Remote equipment access deployment system and method based on probe technology

Abstract

The invention belongs to the technical field of computers, and provides a remote equipment access deployment system and method based on a probe technology, and the system comprises a probe module, a central control platform, a database, and a user terminal. The probe module is used for collecting operation state information of equipment to be accessed, extracting key feature data and uploading the operation state information and the key feature data to the central control platform; the central control platform is used for storing the operation state information to a database, performing state diagnosis on the to-be-accessed equipment according to the key feature data to obtain a state diagnosis result, and generating and issuing an equipment configuration instruction to the probe module; the probe module is also used for applying configuration parameters in the equipment configuration instruction to the target to-be-accessed equipment and feeding back instruction execution information to the central control platform; and the central control platform is also used for sending the operation state information, the state diagnosis result and the instruction execution information to the user terminal. According to the scheme, the accuracy and reliability of the remote equipment access deployment link are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a remote device access deployment system and method based on probe technology. BACKGROUND

[0002] In today's digital era, from large mechanical equipment in industrial production, automation equipment in intelligent factories, to computers, servers in office environment, and smart home devices in daily life, the number and variety of devices are growing explosively. The stable operation of these devices is crucial to enterprise production, business development and people's quality of life. At the same time, with the rise of technologies such as Internet of Things and cloud computing, the demand for remote management of devices is increasingly urgent, and enterprises expect to be able to cross geographical restrictions to monitor, configure and maintain devices distributed in different regions in real time, in order to improve operational efficiency and reduce operation and maintenance costs.

[0003] Under this background, probe technology emerged as the times required and gradually became one of the key technologies to realize remote device management. However, the existing remote device access deployment scheme based on probe technology has many limitations. Specifically, from the perspective of device configuration management, the traditional scheme often lacks intelligent task configuration mechanism, and in terms of device access verification, the verification means of the traditional scheme is relatively single, which can only simply judge whether the device is online or not, and it is difficult to deeply detect the specific state of the device, and it is difficult to realize effective state diagnosis, so it is difficult to meet the current high reliability device access supervision demand.

[0004] In summary, the current remote device access deployment scheme based on probe technology has the technical problems of insufficient function, insufficient accuracy and reliability. SUMMARY

[0005] The present application provides a remote device access deployment system and method based on probe technology to solve the defects of insufficient function, insufficient accuracy and reliability of the current remote device access deployment scheme based on probe technology.

[0006] On the one hand, the present application provides a remote device access deployment system based on probe technology, comprising: a probe module, a central control platform, a database and a user terminal; The probe module is deployed in the device to be accessed, and the probe module, the database and the user terminal are connected to the central control platform; The probe module is used to collect the running state information of the device to be accessed, extract the key feature data in the running state information, and upload the running state information and the key feature data to the central control platform; The central control platform is configured to store the running state information to the database, perform state diagnosis on the to-be-accessed device according to the key feature data, obtain a state diagnosis result, generate and issue a device configuration instruction to the probe module, and send the running state information, the state diagnosis result, and the instruction execution information to the user terminal. The probe module is further configured to apply configuration parameters in the device configuration instruction to a target to-be-accessed device, and feed back instruction execution information to the central control platform. The central control platform is further configured to send the running state information, the state diagnosis result, and the instruction execution information to the user terminal.

[0007] According to the remote device access deployment system based on the probe technology provided by the application, the probe module collects the running state information of the to-be-accessed device, including: obtaining a load state of the to-be-accessed device in a current sampling period; determining a first target sampling frequency for the to-be-accessed device according to the load state; collecting the running state information of the to-be-accessed device in the current sampling period at the first target sampling frequency.

[0008] According to the remote device access deployment system based on the probe technology provided by the application, after the running state information of the to-be-accessed device is collected in the current sampling period at the first target sampling frequency, the probe module is further configured to: determining a numerical fluctuation amplitude of each type of data point in the running state information; determining a target type of data point whose numerical fluctuation amplitude is higher than a preset amplitude threshold, and increasing the first target sampling frequency of the target type of data point in a next sampling period to a second target sampling frequency.

