Equipment operation and maintenance management method, system, equipment and medium based on industrial Internet of Things

Abstract

The present application discloses a method, system, equipment and medium for equipment operation and maintenance management based on the industrial Internet of Things, and relates to the technical field of equipment operation and maintenance management. The present application provides a method for equipment operation and maintenance management based on the industrial Internet of Things, wherein the sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database, and the method includes the following steps: obtaining the original data collected by the object platform through the general database, and performing anomaly detection on the original data to obtain abnormal data; establishing a communication link between the general database and the target sensor sub-platform; the target sensor sub-platform obtains historical big data of historical abnormal data that causes historical abnormal failures of the object platform, and predicts the type of abnormal failure caused by the abnormal data to the object platform based on the historical big data; and adjusting the maintenance strategy according to the type of abnormal failure.

Description

Technical Field

[0001] The present application relates to the technical field of equipment operation and maintenance management, and in particular to an equipment operation and maintenance management method, system, equipment and medium based on industrial Internet of Things. Background Art

[0002] The Industrial Internet of Things is the product of the deep integration of current information technology and industrial manufacturing. It is based on the Internet of Things technology and realizes the digitization, intelligence and automation of industrial production and operation processes by connecting and interconnecting industrial equipment, sensors, systems and personnel. The Industrial Internet of Things focuses on interconnected equipment and systems used in the industrial field. It uses the core technologies of the Internet of Things, such as sensors, communication technology, cloud computing and big data analysis, to achieve data interaction and collaboration between devices.

[0003] Industrial IoT devices usually undergo maintenance when failures occur or on a regular basis. Regular maintenance is usually some routine maintenance items. If the equipment fails during the time period between adjacent maintenance nodes, or if an abnormality occurs during non-routine maintenance items, the problem cannot be eliminated in time. When maintenance is performed after a device fails, production will be interrupted, affecting the normal production of the product. Summary of the invention

[0004] The main purpose of this application is to provide an equipment operation and maintenance management method, system, equipment and medium based on the industrial Internet of Things, aiming to solve the technical problem in the prior art that maintenance after equipment failure will lead to production interruption and affect the normal production of products.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides a device operation and maintenance management method based on the industrial Internet of Things, which is used in an industrial Internet of Things system, wherein the industrial Internet of Things includes a management platform, a sensor network platform and an object platform that establish communication in sequence, and the sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database, and the method includes the following steps:

[0006] Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data;

[0007] Establishing a communication link between the master database and the target sensor sub-platform;

[0008] The target sensor sub-platform acquires historical big data of historical abnormal data causing historical abnormal faults of the target platform, and predicts the type of abnormal fault caused by the abnormal data to the target platform according to the historical big data;

[0009] The maintenance strategy is adjusted according to the abnormal fault type.

[0010] Optionally, the step of performing anomaly detection on the original data to obtain abnormal data includes:

[0011] Acquire a time series of the original data;

[0012] Determine a target preset interval according to the original data and a time attribute corresponding to the original data, compare the original data with the target preset interval, and determine whether the original data is abnormal data;

[0013] If yes, marking the original data as abnormal data;

[0014] If not, the management platform processes the original data according to a preset strategy.

[0015] Optionally, the step of determining a target preset interval according to the original data and a time attribute corresponding to the original data, comparing the original data with the target preset interval, and judging whether the original data is abnormal data includes:

[0016] Determining the target preset interval and the determination preset interval according to the target task of the object platform, wherein the target preset interval is within the determination preset interval;

[0017] If a preset number of consecutive raw data do not belong to the preset determination interval, marking the raw data as first abnormal data;

[0018] If a continuous preset number of the original data belong to the preset determination interval and do not belong to the target preset interval, marking the original data as second abnormal data;

[0019] The priority of processing the first abnormal data is higher than the priority of processing the second abnormal data.

[0020] Optionally, the abnormal data is divided into several data sets according to the type of the abnormal data, and the several data sets are sorted according to the priority of the data sets to obtain a priority sorting result, so that the target sensing sub-platform processes the abnormal data according to the priority sorting result.

