Internet of Things platform operation and maintenance management system and method based on artificial intelligence

Abstract

The invention relates to the technical field of Internet of Things equipment maintenance management, and discloses an Internet of Things platform operation and maintenance management system and method based on artificial intelligence, and the system comprises an Internet of Things platform operation and maintenance object confirmation module, an Internet of Things platform operation and maintenance state analysis module, and an Internet of Things platform operation and maintenance result display module. Internet-of-things equipment composition and Internet-of-things equipment characteristic parameters in different Internet-of-things operation and maintenance communication lines are accurately analyzed through standard preset Internet-of-things equipment characteristic parameters of different operation and maintenance communication lines in combination with an intelligent search algorithm and Internet-of-things operation and maintenance communication line object identification parameters; efficient and accurate retrieval of the feature information of the Internet of Things equipment in any Internet of Things operation and maintenance communication line is realized; the operation abnormal state image parameters of different types of Internet of Things equipment are stored based on big data, and the intelligent analysis of the operation abnormal state type of the Internet of Things equipment is carried out in combination with an artificial intelligence algorithm and the operation abnormal state image parameters of the Internet of Things equipment, so that the reliability and intelligence of operation and maintenance management of an Internet of Things platform are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance and management of Internet of Things devices, and specifically to an Internet of Things platform operation and maintenance management system and method based on artificial intelligence. Background Art

[0002] IoT operation and maintenance is responsible for the operation, maintenance and management of IoT systems, mainly involving hardware equipment, networks and software. IoT systems are complex and involve multiple different types of equipment and technologies. Due to the large number of equipment and complex system architecture, it is necessary to carry out equipment management, data collection, data processing, data analysis and other tasks to ensure the safe, stable and efficient operation of IoT systems. IoT operation and maintenance is an important part of the development of IoT technology. With the continuous popularization of IoT, IoT operation and maintenance talents will be increasingly valued. IoT operation and maintenance is responsible for maintaining and ensuring the high availability of the entire service, while continuously optimizing the system architecture to improve deployment efficiency. The operation status of IoT devices such as routers, switches, servers, power systems, air conditioning systems, storage devices, etc. in the IoT platform is supervised; however, the existing IoT platform operation and maintenance management system cannot intelligently determine the specific IoT operation and maintenance communication line objects and intelligently monitor the operation status of IoT devices according to the amount of time, nor can it realize intuitive and visual feedback of the monitoring results of the operation status of IoT devices in the IoT communication line.

[0003] The Chinese invention patent with announcement number CN104993950B discloses a virtual server operation and maintenance method and operation and maintenance management system, which configures each operation in LVS operation and maintenance as an atomic operation; the operation and maintenance management platform selects one or more corresponding atomic operations according to the operation and maintenance event trigger command; the operation and maintenance management platform divides the one or more atomic operations into one or more configuration items, each configuration item includes one or more atomic operations; the operation and maintenance management platform adds a label to each configuration item as the boundary of the configuration item; the operation and maintenance management platform encapsulates the one or more atomic operations to generate an LVS configuration file; the operation and maintenance management platform receives the LVS configuration file modification command, finds the corresponding configuration item according to the label of the configuration item to be modified, and modifies it. The above technical solutions cannot realize online monitoring of the operating status of network devices in the Internet. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In order to solve the problem that the above-mentioned existing Internet of Things platform operation and maintenance management system cannot intelligently judge the specific Internet of Things operation and maintenance communication line objects and intelligently monitor the operating status of Internet of Things devices according to the time amount, and cannot realize intuitive and visual feedback of the monitoring results of the operating status of Internet of Things devices in the Internet of Things communication line, the above purposes are realized, such as real-time collection of operation and maintenance time point data, accurate identification of Internet of Things operation and maintenance communication line objects at different time points, scientific analysis of specific Internet of Things operation and maintenance communication line Internet of Things device characteristic information, accurate acquisition of Internet of Things device operating status image information, intelligent evaluation of Internet of Things device operating status analysis parameters, and intuitive feedback output of Internet of Things device operating status analysis results.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical solution: an operation and maintenance management method of an Internet of Things platform based on artificial intelligence, the method comprising the following steps:

[0008] S1. Collect operation and maintenance time point data;

[0009] S2. Perform IoT operation and maintenance communication line object identification processing at a specific operation and maintenance time point based on the operation and maintenance time point data and IoT operation and maintenance communication line object data at different time points, generate IoT operation and maintenance communication line object identification data and output it;

[0010] S3, performing a search and processing of characteristic information of IoT devices in a specific IoT operation and maintenance communication line based on the IoT operation and maintenance communication line object identification data and characteristic data of IoT devices in different operation and maintenance communication lines, and generating characteristic data of IoT devices in a specific operation and maintenance communication line;

[0011] S4, collect image data of the operating status of IoT devices;

[0012] S5. Performing an analysis process on the abnormal operation state of the IoT device according to the IoT device operation state image data and the abnormal operation state image data of different types of IoT devices, and generating abnormal operation state analysis data of the IoT device;

[0013] S6. Build real-time operation and maintenance management display data for the IoT platform;

[0014] S7. Execute an operation and maintenance result feedback task of the Internet of Things platform according to the real-time operation and maintenance management display data of the Internet of Things platform.

[0015] Preferably, the operation steps of collecting operation and maintenance time point data are as follows:

[0016] S11, obtain the time characteristic parameters of the current time year, month, day and hour of the IoT operation and maintenance platform online through the time measurement module, and generate the operation and maintenance time point data t dangThe operation and maintenance time point data represents the time point information when the Internet of Things operation and maintenance platform performs the operation and maintenance work.

[0017] Preferably, the operation steps of performing IoT operation and maintenance communication line object identification processing at a specific operation and maintenance time point based on the operation and maintenance time point data and IoT operation and maintenance communication line object data at different time points, generating IoT operation and maintenance communication line object identification data and outputting it are as follows:

[0018] S21. Establishing IoT operation and maintenance communication line object data sets at different time points n=1,2,3,…,η;where t n Indicates the operation and maintenance operation setting time point data corresponding to the nth IoT operation and maintenance communication line, a n Indicates the operation and maintenance operation setting time point data t n The corresponding IoT operation and maintenance communication line object data at different time points, η represents the maximum number of IoT operation and maintenance communication lines, ↓ represents t n with a n The corresponding relationship, that is, t n Corresponding to a n , t1 corresponds to a1, t η with a η ; The IoT operation and maintenance communication line object data at different time points represent specific communication line object identification number data for performing IoT operation and maintenance operations at different time points preset according to time characteristics;

[0019] S22, using the KD tree nearest neighbor search algorithm to search the operation and maintenance time point data t dang The operation and maintenance operation setting time point data t in the IoT operation and maintenance communication line object data set A at different time points n Perform time point value matching to search for the operation and maintenance time point data t dang The corresponding IoT operation and maintenance communication line object data a at different time points n Generate IoT operation and maintenance communication line object identification data a′ through data identification n ;

[0020] S23, the IoT operation and maintenance communication line object identification data a′ n Output data.