[0009] According to the remote device access deployment system based on the probe technology provided by the application, the probe module extracts key feature data in the running state information, including: performing threshold filtering, time window filtering, and correlation filtering on the running state information respectively to obtain filtered information; extracting statistical features, trend features, and abnormal features in the filtered information respectively, and obtaining the key feature data according to the statistical features, the trend features, and the abnormal features.

[0010] According to the remote device access deployment system based on the probe technology provided by the application, the probe module uploads the running state information and the key feature data to the central control platform, including: dividing the running state information into key state information and non-key state information; The key state information and the key feature data are transmitted according to a priority transmission strategy, and the non-key state information is transmitted according to a delay transmission strategy. The priority of the key feature data is higher than the priority of the key state information.

[0011] According to the probe technology-based remote device access deployment system, the probe module is further used for: The running state information is input into a pre-constructed fault prediction model to obtain a fault prediction result. If the fault prediction result is that there is a fault risk, fault early warning information is generated, and the fault early warning information is reported to the central control platform through an encrypted communication link. The fault early warning information includes a device identifier, a fault type, a probability of occurrence, and an influence range.

[0012] According to the probe technology-based remote device access deployment system, the central control platform performs state diagnosis on the to-be-accessed device according to the key feature data to obtain a state diagnosis result, including: The key feature data is integrated according to a time sequence to establish a device full-cycle data set. Each sub-data in the device full-cycle data set is subjected to dimension reduction processing and core feature extraction to obtain core feature data. The core feature data is divided into a plurality of index categories, and a category weight value of each index category is set. According to all the core feature data corresponding to each index category, a core index value of each index category is calculated. According to the category weight value, a weighted operation is performed on the core index values of all the index categories to obtain a state evaluation value. According to the state evaluation value, state diagnosis is performed on the to-be-accessed device to obtain a state diagnosis result.

[0013] According to the probe technology-based remote device access deployment system, the central control platform is further used for: An access state of the to-be-accessed device is obtained. If the access state is an access anomaly, key logs in an interaction process between the to-be-accessed device and the central control platform are obtained. The key logs are parsed to obtain log parsing data. The log parsing data is input into a pre-constructed log diagnosis model to obtain an access anomaly diagnosis result, and the access anomaly diagnosis result is sent to the user terminal.

[0014] According to the application, the user terminal is used for: receiving a data display request input by a user, wherein the data display request contains target data and a target display mode; extracting the target data from the running state information, the state diagnosis result and the instruction execution information, and performing data aggregation and numerical operation processing on the target data to obtain a data processing result; determining rendering objects corresponding to various types of data in the data processing result according to the data display mode and a preset basic mapping relationship; rendering the rendering objects corresponding to various types of data to display the target data in the target display mode.

[0015] In another aspect, the application further provides a remote device access deployment method based on a probe technology, which is based on any of the above-mentioned remote device access deployment systems based on a probe technology, and the method comprises: collecting running state information of a device to be accessed by a probe module, extracting key feature data in the running state information, and uploading the running state information and the key feature data to a central control platform; storing the running state information to a database by the central control platform, performing state diagnosis on the device to be accessed according to the key feature data to obtain a state diagnosis result, generating and issuing a device configuration instruction to the probe module; applying configuration parameters in the device configuration instruction to a target device to be accessed by the probe module, and feeding back instruction execution information to the central control platform; sending the running state information, the state diagnosis result and the instruction execution information to a user terminal by the central control platform.

[0016] The application provides a remote device access deployment system and method based on a probe technology, which collects running state information of a device to be accessed through a probe module, extracts key feature data in the running state information, and uploads the running state information and the key feature data to a central control platform; the central control platform stores the running state information in a database, performs state diagnosis on the device to be accessed according to the key feature data, obtains a state diagnosis result, generates and issues a device configuration instruction to the probe module; the probe module also applies configuration parameters in the device configuration instruction to a target device to be accessed, and feeds back instruction execution information to the central control platform; the central control platform also sends the running state information, the state diagnosis result and the instruction execution information to a user terminal. Due to the remote device access deployment link, the state of the device to be accessed can be effectively diagnosed, the device to be accessed can be configured with parameters according to the device configuration instruction, the instruction execution information can be timely reported, and the user terminal can be timely fed back, so that the device access deployment link function is more perfect, and the accuracy and reliability of the remote device access deployment link are improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

[0018] Figure 1 is a structural schematic diagram of the remote device access deployment system based on the probe technology provided by the embodiment of the application; Figure 2 is a flow schematic diagram of the remote device access deployment method based on the probe technology provided by the embodiment of the application. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the application clearer, the following will combine the drawings in the application to clearly and completely describe the technical solutions in the application. Obviously, the described embodiments are some embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative effort belong to the protection scope of the application.

