hydraulic oil viscosity characteristic state data, and the hydraulic oil water content characteristic state data with the hoist fault state hydraulic oil characteristic state data respectively, and analyzing and generating hoist fault state diagnosis result data;

[0014] S7. Push and feed back the gate hoist fault status diagnosis result data to the gate hoist fault monitoring terminal.

[0015] Preferably, the steps of collecting the hydraulic oil acidity characteristic data, the hydraulic oil viscosity characteristic data, and the hydraulic oil water content characteristic data are as follows:

[0016] S11, measure the acidity of the hydraulic oil in the hydraulic oil hoist online through the hydraulic oil acidity sensor and establish the hydraulic oil acidity characteristic data set A=(a1,…,a i ,…,a α ), i=1,2,3,…,α; where a i represents the i-th hydraulic oil acidity characteristic data collected, α represents the maximum number of hydraulic oil acidity characteristic data, a i The unit is mgKOH / g;

[0017] The hydraulic oil viscosity sensor is used to measure the hydraulic oil viscosity in the hydraulic oil hoist online and establish the hydraulic oil viscosity characteristic data set B = (b1,…,b j ,…,b β ), j = 1, 2, 3, ..., β; where b j represents the jth hydraulic oil viscosity characteristic data collected, β represents the maximum number of hydraulic oil viscosity characteristic data, b j The unit is Pa·s;

[0018] The hydraulic oil water content detector is used to measure the hydraulic oil water content in the hydraulic oil hoist online and establish the hydraulic oil water content characteristic data set C = (c1,…,c o ,…,c χ ), o = 1, 2, 3, ..., χ; where c o represents the oth hydraulic oil water content characteristic data collected, χ represents the maximum number of hydraulic oil water content characteristic data, c o The unit is PPM.

[0019] The present invention uses a hydraulic oil acidity sensor, a hydraulic oil viscosity sensor, and a hydraulic oil water content detector to accurately obtain the acidity, viscosity, and water content characteristic parameters of the hydraulic oil inside the hydraulic gate hoist online, thereby achieving the effect of real-time online and accurate detection of the hydraulic oil characteristics inside the hydraulic gate hoist.

[0020] Preferably, the hydraulic oil acidity characteristic data, the hydraulic oil viscosity characteristic data, and the hydraulic oil water content characteristic data are respectively subjected to data mean processing, and the hydraulic oil acidity mean, hydraulic oil viscosity mean, and hydraulic oil water content mean are measured to generate hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data. The operating steps are as follows:

[0021] S21, based on the hydraulic oil acidity characteristic data a in the hydraulic oil acidity characteristic data set A. i The mean value formula is used to calculate the characteristic mean value data of hydraulic oil acidity in The unit is mgKOH / g;

[0022] According to the hydraulic oil viscosity characteristic data b in the hydraulic oil viscosity characteristic data set B j The hydraulic oil viscosity characteristic mean data is calculated using the mean formula in The unit is Pa·s;

[0023] According to the hydraulic oil water content characteristic data c in the hydraulic oil water content characteristic data set C o The characteristic mean data of hydraulic oil water content is calculated by the mean formula in The unit is PPM.

[0024] The present invention performs mean processing on the characteristic parameters of acidity, viscosity and water content of hydraulic oil through the mean formula, so as to achieve the collection accuracy of the characteristic parameters of acidity, viscosity and water content of hydraulic oil and ensure the reliability analysis of the operating status of the hydraulic hoist.

[0025] Preferably, the steps of using a data search algorithm to compare the hydraulic oil acidity characteristic mean data with the hydraulic oil acidity characteristic data in different states to analyze the hydraulic oil acidity characteristic state data are as follows:

[0026] S31, establish the acidity characteristic data set of hydraulic oil in different states A'=([a'1,a'2) 正常 , [a'2,a'3) 过渡 , [a'3,a'4] 异常 ), where [a'1,a'2) 正常 Indicates the hydraulic oil acidity characteristic data interval [a'1, a'2) corresponding to the hydraulic oil acidity characteristic state in the hydraulic gate hoist when the hydraulic oil acidity characteristic state is normal, a'1 and a'2 represent the left and right end values ​​in the hydraulic oil acidity characteristic data interval [a'1, a'2) when the hydraulic oil acidity characteristic state is normal, indicating that the hydraulic oil acidity is suitable for the normal operation of the hydraulic gate hoist;

[0027] [a'2,a'3) 过渡 Indicates that the acidity characteristic state of the hydraulic oil in the hydraulic gate hoist corresponds to the acidity characteristic data interval of different states of the hydraulic oil [a'2, a'3), a'2 and a'3 represent the left and right end values ​​in the acidity characteristic data interval of different states of the hydraulic oil [a'2, a'3), and the acidity characteristic state of the hydraulic oil is transitional, indicating that the acidity of the hydraulic oil is in a transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist;

[0028] [a'3,a'4] 异常 Indicates the hydraulic oil acidity characteristic data interval [a'3, a'4] corresponding to the hydraulic oil acidity characteristic data interval [a'3, a'4] when the hydraulic oil acidity characteristic state in the hydraulic gate hoist is abnormal, a'3 and a'4 represent the left and right end values ​​in the hydraulic oil acidity characteristic data interval [a'3, a'4] when the hydraulic oil acidity characteristic state is abnormal, indicating that the hydraulic oil acidity is not suitable for the normal operation of the hydraulic gate hoist, and a'1, a'2, a'3, and a'4 are all mgKOH / g;

[0029] S32, using a depth-restricted search algorithm to calculate the hydraulic oil acidity characteristic mean data The acidity characteristic values ​​of the hydraulic oil are compared with the acidity characteristic data intervals of the hydraulic oil in different states in the hydraulic oil acidity characteristic data set A', and the mean value data of the hydraulic oil acidity characteristic is analyzed. The corresponding hydraulic oil acidity characteristic state is constructed to obtain hydraulic oil acidity characteristic state data A".

[0030] The present invention analyzes the characteristic state of hydraulic oil acidity through a depth-restricted search algorithm in combination with the characteristic mean data of hydraulic oil acidity and characteristic data of hydraulic oil acidity in different states, thereby achieving the effect of intelligent and precise monitoring of the characteristic state of hydraulic oil acidity inside the hydraulic gate hoist.

[0031] Preferably, when the analysis of the hydraulic oil acidity characteristic state data is completed, the hydraulic oil viscosity characteristic mean data is compared with the hydraulic oil viscosity characteristic data of different states by using a data search algorithm, and the operation steps for analyzing the hydraulic oil viscosity characteristic state data are as follows:

[0032] S41, establish the characteristic data set B'=([b'1,b'2) of the viscosity of hydraulic oil in different states 正常 ,[b'2,b'3) 过渡 , [b'3,b'4] 异常 ), where [b'1,b'2) 正常 Indicates the hydraulic oil viscosity characteristic data interval [b'1, b'2) corresponding to the hydraulic oil viscosity characteristic state in the hydraulic hoist, b'1 and b'2 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'1, b'2) in different states, and the hydraulic oil viscosity characteristic state being normal indicates that the hydraulic oil viscosity is suitable for the normal operation of the hydraulic hoist;

[0033] [b'2,b'3) 过渡 Indicates that the characteristic state of the viscosity of the hydraulic oil in the hydraulic gate hoist is transitional, corresponding to the hydraulic oil viscosity characteristic data interval [b'2, b'3) in different states, b'2 and b'3 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'2, b'3) in different states, and the hydraulic oil viscosity characteristic state is transitional, indicating that the hydraulic oil viscosity is in a transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist;

[0034] [b'3,b'4] 异常 Indicates the hydraulic oil viscosity characteristic data interval [b'3, b'4] corresponding to the hydraulic oil viscosity characteristic state in the hydraulic gate hoist when the hydraulic oil viscosity characteristic state is abnormal, b'3 and b'4 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'3, b'4] when the hydraulic oil viscosity characteristic state is abnormal, indicating that the hydraulic oil viscosity is not suitable for the normal operation of the hydraulic gate hoist, and b'1, b'2, b'3, and b'4 are all Pa·s;

[0035] S42, when the analysis of the hydraulic oil acidity characteristic state data A" is completed, the hydraulic oil viscosity characteristic mean data A" is searched by using a depth-limited search algorithm. The viscosity characteristic values ​​of the hydraulic oil are compared with the viscosity characteristic data intervals of the hydraulic oil in different states in the hydraulic oil viscosity characteristic data set B′, and the mean value data of the hydraulic oil viscosity characteristic is analyzed. The corresponding hydraulic oil viscosity characteristic state is constructed to obtain hydraulic oil viscosity characteristic state data B".