[0021] Preferably, the operation steps of searching and processing the characteristic information of the IoT devices in the specific IoT operation and maintenance communication line are performed based on the IoT operation and maintenance communication line object identification data and the characteristic data of the IoT devices in different operation and maintenance communication lines to generate the characteristic data of the IoT devices in the specific operation and maintenance communication line are as follows:

[0022] S31. Establish a matrix of characteristic data sets of IoT devices on different operation and maintenance communication lines Among them B n Indicates the IoT operation and maintenance communication line object data a at different time points n Corresponding IoT device feature data sets for different operation and maintenance communication lines, m n =1,2,3,…,ι n , Indicates the mth IoT operation and maintenance communication line in the nth IoT operation and maintenance communication line n IoT device feature data for different operation and maintenance communication lines corresponding to IoT devices; n Indicates the maximum number of IoT devices in the nth IoT operation and maintenance communication line, wherein the characteristic data of IoT devices in different operation and maintenance communication lines include the name, model specification, device number, IP address, device number in the IoT communication line, and spatial geographic location parameters of the IoT device;

[0023] S32, using a unified cost search algorithm to identify the IoT operation and maintenance communication line object data a′ n The characteristic data set B of the IoT devices of different operation and maintenance communication lines in the characteristic data set matrix B of the IoT devices of different operation and maintenance communication lines n According to the IoT operation and maintenance communication line number, the IoT device feature information in the specific IoT operation and maintenance communication line is searched and processed to obtain the IoT operation and maintenance communication line object identification data a′ n The corresponding IoT device feature data set B for different operation and maintenance communication lines n And generate the specific operation and maintenance communication line IoT equipment feature data set C = (c1,…,c o ,…,c κ ), o = 1, 2, 3, ..., κ, where c o represents the characteristic data of the IoT device of the oth specific operation and maintenance communication line corresponding to the specific operation and maintenance communication line, κ represents the maximum number of IoT devices of the specific operation and maintenance communication line, and the IoT devices include routers, switches, servers and storage devices.

[0024] Preferably, the operation steps of collecting the image data of the operating status of the Internet of Things device are as follows:

[0025] S41, based on the specific operation and maintenance communication line IoT device feature data c1 to c in the specific operation and maintenance communication line IoT device feature data set C. κ Orderly locate and search for the physical space location of the IoT device and the virtual space location of the communication network, and use cloud lens and screenshot software to collect the operating status images of the IoT device under the physical space conditions and the operating status images output by the display screen under the virtual space conditions of the communication network, and construct the IoT device operating status image data set matrix Where D o Indicates the characteristic data c of the IoT device of the specific operation and maintenance communication line o Corresponding IoT device operation status image data set; p o =1,2,3,…,λ o , Indicates the collection of the pth specific operation and maintenance communication line IoT device o The image data of the operation status of IoT devices, λ o It represents the maximum value of the number of IoT device operation status image data collected for the IoT device of the oth specific operation and maintenance communication line. The operation status under physical space conditions represents the operation status of the IoT device hardware. The operation status output on the display screen under the communication network virtual space conditions represents the operation status of the IoT device software.

[0026] Preferably, the operation steps of performing analysis and processing on the abnormal operation state of the Internet of Things device according to the Internet of Things device operation state image data and the abnormal operation state image data of different types of Internet of Things devices to generate the abnormal operation state analysis data of the Internet of Things device are as follows:

[0027] S51. Establish an image data set E = (e1,…,e y ,…,e μ ), y=1,2,3,…,μ, where e y represents the image data of different types of abnormal operation states of IoT devices corresponding to the yth type of abnormal operation state of IoT device, and μ represents the maximum number of types of abnormal operation states of IoT devices; the types of abnormal operation states of IoT devices include IoT device fault indicator alarm, IoT device damage, IoT device smoke, IoT device open flame, IoT device connection line disconnection, IoT device connection line failure, IoT device display screen error code information, IoT device display screen freeze and IoT display screen blue screen;

[0028] S52: The Internet of Things device operating status image data sets D1 to D2 in the Internet of Things device operating status image data set matrix D are κ The image data of the operation status of the Internet of Things device and the image data set E of the abnormal operation status of the different types of Internet of Things devices, the image data set E of the abnormal operation status of the different types of Internet of Things devices y Perform image feature matching to analyze the specific operation and maintenance communication line IoT device feature data c1 to c κThe corresponding abnormal operation status analysis results of the IoT devices are used to generate the abnormal operation status analysis data set F of the IoT devices. The specific operation steps for generating the abnormal operation status analysis data set F of the IoT devices are as follows:

[0029] S521, initialization, setting the device state recognition cheetah to initialize its position in the μ-dimensional search space of the image data set E of abnormal operation status of different types of IoT devices. The device state recognition cheetah initialization position is described as: J i,j =ζ+rand(ζ-ψ), where Ji,j represents the position of the ith device state recognition cheetah in the search space of dimension j, that is, the position of the ith device state recognition cheetah in the search space of the image data set E of abnormal operation status of different types of IoT devices with dimension μ, ζ and ψ are the upper and lower limits of the j-dimensional search space respectively, rand is a random number in the interval (0,1), and R represents the maximum number of iterations;

[0030] S522, execute the search strategy to search for prey, the device status recognition cheetah performs a full range scan or actively searches for prey in its territory or surrounding area, that is, the device status recognition cheetah searches for the image data sets D1 to D1 of the abnormal operation status of different types of IoT devices in the search space of the image data set E of the abnormal operation status of the IoT device. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation state type of the IoT device; the search strategy is mathematically described as in It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r+1-th iteration of the prey search strategy execution phase, that is, the position of the i-th device state recognition cheetah in the search space of dimension μ of the abnormal operation state image data set E of different types of IoT devices after the r+1-th iteration of the i-th device state recognition cheetah; It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r-th iteration of the search strategy for prey, that is, the position of the i-th device state recognition cheetah in the search space of dimension μ after the r-th iteration of the image data set E of abnormal operation status of different types of IoT devices, is the random number of the cheetah with normal distribution in the search space of dimension j for the i-th device state recognition, The search step length of the i-th device state recognition cheetah in the search space of dimension j after the r-th iteration;