[0020] The following will describe the details of the remote device access deployment system and method based on the probe technology provided by the embodiment of the application in combination with Figure 1 and Figure 2

[0021] As Figure 1 ​As shown, the probe technology-based remote device access deployment system provided by the embodiment of the application specifically comprises a probe module 110, a central control platform 120, a database 130 and a user terminal 140.

[0022] The probe module 110 is deployed on a device to be accessed, and the probe module 110, the database 130 and the user terminal 140 are connected to the central control platform 120.

[0023] The probe module 110 is configured to collect running state information of the device to be accessed, extract key feature data in the running state information, and upload the running state information and the key feature data to the central control platform 120.

[0024] The central control platform 120 is configured to store the running state information in the database 130, perform state diagnosis on the device to be accessed according to the key feature data, obtain a state diagnosis result, generate and issue a device configuration instruction to the probe module 110.

[0025] The probe module 110 is further configured to apply configuration parameters in the device configuration instruction to a target device to be accessed, and feed back instruction execution information to the central control platform 120.

[0026] The central control platform 120 is further configured to send the running state information, the state diagnosis result and the instruction execution information to the user terminal 140.

[0027] In this embodiment, the probe module 110 is deployed on the device to be accessed, and serves as a key hub for interaction between the device to be accessed and the central control platform 120. The running state information specifically comprises CPU utilization, memory occupancy and network traffic of the device to be accessed.

[0028] In an embodiment, the probe module collects the running state information of the device to be accessed, specifically comprising: First, the load state of the device to be accessed in a current sampling period is obtained.

[0029] Then, according to the load state, a first target sampling frequency of the device to be accessed is determined.

[0030] Finally, the running state information of the device to be accessed in the current sampling period is collected at the first target sampling frequency.

[0031] The embodiment can flexibly change the sampling frequency of the running state information of the to-be-accessed device according to the load condition, that is, the load state, of the to-be-accessed device. For example, when it is detected that the load state of the to-be-accessed device is too high, in order to prevent further consumption of the performance of the device due to frequent sampling, the probe module will actively reduce the sampling frequency; and when the load state of the to-be-accessed device is too low, the sampling frequency can be appropriately increased, so as to more comprehensively and timely obtain the running state information of the device. The purpose of this is to ensure the acquisition of the running state information while reducing the influence of the sampling operation on the performance of the device as much as possible, and to ensure that the device can stably and efficiently run.

[0032] For example, in an actual scene, if the to-be-accessed device is processing a large number of complex tasks, the CPU utilization is high, at this time, the probe module will reduce the sampling frequency of the running state information such as the CPU and the memory of the device, to avoid the additional burden of the to-be-accessed device due to too much sampling work.

[0033] In some embodiments, the first target sampling frequency corresponding to the normal, too high and too low load states of the to-be-accessed device can be set in advance. For example, when the load state is normal, the first target sampling frequency can be set to 50 Hz; when the load state is too high, the first target sampling frequency can be set to 20 Hz; and when the load state is too low, the first target sampling frequency can be set to 100 Hz. In actual application, the specific value of the first target sampling frequency can be reasonably set according to actual needs.

[0034] In an embodiment, after collecting the running state information of the to-be-accessed device at the current sampling period according to the first target sampling frequency, the probe module can be further used for: First, the value fluctuation amplitudes of various data points in the running state information are determined.

[0035] In the embodiment, a range calculation period can be set, the difference between the maximum value and the minimum value of the data points in the period is calculated to obtain the numerical range, and the numerical range is taken as the value fluctuation amplitude. Alternatively, for a range calculation period, the numerical mean of all data points in the period can be calculated, and then the absolute deviation value of each data point from the numerical mean is calculated, and finally the mean of all absolute deviation values is taken as the value fluctuation amplitude.