[0036] The present invention analyzes the viscosity characteristic state of the hydraulic oil by combining the hydraulic oil viscosity characteristic mean data and the viscosity characteristic data of the hydraulic oil in different states through a depth-restricted search algorithm, thereby achieving the effect of online real-time detection of the viscosity characteristic state of the hydraulic oil inside the hydraulic gate hoist.

[0037] Preferably, when the analysis of the hydraulic oil viscosity characteristic state data is completed, the hydraulic oil water content characteristic mean data and the hydraulic oil water content characteristic data of different states are compared with each other by using a data search algorithm, and the operation steps for analyzing the hydraulic oil water content characteristic state data are as follows:

[0038] S51, establish the characteristic data set C'=([c'1,c'2) of the water content of hydraulic oil in different states 正常 , [c'2,c'3) 过渡 , [c'3,c'4] 异常 ), where [c'1,c'2) 正常 Indicates the characteristic data interval [c'1, c'2) of the hydraulic oil water content in different states corresponding to the hydraulic hoist when the characteristic state of the hydraulic oil water content in the hydraulic hoist is normal, c'1 and c'2 represent the left and right end values ​​in the characteristic data interval [c'1, c'2) of the hydraulic oil water content in different states, and the hydraulic oil water content characteristic state being normal indicates that the hydraulic oil water content is suitable for the normal operation of the hydraulic hoist;

[0039] [c'2,c'3) 过渡 Indicates that the characteristic state of the water content of the hydraulic oil in the hydraulic gate hoist is transitional, corresponding to the characteristic data interval of the water content of the hydraulic oil in different states [c'2, c'3), c'2 and c'3 represent the left and right end values ​​in the characteristic data interval of the water content of the hydraulic oil in different states [c'2, c'3), and the characteristic state of the water content of the hydraulic oil is transitional, indicating that the water content of the hydraulic oil is in a transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist;

[0040] [c'3,c'4] 异常Indicates the hydraulic oil water content characteristic data interval [c'3, c'4] corresponding to the hydraulic oil water content characteristic state in the hydraulic gate hoist when the characteristic state of the hydraulic oil water content is abnormal, c'3 and c'4 represent the left and right end values ​​in the hydraulic oil water content characteristic data interval [c'3, c'4] in different states, and the hydraulic oil water content characteristic state is abnormal, indicating that the hydraulic oil water content is not suitable for the normal operation of the hydraulic gate hoist, and c'1, c'2, c'3, and c'4 are all PPM;

[0041] S52, when the analysis of the hydraulic oil viscosity characteristic state data B" is completed, the hydraulic oil water content characteristic mean data is searched by using a depth-limited search algorithm. The characteristic data interval of the hydraulic oil water content in different states in the hydraulic oil water content characteristic data set C' is compared with the characteristic data interval of the hydraulic oil water content in different states, and the characteristic mean data of the hydraulic oil water content is analyzed. The corresponding hydraulic oil water content characteristic state is obtained and the hydraulic oil water content characteristic state data C" is constructed.

[0042] The present invention analyzes the characteristic state of the hydraulic oil water content by combining the characteristic mean data of the hydraulic oil water content and the characteristic data of the water content of the hydraulic oil in different states through a depth-restricted search algorithm, so as to achieve the effect of accurately identifying the characteristic state of the hydraulic oil water content inside the hydraulic hoist.

[0043] Preferably, the hydraulic oil acidity characteristic state data, the hydraulic oil viscosity characteristic state data, and the hydraulic oil water content characteristic state data are matched with the hoist fault state hydraulic oil characteristic state data by hydraulic oil characteristic state keywords, and the operating steps of analyzing and generating hoist fault state diagnosis result data are as follows:

[0044] S61, establish the hydraulic oil characteristic state data set D of the hoist fault state = (A" 异常 ,B” 异常 ,C” 异常 ), where A” 异常 Indicates that the hydraulic oil acidity characteristic data A” is the hydraulic oil characteristic state data of the hoist fault state corresponding to the abnormality; B” 异常 Indicates that the hydraulic oil viscosity characteristic data B" is the hydraulic oil characteristic state data of the hoist fault state corresponding to the abnormality; C" 异常 The characteristic data C" indicating the water content of the hydraulic oil is the characteristic state data of the hydraulic oil in the fault state of the gate hoist corresponding to the abnormality; the characteristic state data of the hydraulic oil in the fault state of the gate hoist indicates the characteristic state data of the acidity, viscosity and water content of the hydraulic oil corresponding to the fault state of the gate hoist when the gate hoist is in operation or will continue to operate;

[0045] S62, using a data recognition algorithm, the hydraulic oil acidity characteristic state data A", the hydraulic oil viscosity characteristic state data B", and the hydraulic oil water content characteristic state data C" are respectively identified with the hoist fault state hydraulic oil characteristic state data A" in the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The specific steps of the data recognition algorithm to perform keyword matching of the characteristic state of hydraulic oil are as follows:

[0046] S621, when the algorithm is initialized, the maximum number of iterations T is updated, and the hydraulic oil characteristic state data set D of the gate hoist fault state is updated. All pheromones in the search space are initialized to ψ0;

[0047] S622, algorithm iteration, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D in the hoist fault state combines the hydraulic oil acidity characteristic state data A", the hydraulic oil viscosity characteristic state data B", and the hydraulic oil water content characteristic state data C" with the hoist fault state hydraulic oil characteristic state data A" 异常 , B” 异常 , C” 异常 Matching, hydraulic oil characteristic state search ant search matching hoist fault state hydraulic oil characteristic state data A" 异常 , B” 异常 , C” 异常 The pheromone content on all trajectories of is reduced, and the pheromone content reduction rate is calculated as follows: where ψ t+1 (u,v) indicates that after t+1 iterations, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D in the hoist fault state combines the hydraulic oil acidity characteristic state data A”, hydraulic oil viscosity characteristic state data B”, and hydraulic oil water content characteristic state data C” with the hoist fault state hydraulic oil characteristic state data A” respectively. 异常 , B” 异常 , C” 异常 The pheromone content reduction rate of the coordinate position (u, v) passed by the matching; ψ t (u,v) indicates that after t iterations, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D in the hoist fault state combines the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", and hydraulic oil water content characteristic state data C" with the hoist fault state hydraulic oil characteristic state data A". 异常 , B” 异常 , C” 异常 The pheromone content reduction rate of the coordinate position (u, v) passed by the match; Δψ k(u,v) indicates that the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" are respectively combined with the hoist fault state hydraulic oil characteristic state data A" in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The pheromone change amount after the coordinate position (u, v) is searched by the kth hydraulic oil characteristic state search ant during the matching process; Θ represents the maximum number of hydraulic oil characteristic state search ants, and ζ is a random number (0,1);

[0048] S623, the hydraulic oil characteristic state search ant searches for the pheromone change of all trajectory lengths after this round of iteration in the search space of the hydraulic oil characteristic state data set D in the hoist fault state according to the trajectory length constructed by itself; the pheromone change calculation formula is as follows:

[0049] in represents the trajectory length, R k It means that the kth hydraulic oil characteristic state search ant has combined the hydraulic oil acidity characteristic state data A”, hydraulic oil viscosity characteristic state data B”, and hydraulic oil water content characteristic state data C” with the hoist fault state hydraulic oil characteristic state data A” in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The coordinate position set passed by the match is searched for the trajectory length with the largest pheromone change value, that is, the hoist fault state hydraulic oil characteristic state data A" that best matches the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", and hydraulic oil water content characteristic state data C" are matched in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 ;

[0050] S624, when the algorithm meets the maximum number of iterations T, the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" and hoist fault state hydraulic oil characteristic state data A" are output 异常 , B” 异常 , C” 异常 If the matching result is not reached, then continue to execute step S622 until the maximum number of iterations T is met;

[0051] S63, outputting the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" and the hoist fault state hydraulic oil characteristic state data A" in step S624. 异常 , B” 异常 , C” 异常 The matching results are analyzed to generate the hoist fault status diagnosis result data E;

[0052] When A" and A" 异常 , B” and B” 异常 , C” and C” 异常 If none of them are matched successfully, it means that the characteristic states of hydraulic oil acidity, viscosity and water content are all suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E means that there is no fault;

[0053] When A" and A" 异常 If the match is successful, it means that the characteristic state of the hydraulic oil acidity is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault;

[0054] When B” and B” 异常 If the match is successful, it means that the characteristic state of the hydraulic oil viscosity is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault;

[0055] When C” and C” 异常 If the match is successful, it means that the characteristic state of the water content in the hydraulic oil is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault;

[0056] When A" and A" 异常 , B” and B” 异常 , C” and C” 异常 All of them are matched successfully, indicating that the characteristic states of the hydraulic oil acidity, viscosity, and water content are not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that a fault exists.