[0031] S523, execute the search for prey using the sit-and-wait strategy. In the search mode, the prey is exposed to the field of view of the device status recognition cheetah, that is, the prey is searched for in the search space of the abnormal operation state image data set E of different types of IoT devices. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal state type of the IoT device operation, the device state recognition, each action of the cheetah causes the prey to escape, the device state recognition, the cheetah adopts a sit-and-wait ambush strategy to approach the prey, the sit-and-wait ambush strategy includes lying on the ground or hiding in the bushes, the sit-and-wait strategy is mathematically described as in It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r+1th iteration of the sit-and-wait strategy phase of searching for prey, that is, the position of the i-th device state recognition cheetah in the search space of the abnormal state image data set E of different types of IoT devices with dimension μ after the r+1th iteration of the i-th device state recognition cheetah; It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r-th iteration of the sit-and-wait strategy phase of searching for prey, that is, the position of the i-th device state recognition cheetah in the search space of the abnormal state image data set E of different types of IoT devices with dimension μ after the r-th iteration of the i-th device state recognition cheetah;

[0032] S524, execute the sit-and-wait strategy to approach the prey and attack the prey according to the attack strategy. In the algorithm, each device status recognition cheetah can adjust its position according to the position of the escaping prey, the leading device status recognition cheetah or the nearby device status recognition cheetah to obtain the best attack position, that is, search for the image data sets D1 to D1 of the operation status of the IoT device in the search space of the abnormal operation status image data set E of different types of IoT devices. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation state type of IoT device, the attack strategy is mathematically described as in It represents the position of the prey in the search space of dimension j after the r+1th iteration of the i-th device state recognition cheetah in the attack phase according to the attack strategy, that is, the position of the prey in the search space of the abnormal state image data set E of different types of IoT devices with dimension μ after the r+1th iteration of the i-th device state recognition cheetah; Z represents the position of the prey in the search space of dimension j after the r-th iteration of the i-th device state recognition cheetah in the attack phase according to the attack strategy, that is, the position of the prey in the search space of dimension μ of the abnormal operation state image data set E of different types of IoT devices after the r-th iteration of the i-th device state recognition cheetah; i,j represents the steering factor of the i-th device state recognition cheetah in the search space of dimension j; Identify the interaction factor of the cheetah at the rth iteration in the search space of dimension j for the i-th head device state;

[0033] S525, execute S522, S523, S524 until the maximum number of iterations is met, and output the image data set D1 to D1 corresponding to the operation status of the IoT device. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation status type of the IoT device;

[0034] S526, compare the output in step S525 with the IoT device operation status image data set D1 to D κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation state type of the IoT device is identified by data to generate the abnormal operation state analysis data set of the IoT device F = (f1,…,f o ,…,f κ ), where f o Indicates the characteristic data c of the IoT device of the specific operation and maintenance communication line o Corresponding to the abnormal operation status analysis data of IoT devices.

[0035] Preferably, the steps for constructing real-time operation and maintenance management display data of the Internet of Things platform are as follows:

[0036] S61, the operation and maintenance time point data t dang , the IoT operation and maintenance communication line object identification data a′ n , the specific operation and maintenance communication line IoT device feature data set C, and the IoT device abnormal operation status analysis data set F are arranged from top to bottom according to the logic tree principle, and the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set F of ... C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and κ and the IoT device operation abnormal state analysis data f1 to f in the IoT device operation abnormal state analysis data set F κAccording to the logic tree principle, the data is combined and arranged from left to right to construct the real-time operation and maintenance management display data H of the Internet of Things platform.

[0037] Preferably, the operation steps of performing the Internet of Things platform operation and maintenance management result feedback operation according to the Internet of Things platform real-time operation and maintenance management display data are as follows:

[0038] S71, the real-time operation and maintenance management display data H of the Internet of Things platform is used to perform the Internet of Things platform operation and maintenance management result feedback display task through the display screen of the Internet of Things platform operation and maintenance management terminal.

[0039] An IoT platform operation and maintenance management system based on artificial intelligence, used to implement the IoT platform operation and maintenance management method based on artificial intelligence, the system includes an IoT platform operation and maintenance object confirmation module, an IoT platform operation and maintenance status analysis module, and an IoT platform operation and maintenance result display module;

[0040] The IoT platform operation and maintenance object confirmation module includes an operation and maintenance time point collection unit, an IoT operation and maintenance communication line object storage unit at different time points, a specific IoT operation and maintenance communication line object identification unit, and a specific IoT operation and maintenance communication line object parameter output unit;

[0041] The operation and maintenance time point collection unit collects operation and maintenance time point data through the time measurement module; the different time point IoT operation and maintenance communication line object storage unit is used to store IoT operation and maintenance communication line object data at different time points; the specific IoT operation and maintenance communication line object identification unit performs IoT operation and maintenance communication line object identification processing at a specific operation and maintenance time point based on the operation and maintenance time point data and IoT operation and maintenance communication line object data at different time points to generate IoT operation and maintenance communication line object identification data; the specific IoT operation and maintenance communication line object parameter output unit is used to output the IoT operation and maintenance communication line object identification data;

[0042] The IoT platform operation and maintenance status analysis module includes a characteristic information unit of IoT devices of different operation and maintenance communication lines, a characteristic information search unit of IoT devices of specific operation and maintenance communication lines, an IoT device operation status image acquisition unit, an abnormal operation status image storage unit of different types of IoT devices, and an abnormal operation status analysis unit of IoT devices;

[0043] The IoT device characteristic information unit of different operation and maintenance communication lines is used to store characteristic data of IoT devices of different operation and maintenance communication lines; the IoT device characteristic information search unit of specific operation and maintenance communication lines performs characteristic information search processing of IoT devices in specific operation and maintenance communication lines based on the IoT operation and maintenance communication line object identification data and characteristic data of IoT devices of different operation and maintenance communication lines, and generates characteristic data of IoT devices of specific operation and maintenance communication lines; the IoT device operation status image acquisition unit acquires image data of IoT device operation status through cloud lens and screenshot software; the image storage unit of different types of IoT devices operation abnormality is used to store image data of different types of IoT devices operation abnormality; the IoT device operation abnormality analysis unit performs abnormal operation status analysis processing of IoT devices based on the IoT device operation status image data combined with artificial intelligence algorithm and image data of different types of IoT devices operation abnormality, and generates analysis data of IoT device operation abnormality;

[0044] The Internet of Things platform operation and maintenance result display module includes an Internet of Things platform operation and maintenance result parameter generation unit and an Internet of Things platform operation and maintenance result feedback unit;

[0045] The Internet of Things platform operation and maintenance result parameter generation unit is used to construct the real-time operation and maintenance management display data of the Internet of Things platform; the Internet of Things platform operation and maintenance result feedback unit performs the Internet of Things platform operation and maintenance management result feedback operation according to the real-time operation and maintenance management display data of the Internet of Things platform.

