Equipment data monitoring method and device, electronic equipment and storage medium

Abstract

The invention relates to an equipment data monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current equipment data and abnormal historical equipment data corresponding to equipment to be monitored; performing analysis processing based on the current equipment data and the abnormal historical equipment data to obtain graded classification data corresponding to the current equipment data, the graded classification data being used for determining a fault grading result corresponding to the current equipment data; comparing the graded and classified data with a preset early warning interval to obtain a comparison result which is used for determining whether the current equipment data is abnormal or not; and determining that the current equipment data is abnormal based on the comparison result, and generating a target early warning signal. The technical problems of poor early warning accuracy and high maintenance cost when monitoring and early warning are carried out on the equipment data in the prior art are solved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent early warning monitoring technology, and in particular to a device data monitoring method, device, electronic device and storage medium. Background Art

[0002] During the operation of equipment, equipment failure or abnormal operation often occurs due to aging, abnormal load and operational errors. For example, temperature failure may cause fire accidents, and mechanical failure may cause equipment damage, power outages, injuries, etc. Therefore, real-time and continuous monitoring and early warning of equipment operation are of great significance for improving equipment operation safety, extending equipment service life and reducing accident losses. At present, real-time monitoring and early warning of equipment operation mainly rely on manual inspections and alarms based on preset thresholds, but manual inspections cannot achieve remote real-time monitoring and cannot detect equipment operation abnormalities in time, which has a great impact on equipment operation safety, equipment service life and accident losses. The alarm method based on preset thresholds cannot adaptively adjust the warning threshold according to the equipment operation status, and unreasonable threshold settings are prone to frequent warnings or missed reports, resulting in equipment operation abnormalities not being discovered and handled in time.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present disclosure provide a device data monitoring method, apparatus, electronic device and storage medium to at least solve the technical problems of poor warning accuracy and high maintenance cost when monitoring and warning device data in related technologies.

[0005] According to one aspect of an embodiment of the present disclosure, there is provided a device data monitoring method, including: acquiring current device data and abnormal historical device data corresponding to a device to be monitored; performing analysis and processing based on the current device data and the abnormal historical device data to obtain hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine a fault classification result corresponding to the current device data; performing comparison and processing using the hierarchical classification data and a preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether the current device data has an abnormality; and generating a target warning signal in response to determining that the current device data has an abnormality based on the comparison result.

[0006] Optionally, analyzing and processing the current device data and abnormal historical device data to obtain hierarchical classification data corresponding to the current device data includes: classifying the current device data to obtain data classification results; and hierarchically processing the data classification results and abnormal historical device data to obtain hierarchical classification data.

[0007] Optionally, performing hierarchical processing based on the data classification results and abnormal historical equipment data to obtain hierarchical classification data includes: determining historical fault frequencies based on abnormal historical equipment data; performing multiple fault grading processes on the data classification results according to the historical fault frequencies to obtain hierarchical classification data.

[0008] Optionally, in response to determining that the current device data is abnormal based on the comparison result, generating a target warning signal includes: in response to determining that the graded and classified data is in a preset warning interval based on the comparison result, generating a target warning signal.

[0009] Optionally, the device data monitoring method further includes: determining device positioning information corresponding to the device to be monitored based on the target warning signal, wherein the device positioning information is used to determine the location information of the device to be monitored on a map; and displaying the device positioning information in a graphical user interface.

[0010] Optionally, the device data monitoring method further includes: determining an early warning detection control signal based on a preset delay threshold and a cyclic period device data acquisition signal; and generating an audible, visual and voice alarm signal based on the early warning detection control signal and the graded classification data.

[0011] According to another aspect of the embodiment of the present disclosure, there is also provided an equipment data monitoring device, including: an acquisition module, used to acquire current equipment data and abnormal historical equipment data corresponding to the equipment to be monitored; an analysis module, used to perform analysis and processing based on the current equipment data and the abnormal historical equipment data, to obtain hierarchical classification data corresponding to the current equipment data, wherein the hierarchical classification data is used to determine a fault classification result corresponding to the current equipment data; a comparison module, used to perform comparison and processing using the hierarchical classification data and a preset warning interval, to obtain a comparison result, wherein the comparison result is used to determine whether the current equipment data has an abnormality; a generation module, used to generate a target warning signal in response to determining that the current equipment data has an abnormality based on the comparison result.