[0057] The present invention compares the characteristic state parameters of the hydraulic oil in the fault state of the gate hoist with the characteristic state parameters of the hydraulic oil acidity, viscosity and water content through an ant-optimized artificial intelligence recognition algorithm, thereby achieving the effect of efficient and accurate online monitoring of the operating state of the hydraulic gate hoist.

[0058] Preferably, the operation steps of pushing and feeding back the gate hoist fault status diagnosis result data to the gate hoist fault monitoring terminal are as follows:

[0059] S71. Push the gate hoist fault status diagnosis result data E to the gate hoist fault monitoring terminal through the Internet of Things communication network, and the gate hoist fault monitoring terminal displays and feeds back the gate hoist fault status diagnosis result data E through a display screen.

[0060] The present invention receives the parameters of the gate hoist fault status diagnosis result through the gate hoist fault monitoring terminal and displays the parameters through the display screen, thereby achieving the effect of real-time visual feedback of the hydraulic gate hoist operation status diagnosis result.

[0061] A system for implementing the artificial intelligence-based gate hoist operation fault diagnosis method, the system comprising a gate hoist hydraulic oil characteristic parameter acquisition module, a gate hoist hydraulic oil characteristic parameter state analysis module, and a gate hoist fault state diagnosis output module;

[0062] The hoist hydraulic oil characteristic parameter acquisition module includes a hydraulic oil acidity characteristic parameter acquisition unit, a hydraulic oil viscosity characteristic parameter acquisition unit, a hydraulic oil water content characteristic parameter acquisition unit, and a hydraulic oil characteristic parameter numerical processing unit;

[0063] The hydraulic oil acidity characteristic parameter acquisition unit acquires hydraulic oil acidity characteristic data through a hydraulic oil acidity sensor; the hydraulic oil viscosity characteristic parameter acquisition unit acquires hydraulic oil viscosity characteristic data through a hydraulic viscosity sensor; the hydraulic oil water content characteristic parameter acquisition unit acquires hydraulic oil water content characteristic data through a hydraulic oil water content detector; the hydraulic oil characteristic parameter numerical processing unit performs data mean processing on the hydraulic oil acidity characteristic data, the hydraulic oil viscosity characteristic data, and the hydraulic oil water content characteristic data, respectively, measures the hydraulic oil acidity mean, the hydraulic oil viscosity mean, and the hydraulic oil water content mean, and generates hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data;

[0064] The hoist hydraulic oil characteristic parameter state analysis module includes a hydraulic oil acidity characteristic parameter storage unit in different states, a hoist hydraulic oil acidity characteristic state analysis unit, a hydraulic oil viscosity characteristic parameter storage unit in different states, a hoist hydraulic oil viscosity characteristic state analysis unit, a hydraulic oil water content characteristic parameter storage unit in different states, and a hoist hydraulic oil water content characteristic state analysis unit;

[0065] The hydraulic oil acidity characteristic parameter storage unit in different states is used to store the acidity characteristic data of the hydraulic oil in different states; the hydraulic oil acidity characteristic state analysis unit of the gate hoist adopts a data search algorithm to compare the hydraulic oil acidity characteristic mean data with the hydraulic oil acidity characteristic data in different states, and analyze the hydraulic oil acidity characteristic state data; the hydraulic oil viscosity characteristic parameter storage unit in different states is used to store the hydraulic oil viscosity characteristic data in different states; the gate hoist hydraulic oil viscosity characteristic state analysis unit adopts a data search algorithm to compare the hydraulic oil viscosity characteristic mean data with the hydraulic oil viscosity characteristic data in different states, and analyze the hydraulic oil viscosity characteristic state data; the hydraulic oil water content characteristic parameter storage unit in different states is used to store the hydraulic oil water content characteristic data; the gate hoist hydraulic oil water content characteristic state analysis unit adopts a data search algorithm to compare the hydraulic oil water content characteristic mean data with the hydraulic oil water content characteristic data in different states, and analyze the hydraulic oil water content characteristic state data;

[0066] The hoist fault state diagnosis output module includes a hoist fault state hydraulic oil characteristic state storage unit, a hoist fault state diagnosis unit, and a hoist fault state diagnosis result output unit;

[0067] The gate hoist fault state hydraulic oil characteristic state storage unit is used to store the gate hoist fault state hydraulic oil characteristic state data; the gate hoist fault state diagnosis unit uses an artificial intelligence recognition algorithm to match the hydraulic oil acidity characteristic state data, the hydraulic oil viscosity characteristic state data, and the hydraulic oil water content characteristic state data with the gate hoist fault state hydraulic oil characteristic state data respectively to perform hydraulic oil characteristic state keyword matching, and analyzes and generates gate hoist fault state diagnosis result data; the gate hoist fault state diagnosis result output unit pushes the gate hoist fault state diagnosis result data to the gate hoist fault monitoring terminal and feeds back through the display screen.

[0068] (III) Beneficial effects

[0069] The present invention provides a gate hoist operation fault diagnosis system and method based on artificial intelligence, which has the following beneficial effects:

[0070] 1. Through the cooperation of the hydraulic oil acidity characteristic parameter acquisition unit, the hydraulic oil viscosity characteristic parameter acquisition unit, and the hydraulic oil water content characteristic parameter acquisition unit, the acidity, viscosity, and water content characteristic parameters of the hydraulic oil inside the hydraulic gate hoist are accurately acquired online, providing data support for the real-time operation status analysis of the hydraulic gate hoist, realizing accurate online detection of the operation status of the hydraulic gate hoist, and improving the service life of the hydraulic gate hoist; the hydraulic oil characteristic parameter numerical processing unit performs mean processing on the acidity, viscosity, and water content characteristic parameters of the hydraulic oil, thereby improving the accuracy of the acquisition of the acidity, viscosity, and water content characteristic parameters of the hydraulic oil, and ensuring the reliability of the operation status analysis of the hydraulic gate hoist.

[0071] 2. Through the cooperation of the hydraulic oil acidity characteristic parameter storage unit in different states and the gate hoist hydraulic oil acidity characteristic state analysis unit, the data search algorithm is used to accurately and efficiently analyze the hydraulic oil acidity characteristic state, so as to realize the intelligent and accurate monitoring of the hydraulic oil acidity characteristic state inside the hydraulic gate hoist, and improve the operation safety of the hydraulic gate hoist; the hydraulic oil viscosity characteristic parameter storage unit in different states and the gate hoist hydraulic oil viscosity characteristic state analysis unit cooperate with each other, and use the data search algorithm to digitize and standardize the hydraulic oil viscosity characteristic state, so as to realize the online real-time detection of the hydraulic oil viscosity characteristic state inside the hydraulic gate hoist, and reduce the operation failure rate of the hydraulic gate hoist; the hydraulic oil water content characteristic parameter storage unit in different states and the gate hoist hydraulic oil water content characteristic state analysis unit cooperate with each other, and the hydraulic oil water content characteristic state is combined with the data search algorithm to independently and scientifically identify the hydraulic oil water content characteristic state, so as to realize the accurate identification of the hydraulic oil water content characteristic state inside the hydraulic gate hoist, and improve the use stability and economy of the hydraulic gate hoist.