[0021] Optionally, the step of obtaining historical big data of historical abnormal data causing historical abnormal failures of the target platform, and predicting the type of abnormal failure caused by the abnormal data to the target platform according to the historical big data includes:

[0022] Obtain historical abnormal data related to historical abnormal failures generated by the target platform;

[0023] The historical abnormal data is used as input, and the types of historical abnormal faults are used as output to train the prediction model;

[0024] The prediction model is used to predict the abnormal fault type based on the abnormal data.

[0025] Optionally, the abnormal fault type is at least one of a mechanical fault, an electrical fault, a control system fault, and a software and communication fault.

[0026] Optionally, the step of adjusting the maintenance strategy according to the abnormal fault type includes:

[0027] When the abnormal fault type is a mechanical fault, the bearings, gearboxes and transmission structures are maintained to reduce the probability of the above faults;

[0028] When the abnormal fault type is an electrical fault, the motor, power supply and line are maintained to reduce the probability of the above fault;

[0029] When the abnormal fault type is a control system fault, the programmable logic controller, sensor and automation system are maintained to reduce the probability of the above fault;

[0030] When the abnormal fault type is software and communication fault, network communication and software are maintained to reduce the probability of the above faults.

[0031] In a second aspect, the present application provides an equipment operation and maintenance management system based on the industrial Internet of Things, including a management platform, a sensor network platform and an object platform that establish communication in sequence, wherein the sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database:

[0032] The sensor network platform is configured as follows:

[0033] Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data;

[0034] Establishing a communication link between the master database and the target sensor sub-platform;

[0035] The target sensor sub-platform acquires historical big data of historical abnormal data causing historical abnormal faults of the target platform, and predicts the type of abnormal fault caused by the abnormal data to the target platform according to the historical big data;

[0036] The management platform is configured as follows:

[0037] The maintenance strategy is adjusted according to the abnormal fault type.

[0038] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0039] In a fourth aspect, the present application provides a computer storage medium, wherein a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement the method as described above.

[0040] Beneficial effects that this application can achieve:

[0041] The embodiments of the present application propose an equipment operation and maintenance management method, system, device and medium based on the industrial Internet of Things, which are used in an industrial Internet of Things system. The industrial Internet of Things includes a management platform, a sensor network platform and an object platform that establish communication in sequence. The sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database. The method includes the following steps: obtaining original data collected by the object platform through the general database, and performing anomaly detection on the original data to obtain abnormal data; establishing a communication link between the general database and a target sensor sub-platform; the target sensor sub-platform obtains historical big data of historical abnormal data that causes historical abnormal failures of the object platform, and predicts the type of abnormal failure caused by the abnormal data to the object platform based on the historical big data; and adjusting the maintenance strategy according to the type of abnormal failure. By monitoring the data collected by the object platform, when abnormal data is obtained, a new communication link is established to process the abnormal data so that the abnormal data can be processed in time, and according to the historical abnormal failure types corresponding to the historical abnormal data, the abnormal failures that may be caused by the abnormal data are predicted, so as to analyze the operation data of the equipment, predict the possible failures of the equipment, perform preventive maintenance on the equipment in advance, reduce the occurrence of production interruptions due to equipment failures, and maintain the smooth progress of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the equipment operation and maintenance management method in this application;

[0043] Figure 2 This is a schematic diagram of the framework of the service platform involved in this application;

[0044] Figure 3 This is a schematic diagram of the framework of the management platform involved in this application;

[0045] Figure 4 A schematic diagram of the framework of the sensor network platform involved in this application.

[0046] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] 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.

[0048] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0051] Example 1

[0052] Reference Figure 1The first embodiment of the present application provides an equipment operation and maintenance management method based on the industrial Internet of Things, which is used in the industrial Internet of Things system. The industrial Internet of Things includes a management platform, a sensor network platform and an object platform that establish communication in sequence. The sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database. The method includes the following steps:

[0053] S1. Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data.