[0012] Optionally, the analysis module is further used to: classify the current device data to obtain a data classification result; and perform hierarchical processing based on the data classification result and abnormal historical device data to obtain hierarchical classified data.

[0013] Optionally, the analysis module is also used to: determine the historical fault frequency based on abnormal historical equipment data; perform multiple fault classification processes on the data classification results according to the historical fault frequency to obtain graded classification data.

[0014] Optionally, the generation module is further used to: generate a target warning signal in response to determining that the graded and classified data is in a preset warning interval based on the comparison result.

[0015] Optionally, the equipment data monitoring device also includes: a determination module, used to determine the equipment positioning information corresponding to the equipment to be monitored based on the target warning signal, wherein the equipment positioning information is used to determine the map location information of the equipment to be monitored; a display module, used to display the equipment positioning information in a graphical user interface.

[0016] Optionally, the determination module is also used to determine the early warning detection control signal based on a preset delay threshold and a cycle period device data acquisition signal; the generation module is also used to generate an audible, visual and voice alarm signal based on the early warning detection control signal and the graded classification data.

[0017] According to another aspect of an embodiment of the present disclosure, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the device data monitoring method in the embodiment of the present disclosure.

[0018] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the device data monitoring method in the embodiment of the present disclosure.

[0019] According to another aspect of the embodiments of the present disclosure, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the device data monitoring method in the embodiments of the present disclosure is implemented.

[0020] In the disclosed embodiment, by acquiring the current device data and abnormal historical device data corresponding to the device to be monitored, and then analyzing and processing based on the current device data and abnormal historical device data, the hierarchical classification data corresponding to the current device data is obtained, and then the hierarchical classification data is used to compare and process with the preset warning interval to obtain a comparison result, and finally responding to determine that the current device data is abnormal based on the comparison result, a target warning signal is generated, thereby achieving real-time monitoring and abnormality detection of the device to be monitored, thereby improving the operational stability and safety of the device. As a result, the disclosed embodiment achieves the purpose of efficient and accurate intelligent early warning monitoring, thereby achieving the technical effect of improving the accuracy of the early warning results and reducing the maintenance cost, and thus solving the technical problems of poor early warning accuracy and high maintenance cost when monitoring and warning device data in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0022] Figure 1is a flow chart of a device data monitoring method according to an embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of a device data monitoring system according to an embodiment of the present disclosure;

[0024] Figure 3 It is a structural block diagram of a device data monitoring apparatus according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present disclosure, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0027] According to an embodiment of the present disclosure, a method embodiment of a device data monitoring method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] The method embodiment can be executed in an electronic device or a similar computing device including a memory and a processor. Taking running on a computer terminal as an example, the computer terminal may include one or more processors (processors may include but are not limited to central processing units (CPU), graphics processing units (GPU), digital signal processing (DSP) chips, microprocessors (MCU), programmable logic devices (Field Programmable Gate Array, FPGA), neural network processors (Neural-network Processor Unit, NPU), tensor processors (TensorProcessing Unit, TPU), artificial intelligence (Artificial Intelligence, AI) type processors and other processing devices) and a memory for storing data. Optionally, the above-mentioned computer terminal may also include a transmission device, an input and output device, and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above-mentioned structural description is only for illustration, and it does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include more or fewer components than the above-mentioned structural description, or have a configuration different from the above-mentioned structural description.

[0029] The memory can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the device data monitoring method in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, the above-mentioned device data monitoring method is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0031] The display device may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of a mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0032] Figure 1 is a flow chart of a device data monitoring method according to an embodiment of the present disclosure, such as Figure 1 As shown, the method comprises the following steps:

[0033] Step S11, obtaining current device data and abnormal historical device data corresponding to the device to be monitored;

[0034] Step S12, analyzing and processing the current device data and the abnormal historical device data to obtain hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine the fault classification result corresponding to the current device data;

[0035] Step S13, comparing the graded and classified data with the preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether there is an abnormality in the current device data;

[0036] Step S14, in response to determining that the current device data is abnormal based on the comparison result, a target warning signal is generated.

[0037] The above-mentioned equipment to be monitored may be equipment or systems whose operating status needs to be continuously monitored, including but not limited to generators in power plants, pumps in oil drilling, production line equipment in factories, servers in data centers, medical equipment, signal devices in transportation facilities, etc. The equipment to be monitored usually has multiple key operating parameters, and abnormal changes in key operating parameters may indicate that the equipment is about to fail or its performance has deteriorated.