[0036] Then, the target type data points whose value fluctuation amplitudes are higher than a preset amplitude threshold are determined, and the first target sampling frequency of the target type data points in the next sampling period is increased to the second target sampling frequency.

[0037] It can be understood that for those target class data points with large variation range and violent fluctuation, the sampling density can be increased, that is, more data samples are collected in the same time period or range, so that the variation characteristics of the data can be more comprehensively and meticulously captured, and the detection accuracy of abnormal conditions can be improved, and the conditions that do not conform to the regular mode or deviate from the normal range in the data can be more accurately found. In practical applications, the second target sampling frequency can be reasonably valued according to the abnormal conditions of the data points that appear to be abnormal, for example, when the numerical fluctuation range is relatively obvious but has not reached a violent degree, the second target sampling frequency can be set to 200Hz, and for the abnormal data points with relatively violent numerical fluctuation range, the second target sampling frequency can be set to 500Hz, and the specific value can be reasonably set according to actual needs.

[0038] In an embodiment, the probe module extracts key feature data in the running state information, specifically including: First, the running state information is respectively filtered by threshold, time window and correlation, to obtain filtered information.

[0039] Before the feature extraction operation, the embodiment adopts a data filtering mechanism to preprocess the running state information. The data filtering mechanism is mainly used for screening processing of the running state information to improve data quality and effectiveness, and facilitate subsequent analysis.

[0040] Among them, the threshold filtering link mainly sets a normal data fluctuation range. Once some data values in the running state information exceed the normal data fluctuation range, they will be regarded as abnormal points and excluded. For example, when monitoring the CPU utilization, the CPU utilization normally fluctuates between 20%-80%. If a sharp peak of 100% appears momentarily, it is an abnormal point that exceeds the normal data fluctuation range, and this abnormal point will be excluded. The threshold filtering operation can avoid the interference of abnormal data on the judgment of the normal running state of the equipment.

[0041] The time window filtering link is mainly based on a sliding time window, such as a 5-minute time window. The mean or variance of the data is calculated within this time window to filter out short-term fluctuations. This way can smooth the data and eliminate the influence of accidental fluctuations in the short term, so that the data can better reflect the long-term trend.

[0042] For example, for the network traffic in the running state information, a 5-minute time window is used. If the traffic suddenly rises sharply in a minute, but the overall traffic is found to be at a normal level after calculating the mean value in 5 minutes, the traffic surge in this minute will be filtered out as a short-term fluctuation.

[0043] The correlation filtering link is mainly used to remove redundant data highly correlated with the key indicators. For example, CPU utilization and memory occupancy are highly correlated in some cases. If memory occupancy can well reflect the device running state, then part of the CPU data highly correlated with memory occupancy can be redundant and can be filtered out, which can reduce the data volume and does not affect the grasp of key information.

[0044] Then, statistical features, trend features and abnormal features in the filtered information are extracted respectively, and key feature data is obtained according to the statistical features, the trend features and the abnormal features.

[0045] In this embodiment, on the one hand, representative statistical features are extracted from the filtered information by calculating statistical quantities such as mean, variance, maximum, minimum and percentile of the data, which can reflect the concentration trend and dispersion degree of the data.

[0046] On the other hand, trend features representing the trend of data change can be extracted from the data sequence of the filtered information by using moving average, difference method and other means. Among them, moving average can smooth the data and highlight the trend; difference method can remove the trend component in the data, which is convenient for analyzing other features.

[0047] On the other hand, by identifying outliers (data points that are significantly different from other data points), mutation points (points where data suddenly changes greatly) and periodic fluctuations (regularly repeated fluctuations), abnormal features can be obtained, which are helpful to find abnormal situations or special patterns in the data.

[0048] Considering that the statistical features, trend features and abnormal features obtained by the above three schemes may have repeated feature information, the embodiment can first determine the intersection of the above three features, and the feature information in the intersection is important feature information. The feature information in the intersection can be marked with importance, so as to highlight the analysis of such features in subsequent feature analysis.