[0072] 3. Through the cooperation of the hydraulic oil characteristic state storage unit of the gate hoist fault state and the gate hoist fault state diagnosis unit, the artificial intelligence recognition algorithm is used to compare the characteristic state parameters of the hydraulic oil of the gate hoist fault state with the characteristic state parameters of the hydraulic oil acidity, viscosity, and water content, so as to accurately diagnose the operating fault state of the hydraulic gate hoist, and realize efficient and accurate online monitoring of the operating state of the hydraulic gate hoist; the gate hoist fault state diagnosis result output unit uses the Internet of Things to push the gate hoist fault state diagnosis result online to the gate hoist fault monitoring terminal, and displays it through the display screen, so as to realize real-time visual feedback of the hydraulic gate hoist operating state diagnosis result, thereby improving the real-time and scientific feedback of the hydraulic gate hoist operating failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A schematic diagram of a module of a gate hoist operation fault diagnosis system based on artificial intelligence provided by the present invention;

[0074] Figure 2 A flow chart of the gate hoist operation fault diagnosis method based on artificial intelligence provided by the present invention. DETAILED DESCRIPTION

[0075] 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.

[0076] The embodiments of the gate hoist operation fault diagnosis system and method based on artificial intelligence are as follows:

[0077] See also Figure 1 - Figure 2 , a gate hoist operation fault diagnosis method based on artificial intelligence, the method comprises the following steps:

[0078] S1, collecting hydraulic oil acidity characteristic data, hydraulic oil viscosity characteristic data, hydraulic oil water content characteristic data;

[0079] S2, respectively performing data mean processing on the hydraulic oil acidity characteristic data, the hydraulic oil viscosity characteristic data, and the hydraulic oil water content characteristic data, measuring the hydraulic oil acidity mean, the hydraulic oil viscosity mean, and the hydraulic oil water content mean, and generating hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data;

[0080] S3, using a data search algorithm to compare the hydraulic oil acidity characteristic mean data with the hydraulic oil acidity characteristic data in different states, and analyzing the hydraulic oil acidity characteristic state data;

[0081] S4. When the analysis of the hydraulic oil acidity characteristic state data is completed, a data search algorithm is used to compare the hydraulic oil viscosity characteristic mean data with the hydraulic oil viscosity characteristic data of different states to analyze the hydraulic oil viscosity characteristic state data;

[0082] S5. When the analysis of the hydraulic oil viscosity characteristic state data is completed, a data search algorithm is used to compare the hydraulic oil water content characteristic mean data with the hydraulic oil water content characteristic data of different states to analyze the hydraulic oil water content characteristic state data;

[0083] S6, matching the hydraulic oil acidity characteristic state data, the hydraulic oil viscosity characteristic state data, and the hydraulic oil water content characteristic state data with the hoist fault state hydraulic oil characteristic state data respectively, and analyzing and generating hoist fault state diagnosis result data;

[0084] S7. Push and feed back the gate hoist fault status diagnosis result data to the gate hoist fault monitoring terminal.

[0085] For further information, see Figure 1 - Figure 2 The steps for collecting the characteristic data of hydraulic oil acidity, hydraulic oil viscosity, and hydraulic oil water content are as follows:

[0086] S11, measure the acidity of the hydraulic oil in the hydraulic oil hoist online through the hydraulic oil acidity sensor and establish the hydraulic oil acidity characteristic data set A=(a1,...,a i ,...,a α ), i=1,2,3,...,α; where a i represents the i-th hydraulic oil acidity characteristic data collected, α represents the maximum number of hydraulic oil acidity characteristic data, a i The unit is mgKOH / g;

[0087] The hydraulic oil viscosity sensor is used to measure the hydraulic oil viscosity in the hydraulic oil hoist online and establish the hydraulic oil viscosity characteristic data set B = (b1,...,b j ,...,b β ), j = 1, 2, 3, ..., β; where b j represents the jth hydraulic oil viscosity characteristic data collected, β represents the maximum number of hydraulic oil viscosity characteristic data, b j The unit is Pa·s;

[0088] The hydraulic oil water content detector is used to measure the hydraulic oil water content in the hydraulic oil hoist online and establish the hydraulic oil water content characteristic data set C = (c1,…,c o ,…,c χ ), o = 1, 2, 3, ..., χ; where c o represents the oth hydraulic oil water content characteristic data collected, χ represents the maximum number of hydraulic oil water content characteristic data, c o The unit is PPM.

[0089] The steps of performing data mean processing on the hydraulic oil acidity characteristic data, hydraulic oil viscosity characteristic data, and hydraulic oil water content characteristic data respectively, measuring the hydraulic oil acidity mean, hydraulic oil viscosity mean, hydraulic oil water content mean, and generating hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data are as follows:

[0090] S21, based on the hydraulic oil acidity characteristic data a in the hydraulic oil acidity characteristic data set A. i The mean value formula is used to calculate the characteristic mean value data of hydraulic oil acidity in The unit is mgKOH / g;

[0091] Based on the hydraulic oil viscosity characteristic data b in the hydraulic oil viscosity characteristic data set B j The hydraulic oil viscosity characteristic mean data is calculated using the mean formula in The unit is Pa·s;

[0092] Based on the hydraulic oil water content characteristic data set C, the hydraulic oil water content characteristic data c o The characteristic mean data of hydraulic oil water content is calculated by the mean formula in The unit is PPM.

[0093] Through the cooperation of the hydraulic oil acidity characteristic parameter acquisition unit, the hydraulic oil viscosity characteristic parameter acquisition unit, and the hydraulic oil water content characteristic parameter acquisition unit, the acidity, viscosity, and water content characteristic parameters of the hydraulic oil inside the hydraulic gate hoist are accurately acquired online, providing data support for the real-time operation status analysis of the hydraulic gate hoist, realizing accurate online detection of the operation status of the hydraulic gate hoist, and improving the service life of the hydraulic gate hoist; the hydraulic oil characteristic parameter numerical processing unit performs mean processing on the acidity, viscosity, and water content characteristic parameters of the hydraulic oil, thereby improving the accuracy of the acquisition of the acidity, viscosity, and water content characteristic parameters of the hydraulic oil, and ensuring the reliability of the operation status analysis of the hydraulic gate hoist.

[0094] For further information, see Figure 1 - Figure 2 , the data search algorithm is used to compare the hydraulic oil acidity characteristic mean data with the hydraulic oil acidity characteristic data of different states to analyze the hydraulic oil acidity characteristic state data. The operation steps are as follows:

[0095] S31, establish the acidity characteristic data set of hydraulic oil in different states A'=([a'1,a'2) 正常 , [a'2,a'3) 过渡 , [a'3,a'4] 异常 ), where [a'1,a'2) 正常 Indicates the hydraulic oil acidity characteristic data interval [a'1, a'2) corresponding to the hydraulic oil acidity characteristic state in the hydraulic gate hoist when the hydraulic oil acidity characteristic state is normal, a'1 and a'2 represent the left and right end values ​​in the hydraulic oil acidity characteristic data interval [a'1, a'2) when the hydraulic oil acidity characteristic state is normal, indicating that the hydraulic oil acidity is suitable for the normal operation of the hydraulic gate hoist;

[0096] [a'2,a'3) 过渡Indicates that the acidity characteristic state of the hydraulic oil in the hydraulic gate hoist corresponds to the acidity characteristic data interval of different states of the hydraulic oil [a'2, a'3), a'2 and a'3 represent the left and right end values ​​of the acidity characteristic data interval of different states of the hydraulic oil [a'2, a'3), and the acidity characteristic state of the hydraulic oil is transitional, indicating that the acidity of the hydraulic oil is in the transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist;

[0097] [a'3,a'4] 异常 Indicates the hydraulic oil acidity characteristic data interval [a'3, a'4] corresponding to the hydraulic oil acidity characteristic data interval [a'3, a'4] when the hydraulic oil acidity characteristic state in the hydraulic gate hoist is abnormal. a'3 and a'4 represent the left and right end values ​​in the hydraulic oil acidity characteristic data interval [a'3, a'4]. The hydraulic oil acidity characteristic state is abnormal, indicating that the hydraulic oil acidity is not suitable for the normal operation of the hydraulic gate hoist. a'1, a'2, a'3, and a'4 are all mgKOH / g;

[0098] S32, using the depth-restricted search algorithm to convert the hydraulic oil acidity characteristic mean data The acidity characteristic values ​​of hydraulic oil are compared with the acidity characteristic data intervals of hydraulic oil in different states in the hydraulic oil acidity characteristic data set A', and the mean value data of hydraulic oil acidity characteristic is analyzed. The corresponding hydraulic oil acidity characteristic state is constructed to obtain hydraulic oil acidity characteristic state data A".