[0038] The above-mentioned current equipment data can be operating parameters collected in real time or recently from the equipment to be monitored at a specific time point. The current equipment data can include but is not limited to various physical or electrical parameters such as temperature, pressure, vibration, speed, current, voltage, etc. of the equipment. The current equipment data reflects the actual operating status of the equipment at the time of monitoring and is an important basis for evaluating the health status of the equipment and predicting potential failures.

[0039] The above abnormal historical equipment data can be the parameter values ​​that exceed the normal range recorded during the operation of the monitored equipment within a certain time range in the past. The abnormal historical equipment data can come from the equipment's regular maintenance inspection, fault diagnosis records, or data points that exceed the preset thresholds automatically detected by the system. Abnormal historical equipment data is crucial to understanding the long-term health trend of the equipment, identifying failure modes, and evaluating the reliability of the equipment.

[0040] Exemplarily, the collected current equipment data and abnormal historical equipment data are preprocessed, including data cleaning (removing invalid or erroneous data), data format unification, data standardization, etc., to ensure data quality and lay the foundation for subsequent analysis. Feature data related to equipment failure are extracted from the preprocessed data, which can be the statistical value of a single parameter, or the combined features of multiple parameters, or even time series features, to capture the trend of parameter changes over time. The extracted feature data are classified, usually based on the type of equipment parameters or the nature of the fault. For example, performance parameters such as speed and vibration are classified into one category, and thermodynamic parameters such as temperature and pressure are classified into another category, so that different analysis models and early warning strategies can be used for different types of parameters. On the basis of classification, the data is further graded, which is usually based on the frequency and severity of abnormal historical equipment data. For example, according to the number and degree of abnormal vibration amplitude in historical records, vibration data can be divided into three levels: low risk, medium risk and high risk.

[0041] The above preset warning intervals are set based on the normal operating parameters of the equipment and the analysis of historical fault data, and usually include multiple intervals, each of which corresponds to a different warning level, such as minor warning, moderate warning, and severe warning. The preset warning intervals reflect the range of equipment parameters under different health conditions. For example, for the temperature parameter, the minor warning interval can be set to 110% to 120% of the normal operating temperature of the equipment, the moderate warning interval to 120% to 130%, and the severe warning interval to more than 130%.

[0042] The hierarchical data is compared with the preset warning interval, and the hierarchical data of each parameter will be compared with the corresponding warning interval value. The comparison process can use simple numerical comparison, or more complex statistical analysis or machine learning models to consider the correlation between parameters and the trend of historical data. After the comparison process, a series of results will be generated, indicating the relative position of the equipment parameters and the warning interval. If the hierarchical data falls within the warning interval, it means that the parameter is abnormal. The comparison result can be a binary result, such as abnormal or normal, or a multi-level result, such as slight abnormality, moderate abnormality, and severe abnormality, depending on the setting of the warning interval and the definition of the system's warning level. Based on the comparison result, it can be determined whether the current equipment data is abnormal. If the hierarchical data of any parameter exceeds the threshold of the preset warning interval, it will be considered that the equipment is abnormal and may be about to or has already failed.

[0043] By comparing the graded and classified data with the preset warning interval, the intelligent early warning monitoring system can not only detect abnormal changes in the equipment's operating status in a timely manner, but also issue graded warnings based on the severity of the abnormality, thereby prompting maintenance personnel to take corresponding response measures based on the warning level, from preventive maintenance for minor warnings to emergency shutdown inspections for serious warnings, to ensure stable operation and safe production of the equipment. This combines data-driven intelligent analysis with a rule-based decision-making mechanism, effectively improving the accuracy and efficiency of fault warnings.

[0044] When it is determined according to the comparison result that the current device data is abnormal, it means that the operating state of the device is unstable and there is a potential risk of failure. According to the warning interval that the abnormal data falls into and the fault classification result, the level of the target warning signal is determined, and then the corresponding target warning signal is generated. The target warning signal can be in various forms such as text messages, emails, sound and light alarms, voice notifications, etc., depending on the configuration of the system and the user's preferences, and is not limited by the embodiments of the present disclosure. The target warning signal usually contains information such as the name of the abnormal parameter, the current value, the abnormal level, and the recommended response action.