[0049] In an embodiment, the probe module uploads the running state information and the key feature data to the central control platform, specifically including: First, the running state information is divided into key state information and non-key state information.

[0050] Then, the key state information and the key feature data are transmitted according to the priority transmission strategy, and the non-key state information is transmitted according to the delayed transmission strategy.

[0051] Among them, the priority of the key feature data is higher than the priority of the key state information.

[0052] It can be understood that the key state information mainly refers to information important for the state analysis of the to-be-accessed device, and priority processing is given to such information so that it is in a front position in the transmission queue and can be quickly transmitted to the central control platform for timely analysis and measures.

[0053] The non-key state information mainly refers to secondary information less affecting the state analysis of the to-be-accessed device, and a delayed transmission mode can be adopted, that is, the transmission is not urgent at the moment, but is performed after the system resources are relatively idle or the key state information and key feature data are all transmitted, so as to balance the system resources and data transmission demand.

[0054] In this embodiment, the priority of the key feature data is set to be higher, and the key feature data can be transmitted before the key state information.

[0055] In an embodiment, the probe module can also be used for: First, the running state information is input into a pre-constructed fault prediction model to obtain a fault prediction result.

[0056] Then, if the fault prediction result is that there is a fault risk, fault warning information is generated and reported to the central control platform through an encrypted communication link.

[0057] The fault warning information includes: device identifier, fault type, occurrence probability, and influence range.

[0058] In this embodiment, the probe module can use edge computing capability to perform real-time analysis on the collected running state information locally. In actual application, a lightweight time series data processing engine can be deployed to perform millisecond-level analysis on the running state information, and a sliding window algorithm and a trend prediction model are combined to realize dynamic monitoring of the device state.

[0059] Specifically, a multi-dimensional fault prediction model can be constructed by using algorithms such as LSTM (Long Short Term Memory) and random forest, which can accurately identify potential faults and abnormal conditions such as device vibration abnormalities, temperature sudden changes, and energy consumption fluctuations. Once a preset threshold or an abnormal pattern matching is triggered, the probe module can immediately generate detailed fault warning information according to the fault prediction result, including the device identifier, fault type, occurrence probability, and influence range of the to-be-accessed device that appears abnormal, and report the fault warning information to the central control platform through a high-reliability message queue mechanism with the shortest delay. At the same time, the fault warning information can be synchronized to the mobile terminal device of the operation and maintenance personnel to ensure that the abnormal condition is responded and handled in time.

[0060] In this embodiment, the central control platform is the core brain of the entire system, responsible for overall coordination of the work of each module. It receives data uploaded by the probe module, and in addition to storing it in the database, it uses artificial intelligence algorithms to conduct in-depth mining and analysis of the data.

[0061] In one embodiment, the central control platform performs state diagnosis on the device to be accessed according to the key feature data, and obtains a state diagnosis result, specifically including: First, integrate the key feature data according to the time sequence to establish a device full-cycle data set.

[0062] In this embodiment, the key feature data can be integrated according to the time sequence, combined with device logs, maintenance records and other multi-source data to construct a device full-cycle data set. At the same time, data cleaning techniques can be used to remove duplicate values, outliers, and fill in missing data, such as processing short-term data missing by linear interpolation method, and estimating long-term missing values using similar device data.

[0063] Second, perform dimensionality reduction processing and core feature extraction on each sub-data in the device full-cycle data set to obtain core feature data.

[0064] In practical applications, principal component analysis, singular value decomposition and other algorithms can be used to reduce the dimensionality of the data, extract core feature data, and reduce computational complexity. For example, key principal components such as network stability and transmission efficiency are extracted from dozens of network indicators, highlighting factors that have a significant impact on device performance.

[0065] Third, divide the core feature data into multiple index categories and set the category weight value of each index category.

[0066] In this embodiment, index category division can be performed from the different stage requirements of the device full cycle. In the device running phase, there are multiple index categories such as hardware performance (CPU utilization, memory occupancy, etc.), network status (network traffic, delay, packet loss rate), etc. In the device maintenance phase, there are multiple index categories such as fault frequency, average repair time, preventive maintenance cycle, etc. In the device retirement phase, there are index categories such as remaining service life and device depreciation rate.

