Data processing method and device and electronic equipment

Abstract

The invention discloses a data processing method and device and electronic equipment. The method comprises the steps that real-time data of all professional devices in a network range and static data in the network range are obtained through a target interface, the target interface is a standard interface used for dynamic environment monitoring in the network range, and the static data are used for representing attributes and resource allocation information of all the devices in the network range; determining an association wide table corresponding to each device in the network range according to the real-time data and the static data; and detecting the network range according to the association wide table. According to the invention, the technical problem of poor accuracy during fault analysis due to relatively independent data of each specialty in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and more specifically, to a data processing method, device, and electronic device. Background Art

[0002] Currently, operators' professional hidden dangers and fault handling processes are relatively independent. Due to historical evolution, each profession has its own set of network management, computer room, and equipment correspondence methods. The global end-to-end correlation rate of the entire network is low, and it is impossible to achieve end-to-end power supply and cooling connection for all professional services. Therefore, there is a problem of poor accuracy when conducting fault analysis.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a data processing method, device, and electronic device to at least solve the technical problem in related technologies that the data of various disciplines are relatively independent, resulting in poor accuracy in fault analysis.

[0005] According to one aspect of an embodiment of the present application, a data processing method is provided, including: obtaining real-time data of each professional device within a network range through a target interface, and obtaining static data in the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device in the network range; based on the real-time data and the static data, determining the associated wide table corresponding to each device in the network range; and detecting the network range based on the associated wide table.

[0006] Optionally, based on real-time data and static data, an associated wide table corresponding to each device in the network range is determined, including: obtaining the device code in the static data, and obtaining the first association relationship between the real-time data; based on the device code, mapping the real-time data of the device corresponding to the device code to the static data to obtain the first mapping data; based on the first association relationship, determining the hierarchical relationship between each device in the first mapping data, wherein the hierarchical relationship includes the power supply relationship between the front end of the device to the end of the device; based on the hierarchical relationship and the first mapping data, determining the associated wide table.

[0007] Optionally, after determining the association wide table corresponding to each device in the network range based on real-time data and static data, the method also includes: obtaining the hierarchical relationship between each device in the association wide table and the information of each device; determining each device information as a node in the association topology diagram, and determining the connection relationship between the nodes based on the hierarchical relationship, wherein when there is a hierarchical relationship between adjacent nodes, the corresponding nodes are connected with lines; converting the association wide table into an association topology diagram based on the node and connection relationship.

[0008] Optionally, the network range is detected based on the associated wide table, including: obtaining the associated topology map and real-time alarm data corresponding to professional equipment within the network range; using a causal discovery algorithm to perform root cause analysis on the associated topology map and real-time alarm data to determine the actual fault cause of the real-time alarm data.

[0009] Optionally, the network range is detected based on the associated wide table, including: determining the device location information of the target device from the associated wide table, and determining the target acquisition device based on the device location information, wherein the target acquisition device is used to collect data of the target area; obtaining the video stream data collected by the target acquisition device, and obtaining a list of persons allowed to enter the target area and a corresponding set of real face images from the target area application form, wherein the target device is located within the target area of the network range; identifying the first face image in the video stream data, and comparing the first face image with the face images in the real face image set to obtain a comparison result; when the comparison result indicates that the first face image is different from all face images in the real face image set, determining that there is human intrusion in the target area.

[0010] Optionally, the network range is detected based on the associated wide table, including: obtaining intrusion rules of the target area, and obtaining human activity data collected by the target collection device; based on the intrusion rules, using the target detection algorithm to detect the human activity data to obtain the detection results; when the detection results indicate that the human activity data is abnormal, generating abnormal information.

[0011] Optionally, the network range is detected based on the associated wide table, including: determining the temperature data and capacity data of professional equipment from the associated wide table, wherein the temperature data includes the periodicity, temperature trend and noise data of the temperature data; using the detection model to predict the temperature data and capacity data to obtain a prediction result, wherein the temperature prediction result in the prediction result is used to determine the air conditioning adjustment strategy.