[0045] After the target warning signal is generated, it can be sent to a preset recipient, such as a device operator, maintenance engineer, or fault handling team. Notification sending can be achieved through a variety of channels, including but not limited to a local display of the device, an application on a mobile device, a dedicated warning system platform, etc., which are not limited in the embodiments of the present disclosure.

[0046] In the disclosed embodiment, the server can obtain the current device data and abnormal historical device data of all devices to be monitored, so as to realize the collection of scattered data and to facilitate the knowledge of the correlation change characteristics between the current and previous data information of each device data. Further, the current device data is classified and the current device data is graded according to the abnormal historical device data, so as to realize the separation of the fused data for comparative monitoring one by one, so as to make the data information processing more organized and simple, and to clearly know the operating data status information of the device to be monitored. The graded and classified data is compared with various preset warning interval values, so as to facilitate early warning notification before various data are abnormal, so as to improve the efficiency of data monitoring. By detecting whether the graded and classified data triggers the alarm threshold, an audible, visual and voice alarm signal is output to perform secondary alarm monitoring on various data, so as to avoid the occurrence of misjudgment of various data in the detection process, and the occurrence of the situation that the problem equipment is not handled in time due to other reasons and the warning signal is not received in time.

[0047] Based on the above steps S11 to S14, by obtaining the current device data and abnormal historical device data corresponding to the device to be monitored, and then analyzing and processing based on the current device data and abnormal historical device data, the hierarchical classification data corresponding to the current device data is obtained, and then the hierarchical classification data is used to compare and process with the preset warning interval to obtain a comparison result, and finally respond to determine that the current device data is abnormal based on the comparison result, and generate a target warning signal, thereby achieving real-time monitoring and abnormality detection of the device to be monitored, thereby improving the operational stability and safety of the device. Therefore, the embodiment of the present disclosure achieves the purpose of efficient and accurate intelligent early warning monitoring, thereby achieving the technical effect of improving the accuracy of the early warning results and reducing the maintenance cost, and thus solves the technical problems of poor early warning accuracy and high maintenance cost when monitoring and warning device data in the related technology.

[0048] The following is a further introduction to the device data monitoring method in the embodiment of the present application.

[0049] Optionally, in step S12, based on the current device data and the abnormal historical device data, the hierarchical classification data corresponding to the current device data is obtained by analyzing and processing, including:

[0050] Step S121, classify the current device data to obtain a data classification result;

[0051] Step S122, performing hierarchical processing based on the data classification result and abnormal historical equipment data to obtain hierarchical classification data.

[0052] Specifically, the data classification process aims to classify the current equipment data into different categories according to the characteristics of the equipment operating parameters, so as to better understand and analyze the operating status of the equipment. Exemplarily, in the classification process, the key features in the current equipment data are first identified, and each feature can reflect different aspects of the equipment to be monitored, such as thermal state or electrical state. Classification rules are set based on the nature of the features and the operating requirements of the equipment. For example, temperature parameters are divided into "low temperature", "normal" and "high temperature" categories, and vibration parameters are divided into "small vibration", "medium vibration" and "significant vibration" according to amplitude. The classification rules are then applied to classify the current equipment data to obtain the data classification results. The data classification process can also be completed automatically using algorithms, such as supervised learning algorithms such as support vector machines, decision trees, k-nearest neighbors, or unsupervised learning algorithms such as cluster analysis.

[0053] Data classification processing is based on data classification results and abnormal historical equipment data to further evaluate the risk level of the equipment status. Exemplarily, abnormal historical equipment data is analyzed to identify the correlation between different categories of data and equipment failures. The failure mode, frequency and relationship with specific data categories in historical data are important bases for determining classification rules. Classification rules are set based on historical data analysis. Classification rules may include a mapping relationship between data categories and fault levels, such as "significant vibration" combined with "high temperature" may indicate that the equipment has a high-risk fault. The classification rules are then applied, combined with the data classification results, to perform a risk assessment on the current equipment data to obtain graded and classified data. For example, the two types of data "significant vibration" and "high temperature" are marked as "high risk", while "small vibration" and "normal" temperature are marked as "low risk".