[0054] The target platform can be an automatic production line; it can be various sensors, such as photoelectric sensors, temperature and humidity sensors, pressure sensors, etc.; it can be intelligent production inspection equipment, such as robots, visual inspection equipment, AGV, etc. The original data can include the target platform's bearing and gearbox operating data, line current and voltage data, programmable logic controller operating data, sensor operating data, software communication logs, equipment temperature, equipment pressure, ambient temperature, ambient humidity, etc.

[0055] The acquisition of abnormal data by processing the raw data through the sensor network platform can be based on the normal distribution assumption: assuming that the data follows the normal distribution, the normal range of the data is determined by calculating the mean and standard deviation, and data points outside a certain range are considered abnormal. Abnormal data can also be acquired through machine learning-based methods: if the abnormal data labels are known, classification algorithms (such as decision trees, random forests, etc.) can be used to train the model to identify new abnormal data.

[0056] S2. Establishing a communication link between the master database and the target sensor sub-platform.

[0057] In a specific implementation, the general database is used to receive and store the original data sent by the target device. The general database is connected to at least two sensor sub-platforms, at least one sensor sub-platform is reserved in a standby state, and the other sensor sub-platforms are used to receive the original data sent by the target platform according to a preset strategy. When abnormal data is monitored, a communication sprocket is established with the sensor sub-platform in the standby state to process the abnormal data. The target sensor sub-platform belongs to the sensor sub-platform in the standby state. By establishing a new communication link, the abnormal data can be processed in time, and the normal data is processed normally by other sensor sub-platforms according to the preset strategy. In the process of processing the abnormal data, the processing channel of the normal data will not be occupied, and the normal processing of the original data will not be affected.

[0058] S3. The target sensor sub-platform obtains historical big data of historical abnormal data that causes historical abnormal failures of the target platform, and predicts the type of abnormal failure caused by the abnormal data to the target platform based on the historical big data.

[0059] Historical big data refers to a large amount of data that has been continuously collected and stored in the general database by object platforms in the industrial Internet of Things system, such as various industrial equipment and sensors, over a period of time. These data not only include the normal operating status information of the equipment, but also cover abnormal data when the equipment fails, as well as various environmental parameters and operation records related to it.

[0060] The composition of historical big data may usually include the following aspects:

[0061] Timestamp: records the specific time when the data is generated, which facilitates subsequent data analysis and fault tracing.

[0062] Device identification: Identify the source device of data to ensure data accuracy and traceability.

[0063] State parameters: various physical or chemical quantities that reflect the operating status of the equipment, such as temperature, pressure, speed, current, etc.

[0064] Abnormal data: Data generated by equipment when it is in a faulty or abnormal state, which is an important basis for fault prediction and diagnosis.

[0065] Environmental parameters: external conditions that affect the operation of the equipment, such as ambient temperature, humidity, air pressure, etc.

[0066] Operation records: various operations performed on the equipment, such as starting, stopping, adjusting parameters, etc. These operations may be related to the occurrence of equipment failures.

[0067] In a specific implementation method, the historical abnormal data is sorted, and the detected historical abnormal data is classified according to the fault type, occurrence time, severity and other dimensions, and each type of abnormal data is labeled accordingly. The sorted historical abnormal data is stored in a database to ensure the traceability and queryability of the data. At the same time, a data index and query mechanism is established to facilitate subsequent data analysis and mining. Features useful for predicting the type of abnormal fault are extracted from the historical abnormal data, such as the trend of changes in sensor readings before the abnormality occurs, changes in ambient temperature, load conditions, etc. Select a suitable machine learning or deep learning model (such as logistic regression, random forest, gradient boosting tree, neural network, etc.) according to the specific needs of the problem. Use historical abnormal data as a training set to train and optimize the model so that it can accurately predict the type of abnormal fault.

[0068] S4. Adjust the maintenance strategy according to the abnormal fault type.