[0012] According to another aspect of an embodiment of the present application, a data processing device is also provided, including: an acquisition module, used to obtain real-time data of each professional equipment within the network range through a target interface, and to obtain static data in the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device in the network range; a determination module, used to determine the associated wide table corresponding to each device in the network range based on the real-time data and the static data; a detection module, used to detect the network range based on the associated wide table.

[0013] According to another aspect of the embodiment of the present application, an electronic device is also provided, including: a memory for storing program instructions; a processor, connected to the memory, for executing program instructions to implement the following functions: obtaining real-time data of each professional device within the network range through a target interface, and obtaining static data in the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device in the network range; determining the associated wide table corresponding to each device in the network range based on the real-time data and the static data; and detecting the network range based on the associated wide table.

[0014] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned data processing method by running the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including computer instructions, which implement the above-mentioned data processing method when executed by a processor.

[0016] In an embodiment of the present application, real-time data of each professional device within the network range is obtained through a target interface, and static data within the network range is obtained, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device within the network range; based on the real-time data and the static data, the associated wide table corresponding to each device in the network range is determined; the network range is detected based on the associated wide table, achieving the purpose of real-time monitoring and analysis, thereby realizing the technical effect of automated fault warning, and further solving the technical problem that the data of each profession in the related technology is relatively independent, resulting in poor accuracy in fault analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a hardware structure block diagram of a computer terminal for implementing a data processing method according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of a data processing method according to an embodiment of the present application;

[0020] Figure 3 It is a structural diagram of a data processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The information collected in the embodiments of the present application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or reject the automated decision results; if the user chooses to reject, the expert decision-making process will be entered.

[0024] First, some nouns or terms that appear in the process of explaining the embodiments of this application are subject to the following explanations:

[0025] B interface: A newly developed interface for monitoring the dynamic environment of a computer room. It defines in detail the equipment interconnection specifications, protocols, and message formats, ensuring compatibility and standardization among various devices and systems.

[0026] Multi-disciplinary linkage: A method that leverages the OSS (Operations Support System) resource system to accurately and quickly query, correlate, and analyze various disciplines’ alarms and performance through automated means during daily operations and troubleshooting.

[0027] Unified acquisition and control: A platform that realizes the acquisition and control capabilities of all professional cloud network operation systems, and is an important platform software that undertakes the service-oriented acquisition layer of all professional cloud network capabilities.

[0028] New generation system: The abbreviation of the new generation monitoring system architecture, which is characterized by modularizing and self-configuring each function to solve the long-term chimney-like existence of various professional systems.

[0029] Alarm sub-center: As a platform for aggregating all professional alarms, it has the ability to collect all professional alarms in one place and configure alarm merging rules and dispatch rules.

[0030] Smart workbench: As one of the new generation systems, it has an independent configuration presentation module and can achieve graphical presentation of various screens and reports by connecting with other databases and systems.

[0031] With the rise of AI models, network service providers are placing increasing demands on bandwidth in communication rooms. The massive influx of real-time access is also driving increasing demands for communication stability. This is especially true in Internet Data Center (IDC) buildings, where a large number of cloud hosts and cloud storage are used by customers. Consequently, ensuring power supply security for these critical sites while maintaining in-house maintenance personnel has become a key concern for communications operators.

[0032] The communication operation and maintenance of traditional communication buildings mainly rely on the huge real-time monitoring systems of various disciplines. Each discipline has monitoring systems for various types of equipment to monitor the maintained equipment 24 hours a day. If an alarm is found, the alarm type will be pushed to the alarm sub-center as soon as possible, and the alarm will be analyzed according to the pre-configured alarm handling rules. According to the analysis results, the alarms will be graded and merged. Finally, according to the various types of merged alarms and the pre-set dispatching rules, the alarms will be dispatched to the site for real-time processing, realizing real-time control of faults in various disciplines.