[0054] Based on the above optional embodiments, by classifying and processing the current equipment data, data classification results are obtained, and then hierarchical processing is performed based on the data classification results and abnormal historical equipment data to obtain hierarchical classification data, which not only improves the efficiency and accuracy of data processing, but also can intelligently predict the potential risks of the equipment based on the historical fault records of the equipment, so as to discover and prevent faults earlier. The generation of hierarchical classification data provides more specific and detailed information for subsequent warnings and decisions. By continuously optimizing the classification and grading rules, changes in equipment status can be identified more accurately, effectively improving the timeliness and accuracy of warnings.

[0055] Optionally, in step S122, hierarchical processing is performed based on the data classification result and the abnormal historical device data, and the hierarchical classification data obtained includes:

[0056] Step S1221, determining the historical fault frequency based on the abnormal historical equipment data;

[0057] Step S1222, performing multiple fault classification processes on the data classification results according to the historical fault frequencies to obtain graded classification data.

[0058] Specifically, first, sort out the abnormal records of parameters of different categories from the abnormal historical equipment data, including the time when the abnormality occurred, the parameter value, the duration of the abnormality, and the final fault level. Then count the number of occurrences of each parameter abnormality within a certain time range, as well as the fault level and maintenance actions caused by each abnormality, to determine the historical fault frequency, which can help identify which parameter abnormalities are more frequent, and the correlation between abnormal parameters and specific fault levels. According to the results of the historical fault frequency analysis, set the mapping rules between parameter abnormalities and fault levels. For example, if a parameter abnormality occurs frequently and causes equipment shutdowns many times, even minor abnormalities should be marked as high risk. Set grading thresholds for different fault levels, which are based on the failure mode and frequency in the historical data, as well as the equipment's operational safety and production efficiency requirements.

[0059] Based on the data classification results, the association rules between fault frequency and fault level are applied to evaluate the fault risk of each classified data. Considering the dynamic changes in the equipment operation status, the classification rules and thresholds may need to be dynamically adjusted according to the latest abnormal historical equipment data to ensure the accuracy and timeliness of the classified data. Based on the classification results of the current equipment data and the historical fault frequency information, each parameter abnormality is classified to obtain the final classified data.

[0060] Based on the above optional embodiments, by determining the historical failure frequency based on abnormal historical equipment data, and then performing multiple fault classification processing on the data classification results according to the historical failure frequency, the graded classification data is obtained, which can more accurately evaluate the operating health status of the equipment, distinguish the parameter anomalies that need to be focused on, and adjust the early warning strategy in time, so as to effectively prevent and reduce equipment failures and ensure production safety and efficiency.

[0061] Optionally, in step S14, in response to determining that the current device data is abnormal based on the comparison result, generating a target warning signal includes: in response to determining that the graded and classified data is in a preset warning interval based on the comparison result, generating a target warning signal.

[0062] Specifically, a series of warning intervals are pre-set based on the normal range of equipment operating parameters and historical fault data. The preset warning intervals are usually divided into several levels, such as minor warning, moderate warning, and severe warning, and each level corresponds to a different parameter value range and fault risk level.

[0063] Compare each parameter value in the hierarchical and classified data with the preset corresponding warning interval. If the value of a data point falls within the preset warning interval, it will be judged that the data point has a failure risk and enter the warning generation stage. The judgment of the warning condition needs to consider the severity and failure possibility of the hierarchical and classified data to ensure the accuracy and effectiveness of the warning signal.

[0064] When it is determined that one or some parameter values ​​in the hierarchical and classified data are in the preset warning interval, the system will generate a target warning signal according to the preset rules. The target warning signal can contain the following information: Equipment identification: Ensure that the signal can accurately point to a specific device or measurement point. Parameter name and value: List the parameters that trigger the warning and their current values. Warning level: Determine the severity of the warning based on which warning interval the parameter value falls. Recommended action: Provide suggestions based on the possibility of failure, such as conducting an on-site inspection, adjusting operating parameters, or shutting down immediately.

[0065] Optionally, the device data monitoring method in the embodiment of the present disclosure further includes:

[0066] Determine the device location information corresponding to the device to be monitored based on the target warning signal, wherein the device location information is used to determine the location information of the device to be monitored on the map;

[0067] Display device location information in the graphical user interface.

[0068] When a target warning signal is generated, the target warning signal usually contains a unique identifier of the device, such as the device serial number, so that the device that issues the target warning signal can be accurately located based on the unique identifier of the device. The device positioning information can be entered in advance in the device database, or obtained in real time through the Global Positioning System (GPS), radio frequency identification, wireless local area network positioning technology, etc. The device positioning information includes the longitude and latitude of the device to be monitored, ensuring that the device location can be accurately identified on the map.