[0069] In a specific implementation method, the trained prediction model is deployed to the monitoring system of the target sensing sub-platform or the object platform to achieve real-time prediction of abnormal fault types. Once a certain type of abnormal fault is predicted to occur, an alarm message is immediately sent to relevant personnel. According to the prediction results, corresponding fault prevention measures or response plans are taken in advance to reduce the probability of fault occurrence or reduce the impact of the fault on the object platform.

[0070] Example 2

[0071] This embodiment provides an equipment operation and maintenance management method based on industrial Internet of Things, which is used in an industrial Internet of Things system. The industrial Internet of Things includes a management platform, a sensor network platform and an object platform that establish communication in sequence. The sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database. The method includes the following steps:

[0072] S1. Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data.

[0073] The step of performing anomaly detection on the original data to obtain abnormal data includes:

[0074] S11, obtaining the time series of the original data;

[0075] S12, determining a target preset interval according to the original data and a time attribute corresponding to the original data, comparing the original data with the target preset interval, and determining whether the original data is abnormal data;

[0076] Specifically, the value range of the preset interval is set according to the processing task of the object platform or according to the process conditions. The preset interval is a criterion for judging whether the original data obtained by the object platform during this time is abnormal. Taking the production process as an example, during the corresponding time period of 13:00-14:00, the temperature change range of the reactor during this time is 150℃-190℃, and the target preset interval can be 150℃-190℃.

[0077] S13, determining a target preset interval according to the original data and a time attribute corresponding to the original data, comparing the original data with the target preset interval, and determining whether the original data is abnormal data;

[0078] The step of determining whether the original data is abnormal data includes:

[0079] S131, determining the target preset interval and the determination preset interval according to the target task of the object platform, wherein the target preset interval is within the determination preset interval;

[0080] Specifically, taking the above production process as an example for further explanation, the target preset interval is 150℃-190℃, and the judgment preset interval can be 140℃-200℃. In the two intervals of 140℃-150℃ and 190℃-200℃, it may be just due to the fluctuation of the object platform, and the object platform has not yet failed, but only some pipeline interface positions are loose, or the heat dissipation structure is slightly blocked, or the heating structure is partially aged, etc., which does not affect the normal operation of the object platform, but it is easy to cause the object platform to fail under this working condition for a long time. By adding a judgment preset interval, the original data is monitored for abnormalities, so as to analyze the original data and obtain the abnormal failure of the object platform in time, so as to perform preventive maintenance on the equipment in time.

[0081] S132, if a preset number of consecutive original data do not belong to the preset determination interval, marking the original data as first abnormal data;

[0082] Specifically, based on the above production process example, the continuous preset number can be set according to the process conditions of the data. For example, when the frequency of data acquisition is high, the continuous preset number can be 5% of the total amount of data acquired. If the frequency of data acquisition is low, the continuous preset number can be 1% of the total amount of data acquired. When the amount of data acquired is small, the continuous preset number can be 3. When the original data is outside the judgment preset interval, for example, when the original data continuously appears 100°C, 101°C, 102°C and 104°C, etc., which do not belong to the judgment preset interval, the above original data is determined to be the first abnormal data. The first abnormal data indicates that the object platform has a fault, which needs to be processed in time to eliminate the abnormal fault.

[0083] S133, if a continuous preset number of the original data belong to the preset determination interval and do not belong to the target preset interval, mark the original data as second abnormal data;

[0084] Specifically, based on the above production process example, similar to the determination of the first abnormal data, for example, when the original data continuously appears 141°C, 142°C, 142°C and 147°C, which belong to the preset determination interval, but do not belong to the target preset interval original data, the above original data is marked as the second abnormal data. The second abnormal data indicates that the original data is abnormal, and there may be some abnormalities in the object platform. By performing preventive maintenance on the object platform, the influence of the above abnormalities can be eliminated; although the above abnormalities do not affect the normal operation of the object platform, being in the above working conditions for a long time may cause the object platform to malfunction.

[0085] S134. The priority of processing the first abnormal data is higher than the priority of processing the second abnormal data.