[0067] In practical applications, the analytic hierarchy process or entropy weight method can be used to determine the weight of each index category in combination with expert experience and historical data. For example, for high-computing-load devices, the CPU utilization weight can be set to 20%; for network-dependent devices, the network traffic weight can be set to 18%. At the same time, the weights corresponding to each index category can be dynamically adjusted according to the device type and use scenario, such as industrial production devices focusing more on stability indicators and data center devices emphasizing resource utilization rate indicators.

[0068] Fourthly, the core index value of each index category is calculated according to all the core feature data under each index category.

[0069] In this embodiment, all the core feature data under each index category can be normalized, and then the normalized feature values are averaged to obtain the core index value of each index category.

[0070] Fifthly, the core index values of all index categories are weighted according to the category weight values to obtain the state evaluation value.

[0071] In this embodiment, the core index values of all index categories can be calculated by weighted summation to obtain the state evaluation value.

[0072] Sixthly, the state diagnosis result of the to-be-connected device is obtained according to the state evaluation value.

[0073] In actual application, the state evaluation value can be any value within 0-100, and the current running state of the to-be-connected device can be intuitively presented according to the state evaluation value. For example, when the state evaluation value is above 90, it can be considered as an ideal working condition, when the state evaluation value is 70-89, it can be considered as a warning state, and when the state evaluation value is below 70, the maintenance process needs to be started immediately, thereby providing a data-driven decision basis for the whole cycle management of the to-be-connected device.

[0074] In an embodiment, the central control platform can also be used for: Firstly, the access state of the to-be-connected device is obtained.

[0075] Secondly, if the access state is abnormal, the key log in the interaction process between the to-be-connected device and the central control platform is obtained.

[0076] Thirdly, the key log is parsed to obtain log analysis data.

[0077] Fourthly, the log analysis data is input into a pre-constructed log diagnosis model to obtain an access abnormality diagnosis result, and the access abnormality diagnosis result is sent to the user terminal.

[0078] In this embodiment, when the access state fails or an abnormal condition is detected in the access process of the to-be-connected device, the central control platform will automatically trigger a multi-level log recording mechanism, which not only saves the original data packet of the device connection request and the network interaction timing, but also real-time captures the system kernel error code, the device driver state and other deep information to obtain the key log.

[0079] After the key logs are parsed by the log parsing tool, log parsing data can be obtained. Meanwhile, the intelligent diagnosis engine can perform multi-dimensional analysis on the log parsing data based on a pre-constructed log diagnosis model, accurately locate potential causes such as network protocol incompatibility, port conflict, device firmware version mismatch, and the like, and obtain abnormal diagnosis results.

[0080] In actual application, for scenarios that can be automatically processed, the central control platform will perform intelligent repair processes including port remapping, protocol stack resetting, and automatic upgrading of driver versions; if the fault needs human intervention, the central control platform will generate a visual repair work order containing detailed error code explanations, fault-associated device topology diagrams, and standardized operation steps, providing clear and explicit troubleshooting guidance for operation and maintenance personnel.

[0081] In this embodiment, the user terminal is a window for operation and maintenance personnel to interact with the entire system. The user terminal can provide rich visual interfaces that can display device status, access progress, data analysis charts, and other information, supporting remote monitoring and management by operation and maintenance personnel. In addition to displaying basic data, the user terminal in this embodiment also introduces humanized data display, intelligent search, and recommendation functions. Operation and maintenance personnel can input query requirements through natural language, and the system automatically understands and retrieves related device information and operation records.

[0082] In an embodiment, the user terminal can be used for: First, a data display request input by a user is received, wherein the data display request contains target data and a target display method.

[0083] In this embodiment, the user can select one or more from a plurality of preset display method options as the target display method, and input or select the target data to be displayed.

[0084] Second, the target data is extracted from the running state information, the state diagnosis result, and the instruction execution information, and data aggregation and numerical operation processing are performed on the target data to obtain a data processing result.