[0069] Furthermore, the acquired device location information is associated with the map data and converted into a specific point on the map. The map display function is integrated in the graphical user interface, and the device location is presented using the map service. When the target warning signal is triggered, the map in the graphical user interface will automatically zoom in to the location of the device, or use visual effects such as flashing and color changes to highlight the warning device for quick identification. Other relevant information can also be displayed in the graphical user interface, such as device status, warning level, fault parameters, etc., to help operators or maintenance teams quickly understand the urgency and specific location of the problem.

[0070] Based on the above optional embodiments, by intuitively displaying the equipment location information based on the target warning signal in the graphical user interface, the intelligent early warning monitoring system greatly improves the speed and efficiency of fault response, reduces the time to find the equipment location, and ensures that the maintenance team can quickly locate the problem and take appropriate maintenance or repair measures, thereby reducing equipment downtime and improving production efficiency and safety.

[0071] Optionally, the device data monitoring method in the embodiment of the present disclosure further includes:

[0072] Determine the early warning detection control signal based on the preset delay threshold and the cycle period equipment data acquisition signal;

[0073] Generate sound, light and voice alarm signals based on the early warning detection control signal and graded classification data.

[0074] The preset delay threshold is a time standard set in the monitoring alarm module to determine whether the entire cycle of data from collection, processing to re-evaluation exceeds the normal range, thereby helping to identify potential delays or failures in the data processing process and ensuring that the system can respond to changes in equipment status in a timely manner.

[0075] Cyclic data collection of equipment is performed according to the cycle time set by the cycle timing controller to ensure continuous monitoring of equipment status. The setting of the cycle period needs to take into account factors such as the equipment's operating cycle, data change rate, and network transmission delay to ensure that the frequency of data collection matches the equipment status monitoring requirements.

[0076] When the cycle timing controller receives the signal that the data processing is complete, it starts to calculate the total time from the issuance of the data acquisition signal to the re-output of the classified data. If the total time exceeds the preset delay threshold, an early warning detection control signal will be generated, indicating that the data processing process or network connection needs to be checked to avoid failure warning failure caused by data delay.

[0077] After receiving the early warning detection control signal, the monitoring alarm module immediately checks whether the latest classification data contains any abnormal data that triggers the alarm threshold. If an abnormality is detected, the system will generate an audible, visual and voice alarm signal based on the preset alarm rules and device status changes. The generation of the signal takes into account information such as the urgency of the alarm, the location of the device and the type of fault to ensure that the alarm information is both comprehensive and accurate.

[0078] The above-mentioned sound, light and voice alarm signals not only include visual prompts (such as lights of different colors) and auditory prompts (such as alarm sounds and voice notifications), but can also be combined with map markers and equipment status displays in the graphical user interface. The multi-dimensional alarm communication mechanism ensures that no matter what environment or state the operator is in, he can quickly notice the alarm information and take timely action.

[0079] Based on the above optional embodiments, by determining the early warning detection control signal based on the preset delay threshold and the cyclic period device data acquisition signal, and then generating an audible, visual and voice alarm signal based on the early warning detection control signal and the graded and classified data, it is possible not only to monitor the device status in real time, but also to intelligently evaluate the timeliness of data processing, and to send out an audible, visual and voice alarm signal when necessary, so as to improve the fault response speed and prevention efficiency, and help reduce equipment failures and maintenance costs.

[0080] Figure 2 is a schematic diagram of a device data monitoring system according to an embodiment of the present disclosure, such as Figure 2 As shown, the system includes: a server, which is communicated with the client server to be monitored and is used to obtain current device data and abnormal historical device data; a data processing module, including a data collector and a data detection and grading classifier, which is used to collect all current device data and abnormal historical device data of the server, and classify the current device data and classify the current device data according to the abnormal historical device data, and output the graded and classified data; a first early warning module, which is connected to the data processing module signal, is used to receive the graded and classified data, and compare the graded and classified data with various preset early warning interval values ​​and then output a target early warning signal; a monitoring alarm module, which is connected to the data processing module signal, is used to receive and detect whether the graded and classified data triggers the alarm threshold, and output an audible, visual and voice alarm signal.