[0086] Specifically, the first abnormal data indicates that the target platform has failed and needs to be repaired in time to eliminate the failure and ensure normal operation. The second abnormal data indicates that the target platform has not had any obvious failure for the time being and does not affect the normal operation of the equipment. The above defects can be eliminated by maintaining the equipment. If the corresponding maintenance is not performed for a long time, the target platform may fail and need to be shut down for maintenance.

[0087] S14. If the original data is within the target preset interval, the original data is determined to be non-abnormal data, and the management platform processes the original data according to a preset strategy.

[0088] The preset strategy means that when the original data is judged as non-abnormal data, the management platform will process the data according to a series of pre-set rules and processes. For ease of understanding, an example is given below:

[0089] Assume that in an industrial IoT system, the management platform receives raw data from the sensor sub-platform about the operating temperature of a machine. The preset strategy may include the following steps:

[0090] Data verification and cleaning: Check that the temperature data is complete and there are no missing or duplicate data points. Verify the accuracy of the temperature data and ensure that they fall within a reasonable range (such as -20℃ to 100℃). Clean up obviously wrong temperature data, such as -500℃ or 500℃.

[0091] Data storage and management: Store the temperature data after cleaning in the database according to the timestamp and machine ID. Index the data to quickly query the temperature data of a specific time period or a specific machine.

[0092] Data analysis and mining: Apply statistical analysis methods to calculate the mean, standard deviation, and trend of temperature data. Use machine learning algorithms to identify correlation patterns between temperature data and machine failures.

[0093] Alarm and notification: Set temperature thresholds, such as triggering an alarm when the temperature exceeds 80°C. When the temperature reaches or exceeds the threshold, relevant personnel will be notified via email, SMS or system.

[0094] Data visualization and reporting: Generate real-time charts and dashboards of temperature data to show the operating temperature trend of the machine. Generate monthly operation and maintenance reports to summarize the machine's temperature performance, number of failures, and maintenance costs.

[0095] Optionally, the abnormal data is divided into several data sets according to the type of the abnormal data, and the several data sets are sorted according to the priority of the data sets to obtain a priority sorting result, so that the target sensing sub-platform processes the abnormal data according to the priority sorting result.

[0096] Specifically, by classifying the source and type of abnormal data, first classify it according to whether the abnormal data comes from the same device or the same production line, and then classify it according to the type of data, divide the abnormal data into data sets, and prioritize each data set according to its importance. The priority ranking results are obtained by sorting them in order from high to low, so that the target sensor sub-platform can process the data in order from high to low priority.

[0097] S2, establishing a communication link between the master database and the target sensor sub-platform;

[0098] Specifically, the main database is connected to a number of sensor sub-platforms, at least one sensor sub-platform is set in a standby state, and the target sensor sub-platform belongs to the sensor network sub-platform in the standby state.

[0099] S3, the target sensor sub-platform obtains historical big data of historical abnormal data causing historical abnormal faults of the target platform, and predicts the type of abnormal fault caused by the abnormal data to the target platform according to the historical big data;

[0100] The step of predicting the abnormal fault type caused by the abnormal data on the target platform according to the historical big data includes:

[0101] S31, obtaining historical abnormal data related to historical abnormal failures generated by the target platform;

[0102] Specifically, the acquired historical abnormal data is preprocessed, and the historical abnormal data is cleaned to remove invalid or abnormal values ​​to ensure the accuracy and reliability of the data. Features useful for predicting abnormal fault types are extracted from the historical abnormal data, such as the trend of sensor reading changes before the abnormality occurs, ambient temperature changes, load conditions, etc.

[0103] S32, using the historical abnormal data as input and the types of historical abnormal faults as output to train a prediction model;

[0104] S33. Predicting the abnormal fault type based on the abnormal data using the prediction model.

[0105] S4. Adjust the maintenance strategy according to the abnormal fault type.

[0106] Optionally, the abnormal fault type is at least one of a mechanical fault, an electrical fault, a control system fault, a software and communication fault.