[0085] In this embodiment, the target data is derived from any one or more of the running state information, the state diagnosis result, and the instruction execution information. Since the extracted target data is mostly discrete numerical values, data aggregation and numerical operation can be achieved through different statistical operation methods. For example, for instruction execution information, the number of successfully executed tasks and the number of failed tasks can be determined through statistical operation.

[0086] Third, according to the data display method and the preset basic mapping relationship, the rendering objects corresponding to each type of data in the data processing result are determined.

[0087] In actual application, the display mode corresponding to different types of data can be preset, that is, a preset basic mapping relationship is set, for example, a ring-shaped dashboard is used to display core indexes such as CPU utilization and memory occupancy; a line chart is drawn according to the change of device performance indexes over time; a Gantt chart is drawn for each configuration task through a task progress Gantt chart, so as to intuitively display the start time, estimated completion time, actual progress and key nodes of the task.

[0088] It can be understood that the rendering object refers to a blank coordinate graph or a Gantt chart waiting for a filling framework.

[0089] In the fourth step, the rendering object corresponding to each type of data is rendered to display the target data in the target display mode.

[0090] In the fourth step, the rendering object corresponding to each type of data is rendered to display the target data in the target display mode.

[0091] In some embodiments, the user terminal can intelligently recommend common operations and configuration templates according to the operation habits and historical behaviors of the operation and maintenance personnel, thereby improving work efficiency. In addition, the user terminal supports multi-device type adaptation, and the operation and maintenance personnel can access the system through various devices such as computers, tablets and mobile phones at any time and anywhere, thereby realizing remote operation and maintenance management.

[0092] The probe module in the remote device access deployment system based on the probe technology provided by the embodiment can utilize edge computing and machine learning to realize data preprocessing and abnormality prediction, the central control platform can optimize task management through intelligent algorithms and dynamic scheduling, and the user terminal can realize intuitive visualization function, so that the system function is more perfect, thereby effectively improving the accuracy and reliability of the remote device access deployment link.

[0093] Based on the same overall inventive concept, the present application also protects a remote device access deployment method based on the probe technology. The remote device access deployment method based on the probe technology provided by the present application will be described below. The remote device access deployment method based on the probe technology described below can be mutually corresponding and referred to with the remote device access deployment system based on the probe technology described above.

[0094] As shown in Figure 2 The remote device access deployment method based on the probe technology provided by the embodiment of the present application is implemented based on the remote device access deployment system based on the probe technology provided by each of the above embodiments. The method mainly includes the following steps: Step 210: Collecting the running state information of the device to be accessed by the probe module, extracting the key feature data in the running state information, and uploading the running state information and the key feature data to the central control platform.

[0095] Step 220: Storing the running state information to the database by the central control platform, diagnosing the state of the device to be accessed according to the key feature data, obtaining the state diagnosis result, generating and issuing the device configuration instruction to the probe module.

[0096] Step 230: Applying the configuration parameters in the device configuration instruction to the target device to be accessed by the probe module, and feeding back the instruction execution information to the central control platform.

[0097] Step 240: Sending the running state information, the state diagnosis result and the instruction execution information to the user terminal by the central control platform.

[0098] As to the method in the above-mentioned embodiments, the specific principles of the operation of each step have been described in detail in the embodiments of the system, which will not be described in detail here.

[0099] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A remote device access deployment system based on probe technology, characterized in that, include: Probe module, central control platform, database, and user terminal; The probe module is deployed on the device to be connected, and the probe module, the database, and the user terminal are all connected to the central control platform. The probe module is used to collect the operating status information of the device to be connected, extract key feature data from the operating status information, and upload the operating status information and the key feature data to the central control platform. The central control platform is used to store the operating status information in the database, perform status diagnosis on the device to be connected based on the key feature data, obtain the status diagnosis result, and generate and send device configuration instructions to the probe module. The probe module is also used to apply the configuration parameters in the device configuration instruction to the target device to be connected, and to feed back the instruction execution information to the central control platform. The central control platform is also used to send the operating status information, status diagnosis results, and instruction execution information to the user terminal.