[0033] As a traditional dynamic environment specialty, power supply safety at the bureau station mainly involves: remote real-time discovery and on-site handling of faults such as power outages, high temperatures, and water immersion, and is incorporated into the whole network fault handling along with all other specialties. Traditional cross-specialty linkage mainly relies on manual methods. After the alarm is assigned to the corresponding specialty first, multiple professional work orders are merged and processed based on the verification and feedback results of on-site maintenance personnel. This leads to the current situation of independent operation and management of each specialty. Coupled with the limitations of the network management system architecture and interfaces of each specialty, the traditional communication bureau station operation and maintenance method has the problem of multiple specialties working together and acting independently. Specifically, there are the following three problems:

[0034] 1. Each major is relatively independent, and the overall correlation rate of the entire network is not high.

[0035] Currently, operators have relatively independent processes for handling hidden dangers and faults in various disciplines. Due to historical evolution, each discipline has its own set of network management, computer rooms, and equipment. This results in a low global end-to-end correlation across the entire network, making it impossible to achieve end-to-end power and cooling linkage for all professional services. This is especially true in IDC buildings, where this situation often leads to long periods of unattended management in these areas, creating "blind spots" in computer room operations and maintenance. This blind spot is not easily apparent when the computer room has sufficient power supply and functional capacity. However, as business volume gradually increases, cross-disciplinary hidden dangers such as rack capacity exceeding limits and master-slave protection switching failures are more likely to occur. Because routine inspections and drills are performed within each discipline, these hidden dangers are difficult to expose. Centralized monitoring and maintenance personnel are also unable to detect these hidden dangers based on system warnings, which can easily lead to failures.

[0036] 2. Traditional analysis models are single and have low accuracy in various scenarios.

[0037] Traditional cross-disciplinary risk and fault analysis models are overly simplistic, often using "keywords" (such as equipment, station, computer room, region, and branch) as input parameters. These models directly invoke interfaces provided by specialized network management systems to verify cross-disciplinary issues in real time. These simplistic and single-minded logical analysis models are increasingly inadequate for current computer room maintenance, especially in IDC rooms with large numbers of devices, and their coordinated accuracy is low. The interfaces provided by specialized network management systems are rigid, typically designed to address specific issues. These interfaces lack scalability and are unable to adapt the analysis model to the current state of the computer room and autonomously identify potential risks throughout the power supply chain. The analysis models also lack intelligence, lacking the ability to access large amounts of data and self-learn. Consequently, neither monitoring and maintenance personnel at the head office nor on-site maintenance personnel at subsidiaries have effective automated methods for real-time, end-to-end risk analysis. The long-term existence of these risks can easily lead to disruptions in core business operations.

[0038] 3. Multiple professional network managers independently develop and manage the network, which is difficult and expensive.

[0039] Currently, due to the historical evolution of specialized network management, developing a single end-to-end, fully specialized function requires the involvement of two groups of people (professional backbones and network management vendors). This discussion is time-consuming and labor-intensive, making management difficult. Furthermore, the relatively closed nature of network management vendors leads to arduous R&D costs, resulting in high development costs for each "stovetop" development model. Field research revealed an urgent need for self-configurable, low-code, or even zero-code, tools to present their design concepts. This allows them to regain control of device data acquisition and analysis, enabling point-of-need configuration and rapid local optimization and modification, effectively enhancing efficiency amidst the current trend of cost reduction and efficiency improvement.

[0040] Due to the above reasons, the current method of multi-professional linkage warning in traditional computer rooms has problems such as low global correlation rate of the entire network, low monitoring coverage, low accuracy in various scenarios, and difficult and expensive management of various professional network managers.

[0041] In order to solve the problems existing in the related art, the embodiment of the present application provides a data processing method, which can be run on Figure 1 Among the computer terminals shown, the computer terminal will be described below.

[0042] The data processing method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG1 shows a hardware structure block diagram of a computer terminal for implementing a data processing method. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated by 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0043] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0044] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the data processing method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned data processing method. The memory 104 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 104 may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 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.

[0045] The transmission module 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission module 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission module 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0046] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0047] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0048] In the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a data processing method. 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.

[0049] Figure 2 is a flow chart of a data processing method according to an embodiment of the present application, such as Figure 2 As shown, the method includes the following steps:

[0050] Step S202, obtain the real-time data of each professional device within the network range through the target interface, and obtain the static data in the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device in the network range.