[0046] (III) Beneficial effects

[0047] The present invention provides an IoT platform operation and maintenance management system and method based on artificial intelligence. It has the following beneficial effects:

[0048] 1. Through the operation and maintenance time point collection unit, the time measurement module is used to accurately obtain the operation and maintenance time point parameters online, providing data support for dynamically and accurately identifying the IoT operation and maintenance communication line objects at different time points; the IoT operation and maintenance communication line object storage unit at different time points and the specific IoT operation and maintenance communication line object identification unit cooperate with each other, scientifically preset the IoT operation and maintenance communication line object parameters at different time points, and combine the intelligent search algorithm with the operation and maintenance time point parameters to perform intelligent identification of the IoT operation and maintenance communication line objects at specific operation and maintenance time points, so as to realize the scientific and autonomous operation and maintenance management of the IoT operation and maintenance communication line objects based on the time quantity, thereby improving the quality and efficiency of the operation and maintenance management of the IoT platform.

[0049] 2. Through the cooperation of IoT device feature information units of different operation and maintenance communication lines and IoT device feature information search units of specific operation and maintenance communication lines, standard preset IoT device feature parameters of different operation and maintenance communication lines are combined with intelligent search algorithms and IoT operation and maintenance communication line object identification parameters to accurately analyze the composition of IoT devices and feature parameters of IoT devices in different IoT operation and maintenance communication lines, so as to realize efficient and accurate retrieval of IoT device feature information in any IoT operation and maintenance communication line; IoT device operation status image acquisition unit, through cloud lens and screenshot software, accurately collects image parameters of software and hardware operation status of IoT devices in any IoT operation and maintenance communication line in real time online; different types of IoT device abnormal operation status image storage units and IoT device abnormal operation status analysis units cooperate with each other, based on big data storage of different types of IoT device abnormal operation status image parameters combined with artificial intelligence algorithms and IoT device operation status image parameters, to realize intelligent evaluation of the operation status of IoT devices in any IoT operation and maintenance communication line, and improve the reliability and intelligence of IoT platform operation and maintenance management.

[0050] 3. Through the cooperation between the IoT platform operation and maintenance result parameter generation unit and the IoT platform operation and maintenance result feedback unit, the operation and maintenance time point, IoT operation and maintenance communication line object, and IoT device operation analysis result parameters of specific IoT operation and maintenance communication line objects are combined and arranged from top to bottom and from left to right based on the logic tree principle, and the real-time operation and maintenance management display parameters of the IoT platform are scientifically constructed and displayed to the IoT platform operation and maintenance management end in time with the display screen, so as to realize clear and intuitive feedback of the IoT platform operation and maintenance results. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of the modules of the artificial intelligence-based IoT platform operation and maintenance management system provided by the present invention;

[0052] Figure 2 This is a flow chart of the artificial intelligence-based Internet of Things platform operation and maintenance management method provided by the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] The embodiments of the IoT platform operation and maintenance management system and method based on artificial intelligence are as follows:

[0055] Embodiment 1:

[0056] See also Figure 1 - Figure 2 , an operation and maintenance management method for an Internet of Things platform based on artificial intelligence, the method comprises the following steps:

[0057] S1. Collect operation and maintenance time point data;

[0058] S2. Perform IoT operation and maintenance communication line object identification processing at a specific operation and maintenance time point based on the operation and maintenance time point data and IoT operation and maintenance communication line object data at different time points, generate IoT operation and maintenance communication line object identification data and output it;

[0059] S3, searching and processing the characteristic information of IoT devices in a specific IoT operation and maintenance communication line based on the IoT operation and maintenance communication line object identification data and the characteristic data of IoT devices in different operation and maintenance communication lines, and generating characteristic data of IoT devices in a specific operation and maintenance communication line;

[0060] S4, collect image data of the operating status of IoT devices;

[0061] S5. Analyze and process the abnormal operation state of the IoT device according to the IoT device operation state image data and the abnormal operation state image data of different types of IoT devices, and generate abnormal operation state analysis data of the IoT device;

[0062] S6. Build real-time operation and maintenance management display data for the IoT platform;

[0063] S7. Execute the operation and maintenance result feedback task of the Internet of Things platform based on the real-time operation and maintenance management display data of the Internet of Things platform.

[0064] For further information, see Figure 1 - Figure 2 The steps for collecting operation and maintenance time point data are as follows:

[0065] S11, obtain the time characteristic parameters of the current time year, month, day and hour of the IoT operation and maintenance platform online through the time measurement module, and generate the operation and maintenance time point data t dang ,The operation and maintenance time point data represents the time point information when the ,IoT operation and maintenance platform performs the operation and maintenance ,task.

[0066] Based on the operation and maintenance time point data and the IoT operation and maintenance communication line object data at different time points, the IoT operation and maintenance communication line object recognition processing is performed at a specific operation and maintenance time point, and the operation steps for generating and outputting the IoT operation and maintenance communication line object recognition data are as follows:

[0067] S21. Establishing IoT operation and maintenance communication line object data sets at different time points n=1,2,3,…,η;where t nIndicates the operation and maintenance operation setting time point data corresponding to the nth IoT operation and maintenance communication line, a n Indicates the operation and maintenance task setting time point data t n The corresponding IoT operation and maintenance communication line object data at different time points, η represents the maximum number of IoT operation and maintenance communication lines, ↓ represents t n with a n The corresponding relationship, that is, t n Corresponding to a n , t1 corresponds to a1, t η with a η ; The IoT operation and maintenance communication line object data at different time points represents the specific communication line object identification number data for performing IoT operation and maintenance operations at different time points preset according to time characteristics;

[0068] S22, use the KD tree nearest neighbor search algorithm to convert the operation and maintenance time point data t dang The operation and maintenance task setting time point data t in the IoT operation and maintenance communication line object data set A at different time points n Perform time point value matching and search for operation and maintenance time point data t dang The corresponding IoT operation and maintenance communication line object data at different time points a n Generate IoT operation and maintenance communication line object identification data a′ through data identification n ;

[0069] S23, the IoT operation and maintenance communication line object identification data a′ n Output data.