[0099] When the hydraulic oil acidity characteristic state data analysis is completed, the hydraulic oil viscosity characteristic mean data is compared with the hydraulic oil viscosity characteristic data of different states by using the data search algorithm. The operation steps for analyzing the hydraulic oil viscosity characteristic state data are as follows:

[0100] S41, establish the characteristic data set B'=([b'1,b'2) of the viscosity of hydraulic oil in different states 正常 ,[b'2,b'3) 过渡 , [b'3,b'4] 异常 ), where [b'1,b'2) 正常 Indicates the hydraulic oil viscosity characteristic data interval [b'1, b'2) corresponding to the hydraulic oil viscosity characteristic state in the hydraulic gate hoist when the hydraulic oil viscosity characteristic state is normal, b'1 and b'2 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'1, b'2) when the hydraulic oil viscosity characteristic state is normal, indicating that the hydraulic oil viscosity is suitable for the normal operation of the hydraulic gate hoist;

[0101] [b'2,b'3) 过渡Indicates that the characteristic state of the hydraulic oil viscosity in the hydraulic gate hoist is transitional, corresponding to the hydraulic oil viscosity characteristic data interval [b'2, b'3) in different states, b'2 and b'3 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'2, b'3) in different states, and the hydraulic oil viscosity characteristic state is transitional, indicating that the hydraulic oil viscosity is in a transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist;

[0102] [b'3,b'4] 异常 Indicates the hydraulic oil viscosity characteristic data interval [b'3, b'4] corresponding to the hydraulic oil viscosity characteristic state in the hydraulic gate hoist when the hydraulic oil viscosity characteristic state is abnormal. b'3 and b'4 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'3, b'4]. The hydraulic oil viscosity characteristic state is abnormal, indicating that the hydraulic oil viscosity is not suitable for the normal operation of the hydraulic gate hoist. b'1, b'2, b'3, and b'4 are all Pa·s.

[0103] S42, when the analysis of the hydraulic oil acidity characteristic state data A" is completed, the hydraulic oil viscosity characteristic mean data is converted into The viscosity characteristic values ​​of the hydraulic oil are compared with the viscosity characteristic data intervals of the hydraulic oil in different states in the hydraulic oil viscosity characteristic data set B', and the mean value data of the hydraulic oil viscosity characteristic is analyzed. The corresponding hydraulic oil viscosity characteristic state is constructed to obtain hydraulic oil viscosity characteristic state data B".

[0104] When the hydraulic oil viscosity characteristic state data analysis is completed, the data search algorithm is used to compare the hydraulic oil water content characteristic mean data with the hydraulic oil water content characteristic data of different states to obtain the hydraulic oil water content characteristic value. The operation steps for analyzing the hydraulic oil water content characteristic state data are as follows:

[0105] S51, establish the characteristic data set C'=([c'1,c'2) of the water content of hydraulic oil in different states 正常 , [c'2,c'3) 过渡 , [c'3,c'4] 异常 ), where [c'1,c'2) 正常 Indicates the characteristic data interval [c'1, c'2) of the hydraulic oil water content in different states corresponding to the hydraulic hoist when the characteristic state of the hydraulic oil water content in the hydraulic hoist is normal, c'1 and c'2 represent the left and right end values ​​in the characteristic data interval [c'1, c'2) of the hydraulic oil water content in different states, and the normal characteristic state of the hydraulic oil water content indicates that the hydraulic oil water content is suitable for the normal operation of the hydraulic hoist;

[0106] [c'2,c'3) 过渡Indicates that the characteristic state of the water content of the hydraulic oil in the hydraulic gate hoist is transitional, corresponding to the characteristic data interval of the water content of the hydraulic oil in different states [c'2, c'3), c'2 and c'3 represent the left and right end values ​​in the characteristic data interval of the water content of the hydraulic oil in different states [c'2, c'3), and the characteristic state of the water content of the hydraulic oil is transitional, indicating that the water content of the hydraulic oil is in the transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist;

[0107] [c'3,c'4] 异常 Indicates the hydraulic oil water content characteristic data interval [c'3, c'4] corresponding to the hydraulic oil water content characteristic state in the hydraulic gate hoist when the characteristic state of the hydraulic oil water content is abnormal. c'3 and c'4 represent the left and right end values ​​in the hydraulic oil water content characteristic data interval [c'3, c'4]. The hydraulic oil water content characteristic state is abnormal, indicating that the hydraulic oil water content is not suitable for the normal operation of the hydraulic gate hoist. c'1, c'2, c'3, and c'4 are all PPM.

[0108] S52, when the analysis of the hydraulic oil viscosity characteristic state data B" is completed, the hydraulic oil water content characteristic mean data is converted into The characteristic data interval of hydraulic oil water content in different states in the hydraulic oil water content characteristic data set C' is compared with the characteristic data interval of hydraulic oil water content in different states, and the characteristic mean data of hydraulic oil water content is analyzed. The corresponding hydraulic oil water content characteristic state is obtained and the hydraulic oil water content characteristic state data C" is constructed.

[0109] Through the cooperation between the hydraulic oil acidity characteristic parameter storage unit in different states and the gate hoist hydraulic oil acidity characteristic state analysis unit, the data search algorithm is used to accurately and efficiently analyze the hydraulic oil acidity characteristic state, so as to realize the intelligent and accurate monitoring of the hydraulic oil acidity characteristic state inside the hydraulic gate hoist, and improve the operation safety of the hydraulic gate hoist; the hydraulic oil viscosity characteristic parameter storage unit in different states and the gate hoist hydraulic oil viscosity characteristic state analysis unit cooperate with each other, and use the data search algorithm to digitize and standardize the hydraulic oil viscosity characteristic state, so as to realize the online real-time detection of the hydraulic oil viscosity characteristic state inside the hydraulic gate hoist, and reduce the operation failure rate of the hydraulic gate hoist; the hydraulic oil water content characteristic parameter storage unit in different states and the gate hoist hydraulic oil water content characteristic state analysis unit cooperate with each other, and the hydraulic oil water content characteristic state is combined with the data search algorithm to independently and scientifically identify the hydraulic oil water content characteristic state, so as to realize the accurate identification of the hydraulic oil water content characteristic state inside the hydraulic gate hoist, and improve the use stability and economy of the hydraulic gate hoist.

[0110] For further information, see Figure 1 - Figure 2, the hydraulic oil acidity characteristic state data, hydraulic oil viscosity characteristic state data, hydraulic oil water content characteristic state data and the hoist fault state hydraulic oil characteristic state data are matched with the hydraulic oil characteristic state data respectively, and the operation steps of analyzing and generating the hoist fault state diagnosis result data are as follows:

[0111] S61, establish the hydraulic oil characteristic state data set D of the hoist fault state = (A" 异常 ,B” 异常 ,C” 异常 ), where A” 异常 Indicates that the hydraulic oil acidity characteristic data A” is the hydraulic oil characteristic state data of the hoist fault state corresponding to the abnormality; B” 异常 Indicates that the hydraulic oil viscosity characteristic data B" is the hydraulic oil characteristic state data of the hoist fault state corresponding to the abnormality; C" 异常 The characteristic data C” indicating the water content of hydraulic oil is the characteristic state data of hydraulic oil in the fault state of the gate hoist corresponding to the abnormality; the characteristic state data of hydraulic oil in the fault state of the gate hoist indicates the characteristic state data of the acidity, viscosity and water content of the hydraulic oil corresponding to the fault state of the gate hoist when the gate hoist is in operation or will continue to operate;

[0112] S62, using a data recognition algorithm, the hydraulic oil acidity characteristic state data A", the hydraulic oil viscosity characteristic state data B", and the hydraulic oil water content characteristic state data C" are respectively identified with the hoist fault state hydraulic oil characteristic state data A" in the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The specific steps of the data recognition algorithm to perform keyword matching of the characteristic state of hydraulic oil are as follows:

[0113] S621, when the algorithm is initialized, the maximum number of iterations T is updated, and the hydraulic oil characteristic state data set D of the gate hoist fault state is updated. All pheromones in the search space are initialized to ψ0;