[0081] The above-mentioned data detection grading classifier includes a data classifier, which is connected to the data collector signal, and is used to receive all the current device data and device history data collected from the server, classify all the current device data into different categories, and output the data classification results.

[0082] The above-mentioned data detection grading classifier also includes a data classifier, which is composed of a first classifier, a second classifier, a third classifier to an Nth classifier. The first classifier, the second classifier, the third classifier to the Nth classifier are all signal-connected to the first data classifier and the data collector, and are used to receive data classification results and abnormal historical equipment data, classify the data classification results into fault levels according to the number of times the abnormal historical equipment data occurs, and output graded and classified data.

[0083] The above-mentioned first early warning module includes a first early warning detector, which is composed of a first early warning device, a second early warning device, and a third early warning device to an N-th early warning device. The first early warning device, the second early warning device, and the third early warning device to the N-th early warning device are respectively connected to the first classifier, the second classifier, and the third classifier to the N-th classifier signals. Warning interval values ​​are preset in the first early warning device, the second early warning device, and the third early warning device to the N-th early warning device, respectively, to determine whether there is an abnormality in the data in the N classifiers, and output the target warning signal.

[0084] The above-mentioned first warning module also includes a map display and a cycle timing controller. The map display is signal-connected to the first warning detector, receives the target warning signal, and outputs the map display information of the problem equipment; the cycle timing controller is signal-connected to the first warning detector and the server, receives the target warning signal, outputs the cycle period equipment data collection signal and sends it to the server.

[0085] The monitoring alarm module is provided with a delay device, and a delay threshold is preset in the delay device. The delay device is connected to the cycle timing controller signal, and is used to receive the data acquisition signal of the cycle period equipment, compare the time taken by the server to receive the data acquisition signal of the cycle period equipment to the data processing module and re-output the graded and classified data with the preset delay threshold in the delay device, and output an early warning detection control signal.

[0086] The monitoring alarm module also includes a second early warning detector and a map marking control device. The second early warning detector is signal-connected to the delay device and the data detection grading classifier. Various preset alarm thresholds are set in the second early warning detector. The second early warning detector receives the early warning detection control signal and the grading classification data, and compares the grading classification data with various preset alarm thresholds, and outputs an audible, visual and voice alarm signal; the map marking control device is signal-connected to the second alarm detector and the map display, and is used to receive the audible, visual and voice alarm signal and the map display information of the problem equipment, and mark the map position of the problem equipment that eventually fails in the map display information of the problem equipment, and output the map marking information of the faulty equipment.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0088] Figure 3 is a structural block diagram of a device data monitoring apparatus according to an embodiment of the present disclosure, such as Figure 3 As shown, the device comprises:

[0089] The acquisition module 301 is used to acquire the current device data and abnormal historical device data corresponding to the device to be monitored;

[0090] The analysis module 302 is used to analyze and process the current device data and the abnormal historical device data to obtain the hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine the fault classification result corresponding to the current device data;

[0091] Comparison module 303, used to compare the graded and classified data with the preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether there is an abnormality in the current device data;

[0092] The generating module 304 is used to generate a target warning signal in response to determining that the current device data is abnormal based on the comparison result.

[0093] Optionally, the analysis module 302 is further used to: classify the current device data to obtain a data classification result; and perform hierarchical processing based on the data classification result and abnormal historical device data to obtain hierarchical classification data.

[0094] Optionally, the analysis module 302 is further used to: determine the historical fault frequency based on the abnormal historical equipment data; and perform multiple fault classification processes on the data classification results according to the historical fault frequency to obtain graded classification data.

[0095] Optionally, the generating module 304 is further used to: in response to determining that the graded and classified data is in a preset warning interval based on the comparison result, generate a target warning signal.

[0096] Optionally, the equipment data monitoring device also includes: a determination module 305, used to determine the equipment positioning information corresponding to the equipment to be monitored based on the target warning signal, wherein the equipment positioning information is used to determine the map location information of the equipment to be monitored; a display module 306, used to display the equipment positioning information in a graphical user interface.

[0097] Optionally, the determination module 305 is further used to determine the early warning detection control signal based on the preset delay threshold and the cyclic period device data acquisition signal; the generation module 304 is further used to generate an audible, visual and voice alarm signal based on the early warning detection control signal and the hierarchical classification data.

[0098] According to another aspect of an embodiment of the present disclosure, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the device data monitoring method in the embodiment of the present disclosure.