[0070] Through the operation and maintenance time point collection unit, the time measurement module is used to accurately obtain the operation and maintenance time point parameters online, providing data support for dynamically and accurately identifying the IoT operation and maintenance communication line objects at different time points; the IoT operation and maintenance communication line object storage unit at different time points and the specific IoT operation and maintenance communication line object identification unit cooperate with each other, scientifically preset the IoT operation and maintenance communication line object parameters at different time points, and combine the intelligent search algorithm with the operation and maintenance time point parameters to perform intelligent identification of the IoT operation and maintenance communication line objects at specific operation and maintenance time points, realize scientific and autonomous operation and maintenance management of the IoT operation and maintenance communication line objects based on time quantity, and improve the quality and efficiency of IoT platform operation and maintenance management.

[0071] For further information, see Figure 1 - Figure 2 , based on the IoT operation and maintenance communication line object identification data and the IoT device feature data of different operation and maintenance communication lines, the IoT device feature information search and processing in the specific IoT operation and maintenance communication line is performed, and the operation steps for generating the IoT device feature data of the specific operation and maintenance communication line are as follows:

[0072] S31. Establish a matrix of characteristic data sets of IoT devices on different operation and maintenance communication lines Among them B n Represents IoT operation and maintenance communication line object data at different time points a n Corresponding IoT device feature data sets for different operation and maintenance communication lines, m n =1,2,3,…,ι n , Indicates the mth IoT operation and maintenance communication line in the nth IoT operation and maintenance communication line n IoT device feature data for different operation and maintenance communication lines corresponding to IoT devices; n Indicates the maximum number of IoT devices in the nth IoT operation and maintenance communication line. The characteristic data of IoT devices in different operation and maintenance communication lines include the name, model specification, device number, IP address, device number in the IoT communication line, and spatial geographic location parameters of the IoT device;

[0073] S32, using a unified cost search algorithm to identify the IoT operation and maintenance communication line object data a′ n The characteristic data set B of IoT devices with different operation and maintenance communication lines in the characteristic data set matrix B n According to the IoT operation and maintenance communication line number, the IoT device feature information in the specific IoT operation and maintenance communication line is searched and processed to obtain the IoT operation and maintenance communication line object identification data a′ n Corresponding IoT device feature data set B for different operation and maintenance communication lines n And generate the specific operation and maintenance communication line IoT equipment feature data set C = (c1,…,c o ,…,c κ ), o = 1, 2, 3, ..., κ, where c o represents the characteristic data of the IoT device of the oth specific operation and maintenance communication line corresponding to the specific operation and maintenance communication line, κ represents the maximum number of IoT devices of the specific operation and maintenance communication line, and the IoT devices include routers, switches, servers and storage devices.

[0074] The steps to collect image data of the operating status of IoT devices are as follows:

[0075] S41, based on the specific operation and maintenance communication line IoT device feature data c1 to c in the specific operation and maintenance communication line IoT device feature data set C. κOrderly locate and search for the physical space location of the IoT device and the virtual space location of the communication network, and use cloud lens and screenshot software to collect the operating status images of the IoT device under the physical space conditions and the operating status images output by the display screen under the virtual space conditions of the communication network, and construct the IoT device operating status image data set matrix Where D o Indicates the characteristic data of IoT devices on specific operation and maintenance communication lines c o Corresponding IoT device operation status image data set; p o =1,2,3,…,λ o , Indicates the collection of the pth specific operation and maintenance communication line IoT device o The image data of the operation status of IoT devices, λ o It represents the maximum value of the number of IoT device operation status image data collected for the IoT device of the oth specific operation and maintenance communication line. The operation status under physical space conditions represents the operation status of the IoT device hardware. The operation status output on the display screen under the communication network virtual space conditions represents the operation status of the IoT device software.

[0076] The operation steps for analyzing and processing the abnormal operation status of IoT devices based on the image data of the operation status of IoT devices and the image data of the abnormal operation status of different types of IoT devices to generate the analysis data of the abnormal operation status of IoT devices are as follows:

[0077] S51. Establish an image data set E = (e1,…,e y ,…,e μ ), y=1,2,3,…,μ, where e y represents the image data of different types of abnormal operation status of IoT devices corresponding to the yth type of abnormal operation status of IoT devices, and μ represents the maximum number of types of abnormal operation status of IoT devices; the types of abnormal operation status of IoT devices include IoT device fault indicator alarm, IoT device damage, IoT device smoke, IoT device open fire, IoT device connection cable disconnection, IoT device connection cable failure, IoT device display screen error code information, IoT device display screen freeze, and IoT display screen blue screen;

[0078] S52, the Internet of Things device operating status image data sets D1 to D κ The image data of the running status of the IoT device and the image data set E of the abnormal running status of different types of IoT devices are the image data of the abnormal running status of different types of IoT devices. yPerform image feature matching to analyze the specific operation and maintenance communication line IoT device feature data c1 to c κ The corresponding abnormal operation status analysis results of the IoT devices are used to generate the abnormal operation status analysis data set F of the IoT devices. The specific operation steps for generating the abnormal operation status analysis data set F of the IoT devices are as follows:

[0079] S521, initialization, setting the device state recognition cheetah to initialize its position in the μ-dimensional search space of the image data set E of abnormal operation status of different types of IoT devices. The device state recognition cheetah initialization position is described as: J i,j =ζ+rand(ζ-ψ), where J i,j represents the position of the ith device state recognition cheetah in the search space of dimension j, that is, the position of the ith device state recognition cheetah in the search space of the image data set E of abnormal operation status of different types of IoT devices with dimension μ, ζ and ψ are the upper and lower limits of the j-dimensional search space respectively, rand is a random number in the interval (0,1), and R represents the maximum number of iterations;