[0107] The step of adjusting the maintenance strategy according to the abnormal fault type includes:

[0108] When the abnormal fault type is a mechanical fault, the bearings, gearboxes and transmission structures are maintained to reduce the probability of the above faults;

[0109] Specifically, bearing damage may be caused by bearing failure due to wear, overload or insufficient lubrication; problems in the gearbox may be gear wear, breakage or misalignment; problems in the transmission structure may be broken, loose or worn belts, chains or couplings.

[0110] When the abnormal fault type is an electrical fault, the motor, power supply and line are maintained to reduce the probability of the above fault;

[0111] Specifically, motor failure may be caused by motor overheating, short circuit, open circuit or insulation failure; power supply problems may be caused by voltage fluctuations, power interruption or poor power quality; line problems may be caused by aging of wires, short circuit, open circuit or poor contact.

[0112] When the abnormal fault type is a control system fault, the programmable logic controller, sensor and automation system are maintained to reduce the probability of the above fault;

[0113] Specifically, the cause of a programmable logic controller failure may be a software error, hardware failure or communication interruption; the cause of a sensor failure may be sensor failure, inaccurate readings or signal loss; the cause of an automation system failure may be a logic error, program error or actuator failure.

[0114] When the abnormal fault type is software and communication fault, network communication and software are maintained to reduce the probability of the above faults.

[0115] Specifically, network communication failures may be caused by network delays, packet loss, interruptions or security attacks; data processing errors may be caused by data loss, error processing or synchronization issues; software crashes or errors may be caused by software defects, version incompatibility or resource exhaustion.

[0116] Example 3

[0117] On the basis of Example 1, this embodiment provides an equipment operation and maintenance management system based on the industrial Internet of Things, including a user platform, a service platform, a management platform, a sensor network platform and an object platform that establish communication in sequence, and the sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database:

[0118] The sensor network platform is configured as follows:

[0119] Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data;

[0120] Establishing a communication link between the master database and the target sensor sub-platform;

[0121] The target sensor sub-platform acquires historical big data of historical abnormal data causing historical abnormal faults of the target platform, and predicts the type of abnormal fault caused by the abnormal data to the target platform according to the historical big data;

[0122] The management platform is configured as follows:

[0123] The maintenance strategy is adjusted according to the abnormal fault type.

[0124] The user platform is configured to provide front-end service functions to users; users obtain the required perceived service information through the user platform, process the perceived service information, and convert it into user perceived information; users analyze the user perceived information and make corresponding decisions based on their own wishes, and convert the user perceived information into user control information through the corresponding information system and send it to the service platform, thereby expressing the user's corresponding service demand intention.

[0125] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., which realize user-side services through integration with user information system software.

[0126] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entity of the service platform includes various servers.

[0127] The management platform is configured to perform at least one of equipment operation status monitoring management, data monitoring management, equipment parameter management, and life cycle management; the management platform is an operational coordination platform for the Internet of Things, which may include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0128] The sensor network platform is configured to perform at least one of network management, command management, device status management, data protocol management, data analysis, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides data communication, analysis, identification, classification, and other functions, avoiding the direct aggregation of data from various object platforms on the management platform, resulting in redundant data on the management platform and low data processing efficiency; the physical entities of the object platform include various gateways, edge computing devices, etc.

[0129] The object platform is configured to perform specific production work such as production control, detection, and measurement; the physical entities in the object platform include various production equipment, sensors, etc.

[0130] Optionally, the sensor network platform includes a master database in communication with the management platform, and at least two sensor network sub-platforms in communication with the master database;

[0131] Optionally, each sensor network sub-platform corresponds to an API function or API server;

[0132] Example 4

[0133] This embodiment provides a device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above methods.

[0134] Example 5

[0135] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement any of the above methods.