[0099] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0100] S1, obtaining current device data and abnormal historical device data corresponding to the device to be monitored;

[0101] S2, analyzing and processing the current device data and the abnormal historical device data to obtain the hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine the fault classification result corresponding to the current device data;

[0102] S3, comparing the graded and classified data with the preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether there is an abnormality in the current device data;

[0103] S4, in response to determining that the current device data is abnormal based on the comparison result, a target warning signal is generated.

[0104] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the device data monitoring method in the embodiment of the present disclosure.

[0105] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0106] S1, obtaining current device data and abnormal historical device data corresponding to the device to be monitored;

[0107] S2, analyzing and processing the current device data and the abnormal historical device data to obtain the hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine the fault classification result corresponding to the current device data;

[0108] S3, comparing the graded and classified data with the preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether there is an abnormality in the current device data;

[0109] S4, in response to determining that the current device data is abnormal based on the comparison result, a target warning signal is generated.

[0110] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0111] According to another aspect of the embodiments of the present disclosure, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the device data monitoring method in the embodiments of the present disclosure is implemented.

[0112] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0113] S1, obtaining current device data and abnormal historical device data corresponding to the device to be monitored;

[0114] S2, analyzing and processing the current device data and the abnormal historical device data to obtain the hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine the fault classification result corresponding to the current device data;

[0115] S3, comparing the graded and classified data with the preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether there is an abnormality in the current device data;

[0116] S4, in response to determining that the current device data is abnormal based on the comparison result, a target warning signal is generated.

[0117] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0118] 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.

[0119] 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 the 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.

[0120] 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 on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0121] 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.

[0122] 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 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 storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned 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.

[0123] The above is only a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present disclosure. These improvements and modifications should also be regarded as the scope of protection of the present disclosure.

Claims

1. A device data monitoring method, characterized in that: include: Obtain current device data and abnormal historical device data corresponding to the device to be monitored; Analyzing and processing the current device data and the abnormal historical device data to obtain hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine a fault classification result corresponding to the current device data; Comparing the hierarchical and classified data with a preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether the current device data is abnormal; In response to determining that the current device data is abnormal based on the comparison result, a target warning signal is generated.

2. The device data monitoring method according to claim 1, characterized in that: The hierarchical classification data corresponding to the current device data is obtained by analyzing and processing the current device data and the abnormal historical device data, including: Classify the current device data to obtain a data classification result; The hierarchical classification data is obtained by performing hierarchical processing based on the data classification result and the abnormal historical device data.

3. The device data monitoring method according to claim 2, characterized in that: Based on the data classification result and the abnormal historical equipment data, the hierarchical classification data obtained includes: Determining the historical fault frequency based on the abnormal historical equipment data; The data classification results are subjected to multiple fault classification processes according to the historical fault frequencies to obtain the graded classification data.

4. The device data monitoring method according to claim 1, characterized in that: In response to determining that the current device data is abnormal based on the comparison result, generating the target warning signal includes: In response to determining that the graded and classified data is within the preset warning interval based on the comparison result, the target warning signal is generated.

5. The device data monitoring method according to claim 1, characterized in that: The method further comprises: Determine the device location information corresponding to the device to be monitored based on the target warning signal, wherein the device location information is used to determine the location information of the device to be monitored on a map; The device location information is displayed in a graphical user interface.

6. The device data monitoring method according to claim 1, characterized in that: The method further comprises: Determine the early warning detection control signal based on the preset delay threshold and the cycle period equipment data acquisition signal; An acoustic, visual and voice alarm signal is generated according to the early warning detection control signal and the hierarchical classification data.

7. A device data monitoring device, characterized in that: include: An acquisition module is used to acquire current device data and abnormal historical device data corresponding to the device to be monitored; An analysis module, configured to perform analysis based on the current device data and the abnormal historical device data to obtain hierarchical classification data corresponding to the current device data, wherein the hierarchical classification data is used to determine a fault classification result corresponding to the current device data; A comparison module, used to compare the graded and classified data with a preset warning interval to obtain a comparison result, wherein the comparison result is used to determine whether the current device data is abnormal; A generating module is used to generate a target warning signal in response to determining that the current device data is abnormal based on the comparison result.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the device data monitoring method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the device data monitoring method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises computer instructions, which, when executed by a processor, implement the device data monitoring method described in any one of claims 1 to 6.

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