[0080] S522, execute the search strategy to search for prey, the device status recognition cheetah performs a full range scan or actively searches for prey in its territory or surrounding area, that is, the device status recognition cheetah searches for the image data sets D1 to D1 of the abnormal operation status of different types of IoT devices in the search space of the image data set E of the abnormal operation status of IoT devices. κ The image data of abnormal operation status of different types of IoT devices matches the image data of IoT device operation status. y The corresponding abnormal operation state type of the IoT device; the search strategy is mathematically described as in It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r+1-th iteration of the prey search strategy execution phase, that is, the position of the i-th device state recognition cheetah in the search space of dimension μ of the abnormal operation state image data set E of different types of IoT devices after the r+1-th iteration of the i-th device state recognition cheetah; It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r-th iteration of the search strategy for prey, that is, the position of the i-th device state recognition cheetah in the search space of dimension μ after the r-th iteration of the image data set E of abnormal operation status of different types of IoT devices, is the random number of the cheetah with normal distribution in the search space of dimension j for the i-th device state recognition, The search step length of the i-th device state recognition cheetah in the search space of dimension j after the r-th iteration;

[0081] S523, execute the sit-and-wait strategy to search for prey. In the search mode, the prey is exposed to the field of view of the device status recognition cheetah, that is, the prey is searched for in the search space of the abnormal operation state image data set E of different types of IoT devices. κ The image data of abnormal operation status of different types of IoT devices matches the image data of IoT device operation status. y The corresponding abnormal state type of the IoT device operation, device state recognition Every action of the cheetah causes the prey to escape. The cheetah adopts a sit-and-wait ambush strategy to approach the prey. The sit-and-wait ambush strategy includes lying on the ground or hiding in the bushes. The sit-and-wait strategy is mathematically described as in It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r+1th iteration of the sit-and-wait strategy phase of searching for prey, that is, the position of the i-th device state recognition cheetah in the search space of the abnormal state image data set E of different types of IoT devices with dimension μ after the r+1th iteration of the i-th device state recognition cheetah; It represents the position of the i-th device state recognition cheetah in the search space of dimension j after the r-th iteration of the sit-and-wait strategy phase of searching for prey, that is, the position of the i-th device state recognition cheetah in the search space of the abnormal state image data set E of different types of IoT devices with dimension μ after the r-th iteration of the i-th device state recognition cheetah;

[0082] S524, execute the sit-and-wait strategy to approach the prey and attack the prey according to the attack strategy. In the algorithm, each device status recognition cheetah can adjust its position according to the position of the escaping prey, the leading device status recognition cheetah or the nearby device status recognition cheetah to obtain the best attack position, that is, search for the image data sets D1 to D1 that are related to the image data sets D1 to D1 of the running state of the IoT device in the search space of the image data sets E of the abnormal running state of the IoT device. κ The image data of abnormal operation status of different types of IoT devices matches the image data of IoT device operation status. y The corresponding abnormal operation state type of IoT device, the attack strategy is mathematically described as in It represents the position of the prey in the search space of dimension j after the r+1th iteration of the i-th device state recognition cheetah in the attack phase according to the attack strategy, that is, the position of the prey in the search space of the abnormal state image data set E of different types of IoT devices with dimension μ after the r+1th iteration of the i-th device state recognition cheetah; Z represents the position of the prey in the search space of dimension j after the r-th iteration of the i-th device state recognition cheetah in the attack phase according to the attack strategy, that is, the position of the prey in the search space of dimension μ of the abnormal operation state image data set E of different types of IoT devices after the r-th iteration of the i-th device state recognition cheetah; i,j represents the steering factor of the i-th device state recognition cheetah in the search space of dimension j; Identify the interaction factor of the cheetah at the rth iteration in the search space of dimension j for the i-th head device state;

[0083] S525, execute S522, S523, S524 until the maximum number of iterations is met, and output the image data set D1 to D1 related to the operation status of the IoT device κ The image data of abnormal operation status of different types of IoT devices matches the image data of IoT device operation status. y The corresponding abnormal operation status type of the IoT device;

[0084] S526: Combine the output in step S525 with the IoT device operation status image data set D1 to D κ The image data of abnormal operation status of different types of IoT devices matches the image data of IoT device operation status. y The corresponding abnormal operation state type of the IoT device is identified by data to generate the abnormal operation state analysis data set of the IoT device F = (f1,…,f o ,…,f κ ), where f o Indicates the characteristic data of IoT devices on specific operation and maintenance communication lines c o Corresponding to the abnormal operation status analysis data of IoT devices.

[0085] Through the cooperation of IoT device characteristic information units of different operation and maintenance communication lines and IoT device characteristic information search units of specific operation and maintenance communication lines, standard preset characteristic parameters of IoT devices of different operation and maintenance communication lines are combined with intelligent search algorithm and IoT operation and maintenance communication line object identification parameters to accurately analyze the composition of IoT devices and characteristic parameters of IoT devices in different IoT operation and maintenance communication lines, so as to realize efficient and accurate retrieval of characteristic information of IoT devices in any IoT operation and maintenance communication line; IoT device operation status image acquisition unit accurately collects image parameters of software and hardware operation status of IoT devices in any IoT operation and maintenance communication line online in real time through cloud lens and screenshot software; different types of IoT device abnormal operation status image storage units and IoT device abnormal operation status analysis units cooperate with each other, and based on big data storage of different types of IoT device abnormal operation status image parameters combined with artificial intelligence algorithm and IoT device operation status image parameters, intelligent analysis of IoT device abnormal operation status types is carried out, so as to realize intelligent evaluation of the operation status of IoT devices in any IoT operation and maintenance communication line, and improve the reliability and intelligence of IoT platform operation and maintenance management.

[0086] For further information, see Figure 1 - Figure 2 The steps to build real-time operation and maintenance management display data for the IoT platform are as follows:

[0087] S61, the operation and maintenance time point data t dang , IoT operation and maintenance communication line object identification data a′ n , specific operation and maintenance communication line IoT device feature data set C, and IoT device abnormal operation status analysis data set F are from top to bottom according to the logic tree principle, and the specific operation and maintenance communication line IoT device feature data c1 to c in the specific operation and maintenance communication line IoT device feature data set C are κ and the IoT device operation abnormal state analysis data set F of the IoT device operation abnormal state analysis data f1 to f κ According to the logic tree principle, the data is combined and arranged from left to right to construct the real-time operation and maintenance management display data H of the Internet of Things platform.