2. The remote device access deployment system based on probe technology according to claim 1, characterized in that, The probe module collects the operating status information of the device to be connected, including: Obtain the load status of the device to be connected during the current sampling period; Based on the load status, determine the first target sampling frequency for the device to be connected; According to the first target sampling frequency, the operating status information of the device to be connected is collected in the current sampling period.

3. The remote device access deployment system based on probe technology according to claim 2, characterized in that, After collecting the operating status information of the device to be accessed at the first target sampling frequency and within the current sampling period, the probe module is further configured to: Determine the numerical fluctuation range of various data points in the operational status information; Once a target data point whose numerical fluctuation amplitude exceeds a preset amplitude threshold is identified, the first target sampling frequency of the target data point in the next sampling period is increased to the second target sampling frequency.

4. The remote device access deployment system based on probe technology according to claim 1, characterized in that, The probe module extracts key feature data from the operating status information, including: The running status information is subjected to threshold filtering, time window filtering, and correlation filtering to obtain filtered information. Statistical features, trend features, and abnormal features are extracted from the filtered information, and key feature data are obtained based on these features.

5. The remote device access deployment system based on probe technology according to claim 1, characterized in that, The probe module uploads the operating status information and the key feature data to the central control platform, including: The operational status information is divided into critical status information and non-critical status information; The critical status information and the critical feature data are transmitted according to a priority transmission strategy, while the non-critical status information is transmitted according to a delayed transmission strategy. Among them, the priority of the key feature data is higher than the priority of the key status information.

6. The remote device access deployment system based on probe technology according to any one of claims 1 to 5, characterized in that, The probe module is also used for: The operating status information is input into a pre-built fault prediction model to obtain fault prediction results; If the fault prediction result indicates that there is a fault risk, a fault warning message is generated and reported to the central control platform through an encrypted communication link. The fault warning information includes: equipment identification, fault type, probability of occurrence, and scope of impact.

7. The remote device access deployment system based on probe technology according to claim 1, characterized in that, The central control platform performs status diagnosis on the device to be connected based on the key feature data, and obtains status diagnosis results, including: The key feature data are integrated according to time series to establish a full lifecycle dataset for the device; Dimensionality reduction and core feature extraction are performed on each sub-data in the full lifecycle dataset of the device to obtain core feature data; The core feature data is divided into multiple indicator categories, and a category weight value is set for each indicator category; Based on all the core feature data corresponding to each indicator category, the core indicator value of each indicator category is calculated. According to the category weight values, the core indicator values ​​of all indicator categories are weighted to obtain the state evaluation value; The status of the device to be connected is diagnosed based on the status assessment value, and the status diagnosis result is obtained.

8. The remote device access deployment system based on probe technology according to claim 1, characterized in that, The central control platform is also used for: Obtain the access status of the device to be accessed; If the access status is an access anomaly, obtain the key logs of the interaction process between the device to be accessed and the central control platform. The key logs are parsed to obtain log parsing data; The log parsing data is input into a pre-built log diagnostic model to obtain access anomaly diagnostic results, and the access anomaly diagnostic results are sent to the user terminal.

9. The remote device access deployment system based on probe technology according to claim 1, characterized in that, The user terminal is used for: Receive a data display request input by the user, wherein the data display request includes target data and target display method; The target data is extracted from the running status information, status diagnosis results, and instruction execution information, and the target data is aggregated and numerically processed to obtain the data processing result. Based on the data display method and the preset basic mapping relationship, determine the rendering objects corresponding to various types of data in the data processing results; Render the rendering objects corresponding to various types of data to display the target data in accordance with the target display method.

10. A remote device access deployment method based on probe technology, the method being based on the remote device access deployment system based on probe technology as described in any one of claims 1 to 9, characterized in that, The method includes: The probe module collects the operating status information of the device to be connected, extracts key feature data from the operating status information, and uploads the operating status information and the key feature data to the central control platform. The central control platform stores the operating status information in the database, performs status diagnosis on the device to be connected based on the key feature data, obtains the status diagnosis result, and generates and sends device configuration instructions to the probe module. The probe module applies the configuration parameters in the device configuration command to the target device to be connected and feeds back the command execution information to the central control platform. The central control platform sends the operating status information, status diagnosis results, and instruction execution information to the user terminal.

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