[0114] S622, algorithm iteration, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D in the hoist fault state combines the hydraulic oil acidity characteristic state data A", the hydraulic oil viscosity characteristic state data B", and the hydraulic oil water content characteristic state data C" with the hoist fault state hydraulic oil characteristic state data A" 异常 , B” 异常 , C” 异常 Matching, hydraulic oil characteristic state search ant search matching hoist fault state hydraulic oil characteristic state data A" 异常 , B” 异常 , C” 异常The pheromone content on all trajectories of is reduced, and the pheromone content reduction rate is calculated as follows: where ψ t+1 (u,v) indicates that after t+1 iterations, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D in the hoist fault state combines the hydraulic oil acidity characteristic state data A”, hydraulic oil viscosity characteristic state data B”, and hydraulic oil water content characteristic state data C” with the hoist fault state hydraulic oil characteristic state data A” respectively. 异常 , B” 异常 , C” 异常 The pheromone content reduction rate of the coordinate position (u, v) passed by the matching; ψ t (u,v) indicates that after t iterations, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D in the hoist fault state combines the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", and hydraulic oil water content characteristic state data C" with the hoist fault state hydraulic oil characteristic state data A". 异常 , B” 异常 , C” 异常 The pheromone content reduction rate of the coordinate position (u, v) passed by the match; Δψ k (u,v) indicates that the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" are respectively combined with the hoist fault state hydraulic oil characteristic state data A" in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The pheromone change amount after the coordinate position (u, v) is searched by the kth hydraulic oil characteristic state search ant during the matching process; Θ represents the maximum number of hydraulic oil characteristic state search ants, and ζ is a random number (0,1);

[0115] S623, the hydraulic oil characteristic state search ant searches for the pheromone change of all trajectory lengths after this round of iteration in the search space of the hydraulic oil characteristic state data set D in the hoist fault state according to the trajectory length constructed by itself; the pheromone change calculation formula is as follows:

[0116] in represents the trajectory length, R k It means that the kth hydraulic oil characteristic state search ant has combined the hydraulic oil acidity characteristic state data A”, hydraulic oil viscosity characteristic state data B”, and hydraulic oil water content characteristic state data C” with the hoist fault state hydraulic oil characteristic state data A” in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常, C” 异常 The coordinate position set passed by the match is searched for the trajectory length with the largest pheromone change value, that is, the hoist fault state hydraulic oil characteristic state data A" that best matches the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", and hydraulic oil water content characteristic state data C" are matched in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 ;

[0117] S624, when the algorithm meets the maximum number of iterations T, the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" and hoist fault state hydraulic oil characteristic state data A" are output 异常 , B” 异常 , C” 异常 If the matching result is not reached, then continue to execute step S622 until the maximum number of iterations T is met;

[0118] S63, outputting the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" and the hoist fault state hydraulic oil characteristic state data A" in step S624. 异常 , B” 异常 , C” 异常 The matching results are analyzed to generate the hoist fault status diagnosis result data E;

[0119] When A" and A" 异常 , B” and B” 异常 , C” and C” 异常 If none of them are matched successfully, it means that the characteristic states of hydraulic oil acidity, viscosity and water content are all suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E means that there is no fault;

[0120] When A" and A" 异常 If the match is successful, it means that the characteristic state of the hydraulic oil acidity is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault;

[0121] When B” and B” 异常 If the match is successful, it means that the characteristic state of the hydraulic oil viscosity is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault;

[0122] When C” and C” 异常 If the match is successful, it means that the characteristic state of the water content in the hydraulic oil is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault;

[0123] When A" and A"异常 , B” and B” 异常 , C” and C” 异常 All of them are matched successfully, indicating that the characteristic states of the hydraulic oil acidity, viscosity, and water content are not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that a fault exists.

[0124] The steps for pushing and feeding back the hoist fault status diagnosis result data to the hoist fault monitoring terminal are as follows:

[0125] S71. Push the gate hoist fault status diagnosis result data E to the gate hoist fault monitoring terminal through the Internet of Things communication network. The gate hoist fault monitoring terminal displays and feeds back the gate hoist fault status diagnosis result data E through a display screen.

[0126] Through the cooperation of the gate hoist fault state hydraulic oil characteristic state storage unit and the gate hoist fault state diagnosis unit, the artificial intelligence recognition algorithm is used to compare the gate hoist fault state hydraulic oil characteristic state parameters with the hydraulic oil acidity, viscosity, and water content characteristic state parameters, so as to accurately diagnose the operating fault state of the hydraulic gate hoist, and realize efficient and accurate online monitoring of the operating state of the hydraulic gate hoist; the gate hoist fault state diagnosis result output unit uses the Internet of Things to push the gate hoist fault state diagnosis result online to the gate hoist fault monitoring terminal, and displays it through the display screen, so as to realize real-time visual feedback of the hydraulic gate hoist operating state diagnosis result, thereby improving the real-time and scientific feedback of the hydraulic gate hoist operating failure.

[0127] A system for realizing a gate hoist operation fault diagnosis method based on artificial intelligence, the system comprising a gate hoist hydraulic oil characteristic parameter acquisition module, a gate hoist hydraulic oil characteristic parameter state analysis module, and a gate hoist fault state diagnosis output module;

[0128] The gate hoist hydraulic oil characteristic parameter acquisition module includes a hydraulic oil acidity characteristic parameter acquisition unit, a hydraulic oil viscosity characteristic parameter acquisition unit, a hydraulic oil water content characteristic parameter acquisition unit, and a hydraulic oil characteristic parameter numerical processing unit;

[0129] A hydraulic oil acidity characteristic parameter acquisition unit, which acquires hydraulic oil acidity characteristic data through a hydraulic oil acidity sensor; a hydraulic oil viscosity characteristic parameter acquisition unit, which acquires hydraulic oil viscosity characteristic data through a hydraulic viscosity sensor; a hydraulic oil water content characteristic parameter acquisition unit, which acquires hydraulic oil water content characteristic data through a hydraulic oil water content detector; a hydraulic oil characteristic parameter numerical processing unit, which performs data mean processing on hydraulic oil acidity characteristic data, hydraulic oil viscosity characteristic data, and hydraulic oil water content characteristic data, respectively, measures the hydraulic oil acidity mean, hydraulic oil viscosity mean, and hydraulic oil water content mean, and generates hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data;

[0130] The gate hoist hydraulic oil characteristic parameter state analysis module includes a hydraulic oil acidity characteristic parameter storage unit in different states, a gate hoist hydraulic oil acidity characteristic state analysis unit, a hydraulic oil viscosity characteristic parameter storage unit in different states, a gate hoist hydraulic oil viscosity characteristic state analysis unit, a hydraulic oil water content characteristic parameter storage unit in different states, and a gate hoist hydraulic oil water content characteristic state analysis unit;

[0131] A hydraulic oil acidity characteristic parameter storage unit in different states is used to store the acidity characteristic data of hydraulic oil in different states; a gate hoist hydraulic oil acidity characteristic state analysis unit uses a data search algorithm to compare the hydraulic oil acidity characteristic mean value data with the hydraulic oil acidity characteristic data in different states, and analyze the hydraulic oil acidity characteristic state data; a hydraulic oil viscosity characteristic parameter storage unit in different states is used to store the hydraulic oil viscosity characteristic data in different states; a gate hoist hydraulic oil viscosity characteristic state analysis unit uses a data search algorithm to compare the hydraulic oil viscosity characteristic mean value data with the hydraulic oil viscosity characteristic data in different states, and analyze the hydraulic oil viscosity characteristic state data; a hydraulic oil water content characteristic parameter storage unit in different states is used to store the hydraulic oil water content characteristic data; a gate hoist hydraulic oil water content characteristic state analysis unit uses a data search algorithm to compare the hydraulic oil water content characteristic mean value data with the hydraulic oil water content characteristic data in different states, and analyze the hydraulic oil water content characteristic state data;

[0132] The hoist fault state diagnosis output module includes a hoist fault state hydraulic oil characteristic state storage unit, a hoist fault state diagnosis unit, and a hoist fault state diagnosis result output unit;

[0133] The gate hoist fault state hydraulic oil characteristic state storage unit is used to store the gate hoist fault state hydraulic oil characteristic state data; the gate hoist fault state diagnosis unit uses an artificial intelligence recognition algorithm to match the hydraulic oil acidity characteristic state data, the hydraulic oil viscosity characteristic state data, and the hydraulic oil water content characteristic state data with the gate hoist fault state hydraulic oil characteristic state data respectively to perform hydraulic oil characteristic state keyword matching, and analyzes and generates the gate hoist fault state diagnosis result data; the gate hoist fault state diagnosis result output unit pushes the gate hoist fault state diagnosis result data to the gate hoist fault monitoring terminal and feeds back through the display screen.