[0088] The steps for performing IoT platform operation and maintenance management result feedback based on IoT platform real-time operation and maintenance management display data are as follows:

[0089] S71. The real-time operation and maintenance management display data H of the Internet of Things platform is displayed through the display screen of the Internet of Things platform operation and maintenance management terminal to perform the Internet of Things platform operation and maintenance management result feedback display task.

[0090] Through the cooperation between the IoT platform operation and maintenance result parameter generation unit and the IoT platform operation and maintenance result feedback unit, the operation and maintenance time point, IoT operation and maintenance communication line object, and IoT device operation analysis result parameters of specific IoT operation and maintenance communication line objects are combined and arranged from top to bottom and from left to right based on the logic tree principle, and the real-time operation and maintenance management display parameters of the IoT platform are scientifically constructed and displayed to the IoT platform operation and maintenance management end in a timely manner in conjunction with the display screen, so as to realize clear and intuitive feedback of the IoT platform operation and maintenance results.

[0091] Embodiment 2:

[0092] See also Figure 1 - Figure 2 , an IoT platform operation and maintenance management system based on artificial intelligence, used to implement an IoT platform operation and maintenance management method based on artificial intelligence, the system includes an IoT platform operation and maintenance object confirmation module, an IoT platform operation and maintenance status analysis module, and an IoT platform operation and maintenance result display module;

[0093] The IoT platform operation and maintenance object confirmation module includes an operation and maintenance time point collection unit, an IoT operation and maintenance communication line object storage unit at different time points, a specific IoT operation and maintenance communication line object identification unit, and a specific IoT operation and maintenance communication line object parameter output unit;

[0094] An operation and maintenance time point collection unit collects operation and maintenance time point data through a time measurement module; an IoT operation and maintenance communication line object storage unit at different time points is used to store IoT operation and maintenance communication line object data at different time points; a specific IoT operation and maintenance communication line object identification unit performs IoT operation and maintenance communication line object identification processing at a specific operation and maintenance time point based on the operation and maintenance time point data and IoT operation and maintenance communication line object data at different time points to generate IoT operation and maintenance communication line object identification data; a specific IoT operation and maintenance communication line object parameter output unit is used to output IoT operation and maintenance communication line object identification data;

[0095] The IoT platform operation and maintenance status analysis module includes an IoT device feature information unit for different operation and maintenance communication lines, an IoT device feature information search unit for specific operation and maintenance communication lines, an IoT device operation status image acquisition unit, an abnormal operation status image storage unit for different types of IoT devices, and an IoT device abnormal operation status analysis unit;

[0096] The characteristic information unit of IoT devices of different operation and maintenance communication lines is used to store characteristic data of IoT devices of different operation and maintenance communication lines; the characteristic information search unit of IoT devices of specific operation and maintenance communication lines performs characteristic information search and processing of IoT devices in specific operation and maintenance communication lines based on the object identification data of IoT operation and maintenance communication lines and characteristic data of IoT devices of different operation and maintenance communication lines, and generates characteristic data of IoT devices of specific operation and maintenance communication lines; the image acquisition unit of IoT device operation status collects image data of IoT device operation status through cloud lens and screenshot software; the image storage unit of abnormal operation status of different types of IoT devices is used to store image data of abnormal operation status of different types of IoT devices; the abnormal operation status analysis unit of IoT devices performs abnormal operation status analysis and processing of IoT devices based on the image data of IoT device operation status combined with artificial intelligence algorithm and image data of abnormal operation status of different types of IoT devices, and generates analysis data of abnormal operation status of IoT devices;

[0097] The IoT platform operation and maintenance result display module includes an IoT platform operation and maintenance result parameter generation unit and an IoT platform operation and maintenance result feedback unit;

[0098] The IoT platform operation and maintenance result parameter generation unit is used to construct the real-time operation and maintenance management display data of the IoT platform; the IoT platform operation and maintenance result feedback unit performs the IoT platform operation and maintenance management result feedback operation based on the real-time operation and maintenance management display data of the IoT platform.

[0099] 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. An operation and maintenance management method for an Internet of Things platform based on artificial intelligence, characterized in that: The method comprises the following steps: S1. Collect operation and maintenance time point data; S2. Perform IoT operation and maintenance communication line object identification processing at a specific operation and maintenance time point based on the operation and maintenance time point data and IoT operation and maintenance communication line object data at different time points, generate IoT operation and maintenance communication line object identification data and output it; S3, performing a search and processing of characteristic information of IoT devices in a specific IoT operation and maintenance communication line based on the IoT operation and maintenance communication line object identification data and characteristic data of IoT devices in different operation and maintenance communication lines, and generating characteristic data of IoT devices in a specific operation and maintenance communication line; S4, collect image data of the operating status of IoT devices; S5. Performing an analysis process on the abnormal operation state of the IoT device according to the IoT device operation state image data and the abnormal operation state image data of different types of IoT devices, and generating abnormal operation state analysis data of the IoT device; S6. Build real-time operation and maintenance management display data for the IoT platform; S7. Execute an operation and maintenance result feedback task of the Internet of Things platform according to the real-time operation and maintenance management display data of the Internet of Things platform.

2. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 1 is characterized in that: The S1 comprises the following steps: S11, obtain the time characteristic parameters of the current time year, month, day and hour of the IoT operation and maintenance platform online through the time measurement module, and generate the operation and maintenance time point data t dang .

3. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 2 is characterized in that: The S2 comprises the following steps: S21. Establishing IoT operation and maintenance communication line object data sets at different time points n=1,2,3,…,η;where t n Indicates the operation and maintenance operation setting time point data corresponding to the nth IoT operation and maintenance communication line, a n Indicates the operation and maintenance operation setting time point data t n The corresponding IoT operation and maintenance communication line object data at different time points, η represents the maximum number of IoT operation and maintenance communication lines, ↓ represents t n with a n The corresponding relationship, that is, t n Corresponding to a n , t1 corresponds to a1, t η with a η ; S22, using the KD tree nearest neighbor search algorithm to search the operation and maintenance time point data t dang The operation and maintenance operation setting time point data t in the IoT operation and maintenance communication line object data set A at different time points n Perform time point value matching to search for the operation and maintenance time point data t dang The corresponding IoT operation and maintenance communication line object data a at different time points n Generate IoT operation and maintenance communication line object identification data a' through data identification n ; S23, the IoT operation and maintenance communication line object identification data a' n Output data.

4. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 3 is characterized in that: The S3 comprises the following steps: S31. Establish a matrix of characteristic data sets of IoT devices on different operation and maintenance communication lines Among them B n Indicates the IoT operation and maintenance communication line object data a at different time points n Corresponding IoT device feature data sets for different operation and maintenance communication lines, m n =1,2,3,…,ι n , Indicates the mth IoT operation and maintenance communication line in the nth IoT operation and maintenance communication line n IoT device feature data for different operation and maintenance communication lines corresponding to IoT devices; n Indicates the maximum number of IoT devices in the nth IoT operation and maintenance communication line; S32, using a unified cost search algorithm to identify the IoT operation and maintenance communication line object data a' n The characteristic data set B of the IoT devices of different operation and maintenance communication lines in the characteristic data set matrix B of the IoT devices of different operation and maintenance communication lines n According to the IoT operation and maintenance communication line number, the IoT device feature information in the specific IoT operation and maintenance communication line is searched and processed to obtain the IoT operation and maintenance communication line object identification data a' n The corresponding IoT device feature data set B for different operation and maintenance communication lines n And generate the specific operation and maintenance communication line IoT equipment feature data set C = (c1,…,c o ,…,c κ ), o = 1, 2, 3, ..., κ, where c o represents the characteristic data of the IoT device of the oth specific operation and maintenance communication line corresponding to the specific operation and maintenance communication line, and κ represents the maximum number of IoT devices of the specific operation and maintenance communication line.

5. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 4 is characterized in that: The S4 comprises the following steps: S41, based on the specific operation and maintenance communication line IoT device feature data c1 to c in the specific operation and maintenance communication line IoT device feature data set C. κ Orderly locate and search for the physical space location of the IoT device and the virtual space location of the communication network, and use cloud lens and screenshot software to collect the operating status images of the IoT device under the physical space conditions and the operating status images output by the display screen under the virtual space conditions of the communication network, and construct the IoT device operating status image data set matrix Where D o Indicates the characteristic data c of the IoT device of the specific operation and maintenance communication line o Corresponding IoT device operation status image data set; p o =1,2,3,…,λ o , Indicates the collection of the pth specific operation and maintenance communication line IoT device o The image data of the operation status of IoT devices, λ o Indicates the maximum number of IoT device operation status image data collected for the o-th specific operation and maintenance communication line IoT device.

6. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 5 is characterized in that: The S5 comprises the following steps: S51. Establish an image data set E = (e1,…,e y ,…,e μ ), y=1,2,3,…,μ, where e y represents the image data of different types of abnormal operation states of IoT devices corresponding to the yth type of abnormal operation state of IoT device, and μ represents the maximum number of types of abnormal operation states of IoT devices; S52: The Internet of Things device operating status image data sets D1 to D2 in the Internet of Things device operating status image data set matrix D are κ The image data of the operation status of the Internet of Things device and the image data set E of the abnormal operation status of the different types of Internet of Things devices, the image data set E of the abnormal operation status of the different types of Internet of Things devices y Perform image feature matching to analyze the specific operation and maintenance communication line IoT device feature data c1 to c κ The corresponding abnormal operation status analysis results of the IoT devices are used to generate the abnormal operation status analysis data set F of the IoT devices. The specific operation steps for generating the abnormal operation status analysis data set F of the IoT devices are as follows: S521, initialization, setting the device status recognition cheetah to initialize its position in the μ-dimensional search space of the image data set E of abnormal operation status of different types of IoT devices; S522, execute the search strategy to search for prey, the device status recognition cheetah performs a full range scan or actively searches for prey in its territory or surrounding area, that is, the device status recognition cheetah searches for the image data sets D1 to D1 of the abnormal operation status of different types of IoT devices in the search space of the image data set E of the abnormal operation status of the IoT device. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation status type of the IoT device; S523, execute the search for prey using the sit-and-wait strategy. In the search mode, the prey is exposed to the field of view of the device status recognition cheetah, that is, the prey is searched for in the search space of the abnormal operation state image data set E of different types of IoT devices. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation status type of the IoT device; S524, execute the sit-and-wait strategy to approach the prey and attack the prey according to the attack strategy. In the algorithm, each device status recognition cheetah can adjust its position according to the position of the escaping prey, the leading device status recognition cheetah or the nearby device status recognition cheetah to obtain the best attack position, that is, search for the image data sets D1 to D1 of the operation status of the IoT device in the search space of the abnormal operation status image data set E of different types of IoT devices. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation status type of the IoT device; S525, execute S522, S523, S524 until the maximum number of iterations is met, and output the image data set D1 to D1 corresponding to the operation status of the IoT device. κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation status type of the IoT device; S526, compare the output in step S525 with the IoT device operation status image data set D1 to D κ The abnormal operation state image data of the different types of Internet of Things devices that match the operation state image data of the Internet of Things device y The corresponding abnormal operation state type of the IoT device is identified by data to generate the abnormal operation state analysis data set of the IoT device F = (f1,…,f o ,…,f κ ), where f o Indicates the characteristic data c of the IoT device of the specific operation and maintenance communication line o Corresponding to the abnormal operation status analysis data of IoT devices.

7. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 6 is characterized in that: The S6 comprises the following steps: S61, the operation and maintenance time point data t dang , the IoT operation and maintenance communication line object identification data a' n , the specific operation and maintenance communication line IoT device feature data set C, and the IoT device abnormal operation status analysis data set F are arranged from top to bottom according to the logic tree principle, and the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set F of ... C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and maintenance communication line IoT device feature data set C of the specific operation and κ and the IoT device operation abnormal state analysis data f1 to f in the IoT device operation abnormal state analysis data set F κ According to the logic tree principle, the data is combined and arranged from left to right to construct the real-time operation and maintenance management display data H of the Internet of Things platform.

8. The operation and maintenance management method of the Internet of Things platform based on artificial intelligence according to claim 7 is characterized in that: The S7 comprises the following steps: S71, the real-time operation and maintenance management display data H of the Internet of Things platform is used to perform the Internet of Things platform operation and maintenance management result feedback display task through the display screen of the Internet of Things platform operation and maintenance management terminal.

9. An artificial intelligence-based Internet of Things platform operation and maintenance management system, used to implement the artificial intelligence-based Internet of Things platform operation and maintenance management method described in any one of claims 1 to 8, characterized in that: The system includes an Internet of Things platform operation and maintenance object confirmation module, an Internet of Things platform operation and maintenance status analysis module, and an Internet of Things platform operation and maintenance result display module.

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