[0136] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0137] In the above embodiments of the present disclosure, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0140] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a non-volatile storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0142] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An equipment operation and maintenance management method based on industrial Internet of Things, used in an industrial Internet of Things system, wherein the industrial Internet of Things includes a management platform, a sensor network platform and an object platform that establish communication in sequence, and the sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database, characterized in that: The method comprises the following steps: Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data; Establishing a communication link between the master database and the target sensor sub-platform; The target sensor sub-platform acquires historical big data of historical abnormal data causing historical abnormal faults of the target platform, and predicts the type of abnormal fault caused by the abnormal data to the target platform according to the historical big data; The maintenance strategy is adjusted according to the abnormal fault type.

2. The equipment operation and maintenance management method based on industrial Internet of Things according to claim 1, characterized in that: The step of performing anomaly detection on the original data to obtain abnormal data includes: Acquire a time series of the original data; Determine a target preset interval according to the original data and a time attribute corresponding to the original data, compare the original data with the target preset interval, and determine whether the original data is abnormal data; If yes, marking the original data as abnormal data; If not, the management platform processes the original data according to a preset strategy.

3. The device operation and maintenance management method based on the Internet of Things according to claim 2, characterized in that: The step of determining a target preset interval according to the original data and a time attribute corresponding to the original data, comparing the original data with the target preset interval, and judging whether the original data is abnormal data includes: Determining the target preset interval and the determination preset interval according to the target task of the object platform, wherein the target preset interval is within the determination preset interval; If a preset number of consecutive raw data do not belong to the preset determination interval, marking the raw data as first abnormal data; If a continuous preset number of the original data belong to the preset determination interval and do not belong to the target preset interval, marking the original data as second abnormal data; The priority of processing the first abnormal data is higher than the priority of processing the second abnormal data.

4. The device operation and maintenance management method based on the Internet of Things according to claim 1, characterized in that: The abnormal data are divided into several data sets according to the types of the abnormal data, and the several data sets are sorted according to the priorities of the data sets to obtain a priority sorting result, so that the target sensor sub-platform processes the abnormal data according to the priority sorting result.

5. The device operation and maintenance management method based on the Internet of Things according to claim 1, characterized in that: The step of obtaining historical big data of historical abnormal data causing historical abnormal failures of the target platform, and predicting the type of abnormal failure caused by the abnormal data to the target platform according to the historical big data, comprises: Obtain historical abnormal data related to historical abnormal failures generated by the target platform; The historical abnormal data is used as input, and the types of historical abnormal faults are used as output to train the prediction model; The prediction model is used to predict the abnormal fault type based on the abnormal data.

6. The device operation and maintenance management method based on the Internet of Things according to claim 1, characterized in that: The abnormal fault type is at least one of a mechanical fault, an electrical fault, a control system fault, and a software and communication fault.

7. The device operation and maintenance management method based on the Internet of Things according to claim 6, characterized in that: The step of adjusting the maintenance strategy according to the abnormal fault type includes: When the abnormal fault type is a mechanical fault, the bearings, gearboxes and transmission structures are maintained to reduce the probability of the above faults; When the abnormal fault type is an electrical fault, the motor, power supply and line are maintained to reduce the probability of the above fault; When the abnormal fault type is a control system fault, the programmable logic controller, sensor and automation system are maintained to reduce the probability of the above fault; When the abnormal fault type is software and communication fault, network communication and software are maintained to reduce the probability of the above faults.

8. An equipment operation and maintenance management system based on industrial Internet of Things, characterized in that: It includes a management platform, a sensor network platform and an object platform that establish communication in sequence, wherein the sensor network platform includes a general database that communicates with the management platform and the object platform respectively, and at least two sensor sub-platforms that communicate with the general database: The sensor network platform is configured as follows: Acquire the original data collected by the object platform through the general database, and perform anomaly detection on the original data to obtain abnormal data; Establishing a communication link between the master database and the target sensor sub-platform; The target sensor sub-platform acquires historical big data of historical abnormal data causing historical abnormal faults of the target platform, and predicts the type of abnormal fault caused by the abnormal data to the target platform according to the historical big data; The management platform is configured as follows: The maintenance strategy is adjusted according to the abnormal fault type.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

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