[0134] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A gate hoist operation fault diagnosis method based on artificial intelligence, characterized in that: The method comprises the following steps: S1, collecting hydraulic oil acidity characteristic data, hydraulic oil viscosity characteristic data, hydraulic oil water content characteristic data; S2, respectively performing data mean processing on the hydraulic oil acidity characteristic data, the hydraulic oil viscosity characteristic data, and the hydraulic oil water content characteristic data, measuring the hydraulic oil acidity mean, the hydraulic oil viscosity mean, and the hydraulic oil water content mean, and generating hydraulic oil acidity characteristic mean data, hydraulic oil viscosity characteristic mean data, and hydraulic oil water content characteristic mean data; S3, using a data search algorithm to compare the hydraulic oil acidity characteristic mean data with the hydraulic oil acidity characteristic data in different states, and analyzing the hydraulic oil acidity characteristic state data; S4. When the analysis of the hydraulic oil acidity characteristic state data is completed, a data search algorithm is used to compare the hydraulic oil viscosity characteristic mean data with the hydraulic oil viscosity characteristic data of different states to obtain the hydraulic oil viscosity characteristic state data; S5. When the analysis of the hydraulic oil viscosity characteristic state data is completed, a data search algorithm is used to compare the hydraulic oil water content characteristic mean data with the hydraulic oil water content characteristic data of different states to analyze the hydraulic oil water content characteristic state data; S6, matching the hydraulic oil acidity characteristic state data, the hydraulic oil viscosity characteristic state data, and the hydraulic oil water content characteristic state data with the hoist fault state hydraulic oil characteristic state data respectively, and analyzing and generating hoist fault state diagnosis result data; S7. Push and feed back the gate hoist fault status diagnosis result data to the gate hoist fault monitoring terminal.

2. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 1 is characterized in that: The S1 comprises the following steps: S11, measure the acidity of the hydraulic oil in the hydraulic oil hoist online through the hydraulic oil acidity sensor and establish the hydraulic oil acidity characteristic data set A=(a1,…,a i ,…,a α ), i=1,2,3,…,α; where a i represents the i-th hydraulic oil acidity characteristic data collected, α represents the maximum value of the hydraulic oil acidity characteristic data, a i The unit is mgKOH / g; The hydraulic oil viscosity sensor is used to measure the hydraulic oil viscosity in the hydraulic oil hoist online and establish the hydraulic oil viscosity characteristic data set B = (b1,…,b j ,…,b β ), j = 1, 2, 3, ..., β; where b j represents the jth hydraulic oil viscosity characteristic data collected, β represents the maximum number of hydraulic oil viscosity characteristic data, b j The unit is Pa·s; The hydraulic oil water content detector is used to measure the hydraulic oil water content in the hydraulic oil hoist online and establish the hydraulic oil water content characteristic data set C = (c1,…,c o ,…,c χ ), o = 1, 2, 3, ..., χ; where c o represents the oth hydraulic oil water content characteristic data collected, χ represents the maximum number of hydraulic oil water content characteristic data, c o The unit is PPM.

3. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 2 is characterized in that: The S2 comprises the following steps: S21, based on the hydraulic oil acidity characteristic data a in the hydraulic oil acidity characteristic data set A. i The mean value formula is used to calculate the characteristic mean value data of hydraulic oil acidity in The unit is mgKOH / g; According to the hydraulic oil viscosity characteristic data b in the hydraulic oil viscosity characteristic data set B j The hydraulic oil viscosity characteristic mean data is calculated using the mean formula in The unit is Pa·s; According to the hydraulic oil water content characteristic data c in the hydraulic oil water content characteristic data set C o The characteristic mean data of hydraulic oil water content is calculated by the mean formula in The unit is PPM.

4. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 3 is characterized in that: The S3 comprises the following steps: S31, establish the acidity characteristic data set of hydraulic oil in different states A'=([a'1,a'2) 正常 , [a'2,a'3) 过渡 , [a'3,a'4] 异常 ), where [a'1,a'2) 正常 Indicates the hydraulic oil acidity characteristic data interval [a'1, a'2) corresponding to the hydraulic oil acidity characteristic state in the hydraulic gate hoist when the hydraulic oil acidity characteristic state is normal, a'1 and a'2 represent the left and right end values ​​in the hydraulic oil acidity characteristic data interval [a'1, a'2) when the hydraulic oil acidity characteristic state is normal, indicating that the hydraulic oil acidity is suitable for the normal operation of the hydraulic gate hoist; [a'2,a'3) 过渡 Indicates that the acidity characteristic state of the hydraulic oil in the hydraulic gate hoist corresponds to the acidity characteristic data interval of different states of the hydraulic oil [a'2, a'3), a'2 and a'3 represent the left and right end values ​​in the acidity characteristic data interval of different states of the hydraulic oil [a'2, a'3), and the acidity characteristic state of the hydraulic oil is transitional, indicating that the acidity of the hydraulic oil is in a transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist; [a'3,a'4] 异常 Indicates the hydraulic oil acidity characteristic data interval [a'3, a'4] corresponding to the hydraulic oil acidity characteristic data interval [a'3, a'4] when the hydraulic oil acidity characteristic state in the hydraulic gate hoist is abnormal, a'3 and a'4 represent the left and right end values ​​in the hydraulic oil acidity characteristic data interval [a'3, a'4] when the hydraulic oil acidity characteristic state is abnormal, indicating that the hydraulic oil acidity is not suitable for the normal operation of the hydraulic gate hoist, and a'1, a'2, a'3, and a'4 are all mgKOH / g; S32, using a depth-restricted search algorithm to calculate the hydraulic oil acidity characteristic mean data The acidity characteristic values ​​of the hydraulic oil are compared with the acidity characteristic data intervals of the hydraulic oil in different states in the hydraulic oil acidity characteristic data set A′, and the mean value data of the hydraulic oil acidity characteristic is analyzed. The corresponding hydraulic oil acidity characteristic state is constructed to obtain the hydraulic oil acidity characteristic state data A″.

5. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 4 is characterized in that: The S4 comprises the following steps: S41, establish the characteristic data set B'=([b'1,b'2) of the viscosity of hydraulic oil in different states 正常 ,[b'2,b'3) 过渡 , [b'3,b'4] 异常 ), where [b'1,b'2) 正常 Indicates the hydraulic oil viscosity characteristic data interval [b'1, b'2) corresponding to the hydraulic oil viscosity characteristic state in the hydraulic hoist, b'1 and b'2 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'1, b'2) in different states, and the hydraulic oil viscosity characteristic state being normal indicates that the hydraulic oil viscosity is suitable for the normal operation of the hydraulic hoist; [b'2,b'3) 过渡 Indicates that the characteristic state of the viscosity of the hydraulic oil in the hydraulic gate hoist is transitional, corresponding to the hydraulic oil viscosity characteristic data interval [b'2, b'3) in different states, b'2 and b'3 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'2, b'3) in different states, and the hydraulic oil viscosity characteristic state is transitional, indicating that the hydraulic oil viscosity is in a transitional change stage between being suitable for the normal operation of the hydraulic gate hoist and being unsuitable for the normal operation of the hydraulic gate hoist; [b'3,b'4] 异常 Indicates the hydraulic oil viscosity characteristic data interval [b'3, b'4] corresponding to the hydraulic oil viscosity characteristic state in the hydraulic gate hoist when the hydraulic oil viscosity characteristic state is abnormal, b'3 and b'4 represent the left and right end values ​​in the hydraulic oil viscosity characteristic data interval [b'3, b'4] when the hydraulic oil viscosity characteristic state is abnormal, indicating that the hydraulic oil viscosity is not suitable for the normal operation of the hydraulic gate hoist, and b'1, b'2, b'3, and b'4 are all Pa·s; S42, when the analysis of the hydraulic oil acidity characteristic state data A″ ​​is completed, the hydraulic oil viscosity characteristic mean data A″ ​​is searched by using a depth-limited search algorithm. The viscosity characteristic values ​​of the hydraulic oil are compared with the viscosity characteristic data intervals of the hydraulic oil in different states in the hydraulic oil viscosity characteristic data set B′, and the mean value data of the hydraulic oil viscosity characteristic is analyzed. The corresponding hydraulic oil viscosity characteristic state is constructed to obtain the hydraulic oil viscosity characteristic state data B".

6. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 5 is characterized in that: The S5 comprises the following steps: S51, establish the characteristic data set C'=([c'1,c'2) of the water content of hydraulic oil in different states 正常 , [c'2,c'3) 过渡 , [c'3,c'4] 异常 ), where [c'1,c'2) 正常 Indicates the characteristic data interval [c'1, c'2) of the hydraulic oil water content in different states corresponding to the hydraulic hoist when the characteristic state of the hydraulic oil water content in the hydraulic hoist is normal, c'1 and c'2 represent the left and right end values ​​in the characteristic data interval [c'1, c'2) of the hydraulic oil water content in different states, and the hydraulic oil water content characteristic state being normal indicates that the hydraulic oil water content is suitable for the normal operation of the hydraulic hoist; [c'2,c'3) 过渡 Indicates that the characteristic state of the water content of the hydraulic oil in the hydraulic gate hoist is transitional, and the corresponding hydraulic oil water content characteristic data interval [c'2, c'3) in different states, c'2 and c'3 represent the left and right end values ​​in the hydraulic oil water content characteristic data interval [c'2, c'3) in different states, and the hydraulic oil water content characteristic state is transitional, indicating that the hydraulic oil water content is in a transitional change stage between being suitable for normal operation of the hydraulic gate hoist and being unsuitable for normal operation of the hydraulic gate hoist; [c'3,c'4] 异常 Indicates the hydraulic oil water content characteristic data interval [c'3, c'4] corresponding to the hydraulic oil water content characteristic state in the hydraulic gate hoist when the characteristic state of the hydraulic oil water content is abnormal, c'3 and c'4 represent the left and right end values ​​in the hydraulic oil water content characteristic data interval [c'3, c'4] in different states, and the hydraulic oil water content characteristic state is abnormal, indicating that the hydraulic oil water content is not suitable for the normal operation of the hydraulic gate hoist, and c'1, c'2, c'3, and c'4 are all PPM; S52, when the analysis of the hydraulic oil viscosity characteristic state data B" is completed, the hydraulic oil water content characteristic mean data is searched by using a depth-limited search algorithm. The characteristic data interval of the hydraulic oil water content in different states in the hydraulic oil water content characteristic data set C' is compared with the characteristic data interval of the hydraulic oil water content in different states, and the characteristic mean data of the hydraulic oil water content is analyzed. The corresponding hydraulic oil water content characteristic state is obtained and the hydraulic oil water content characteristic state data C" is constructed.

7. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 6 is characterized in that: The S6 comprises the following steps: S61, establish the hydraulic oil characteristic state data set D = (A" in the fault state of the hoist 异常 ,B” 异常 ,C” 异常 ), where A” 异常 Indicates that the hydraulic oil acidity characteristic data A" is the hydraulic oil characteristic state data of the hoist fault state corresponding to the abnormality; B" 异常 Indicates that the hydraulic oil viscosity characteristic data B" is the hydraulic oil characteristic state data of the hoist fault state corresponding to the abnormality; C" 异常 The characteristic data C" indicating the water content of the hydraulic oil is the characteristic state data of the hydraulic oil in the fault state of the gate hoist corresponding to the abnormality; the characteristic state data of the hydraulic oil in the fault state of the gate hoist indicates the characteristic state data of the acidity, viscosity and water content of the hydraulic oil corresponding to the fault state of the gate hoist when the gate hoist is in operation or will continue to operate; S62, using a data recognition algorithm, the hydraulic oil acidity characteristic state data A", the hydraulic oil viscosity characteristic state data B", and the hydraulic oil water content characteristic state data C" are respectively identified with the hoist fault state hydraulic oil characteristic state data A" in the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The specific steps of the data recognition algorithm to perform keyword matching of the characteristic state of hydraulic oil are as follows: S621, when the algorithm is initialized, the maximum number of iterations T is updated, and the hydraulic oil characteristic state data set D of the gate hoist fault state is updated. All pheromones in the search space are initialized to ψ0; S622, algorithm iteration, the hydraulic oil characteristic state search ant in the search space of the hydraulic oil characteristic state data set D of the hoist fault state combines the hydraulic oil acidity characteristic state data A″, the hydraulic oil viscosity characteristic state data B″, and the hydraulic oil water content characteristic state data C″ with the hoist fault state hydraulic oil characteristic state data A″ ​​respectively. 异常 , B” 异常 , C” 异常 Matching, hydraulic oil characteristic state search ant search matching hoist fault state hydraulic oil characteristic state data A" 异常 , B” 异常 , C” 异常 The pheromone content on all trajectories decreased; S623, the hydraulic oil characteristic state search ant searches for the pheromone change of all trajectory lengths after this round of iteration in the search space of the hydraulic oil characteristic state data set D in the hoist fault state according to the trajectory length constructed by itself; the pheromone change calculation formula is as follows: where Δψ k (u,v) indicates that the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" are respectively combined with the hoist fault state hydraulic oil characteristic state data A" in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The pheromone change amount after the coordinate position (u, v) is searched by the kth hydraulic oil characteristic state search ant during the matching process; represents the trajectory length, R k It means that the kth hydraulic oil characteristic state search ant has combined the hydraulic oil acidity characteristic state data A”, hydraulic oil viscosity characteristic state data B”, and hydraulic oil water content characteristic state data C” with the hoist fault state hydraulic oil characteristic state data A” in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 The coordinate position set passed by the match is searched for the trajectory length with the largest pheromone change value, that is, the hoist fault state hydraulic oil characteristic state data A" that best matches the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", and hydraulic oil water content characteristic state data C" are matched in the search space of the hoist fault state hydraulic oil characteristic state data set D. 异常 , B” 异常 , C” 异常 ; S624, when the algorithm meets the maximum number of iterations T, the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" and hoist fault state hydraulic oil characteristic state data A" are output 异常 , B” 异常 , C” 异常 If the matching result is not reached, then continue to execute step S622 until the maximum number of iterations T is met; S63, outputting the hydraulic oil acidity characteristic state data A", hydraulic oil viscosity characteristic state data B", hydraulic oil water content characteristic state data C" and the hoist fault state hydraulic oil characteristic state data A" in step S624. 异常 , B” 异常 , C” 异常 The matching results are analyzed to generate the hoist fault status diagnosis result data E; When A" and A" 异常 , B” and B” 异常 , C” and C” 异常 If none of them are matched successfully, it means that the characteristic states of hydraulic oil acidity, viscosity and water content are all suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E means that there is no fault; When A" and A" 异常 If the match is successful, it means that the characteristic state of the hydraulic oil acidity is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault; When B” and B″ 异常 If the match is successful, it means that the characteristic state of the hydraulic oil viscosity is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault; When C” and C” 异常 If the match is successful, it means that the characteristic state of the water content in the hydraulic oil is not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that there is a fault; When A" and A" 异常 , B” and B” 异常 , C” and C” 异常 All of them are matched successfully, indicating that the characteristic states of the hydraulic oil acidity, viscosity, and water content are not suitable for the normal operation of the gate hoist, and the gate hoist fault state diagnosis result data E indicates that a fault exists.

8. The method for diagnosing operating faults of gate hoists based on artificial intelligence according to claim 7 is characterized in that: The S7 comprises the following steps: S71. Push the gate hoist fault status diagnosis result data E to the gate hoist fault monitoring terminal through the Internet of Things communication network, and the gate hoist fault monitoring terminal displays and feeds back the gate hoist fault status diagnosis result data E through a display screen.

9. A system for implementing the method for diagnosing operating faults of gate hoists based on artificial intelligence as claimed in any one of claims 1 to 8, characterized in that: The system comprises a gate hoist hydraulic oil characteristic parameter acquisition module, a gate hoist hydraulic oil characteristic parameter state analysis module, and a gate hoist fault state diagnosis output module.

Images