[0051] In the above step S202, the target interface may be the B interface, and the real-time data refers to the operating status information of the device at the current or most recent moment, including but not limited to voltage, current, temperature, humidity, device operating status, alarm information, etc., for example, it may be the dynamic ring data of the computer room. Through the B interface, the monitoring system can receive this data regularly or in real time from various professional equipment (such as power supply equipment, air conditioning equipment, security equipment, etc.), so as to monitor the health status of the equipment and environmental changes in real time. Static data refers to the fixed attributes and resource allocation information of each device within the network range, such as the name, model, location, computer room, station code, equipment access time, current status of the equipment, rack allocation, U position information, equipment type, hierarchical relationship between devices, etc. This information is established during the installation and initial configuration of the equipment, and will not change frequently unless major changes are made.

[0052] Specifically, static data can be reflected through the data in the equipment static table and the station room static table. The equipment static table includes: equipment name, equipment code, equipment type, computer room, computer room code, equipment network access time, current status of the equipment, etc.; the station room static table includes: computer room name, computer room code, station, station code, etc.

[0053] When using the B interface to collect real-time data, configure the B interface power supply equipment scheduled data collection task, mainly for the terminal cabinet, distribution panel, power supply, and high-voltage incoming line. Some important signals that need to be monitored are shown in the following table:

[0054]

[0055]

[0056] Configure the scheduled collection interface for professional resources and performance data to obtain professional equipment rack and performance data in real time. The corresponding interfaces all use the post request method:

[0057]

[0058]

[0059] The specific data acquisition is as follows:

[0060] 1) Resource data interface: data_dev, which obtains equipment data such as station, room, rack, U-position, manufacturer, and model according to the naming rules. For example, the parsing rule can be: separated by -, with fields 1 and 2 representing the service name (JS for Jiangsu, NJ for Nanjing), followed by the room information, cabinet, U-position, etc.

[0061] 2) Alarm data acquisition interface: alarm_dev, uses the device IP for mapping, and the interface adopts the post port docking method to obtain the unique ID, title, status and IP address of the device alarm;

[0062] 3) Performance data docking interface: performance_dev, using the device IP address to obtain the device name, IP address, collection time, type, and real-time value.

[0063] Step S204: Determine the associated wide table corresponding to each device in the network range based on the real-time data and the static data.

[0064] In step S204, the associated wide table is a data structure that combines static device information (i.e., the aforementioned static data) with real-time monitoring data (i.e., the aforementioned real-time data), providing a complete view of each device, including detailed attributes and current status. Attributes in the wide table may include: device name, device code, device type, computer room, computer room code, station, station code, device access time, current device status, previous column header, collection attribute name, and real-time collection value.

[0065] In some embodiments of the present application, an associated wide table can be constructed as follows: First, real-time data and static data obtained from different sources (such as B-interface, OSS resource system, etc.) are integrated. Ensure that each piece of real-time data can be matched with a specific device record in the static data, for example, by associating it through a device code or IP address. Add the attributes of the real-time data to the static data record, such as attaching performance indicators such as temperature and voltage and alarm status to the device information entry, to form a richer and more dynamic data entry. Based on the hierarchical relationship and physical location of the devices in the static data, add the information of the upper and lower level devices to each device entry, such as the cabinet code and the upper power supply device code, to build an end-to-end link from the bottom-level device to the high-level service node. Based on the above steps, a wide table containing all devices and their associated information is created. This table spans multiple data dimensions and covers the device's static attributes, location information, real-time performance and alarm status, as well as the hierarchical relationship between devices. By configuring collection tasks, the static data and real-time data in the associated wide table are updated to ensure the timeliness of the data. In addition, by configuring the external output capability, the input parameter is the device code, and the output parameter is the affiliated computer room, affiliated station, previous column header, collection attribute name, and real-time collection value, and finally the association and management of all network devices are completed.

[0066] Step S206: Detect the network range according to the associated width table.

[0067] In the above step S206, the associated wide table can be used for fault tracing, resource management and optimization, security monitoring and preventive maintenance. Specifically, when a fault occurs, maintenance personnel or the system can quickly trace the potential source of the fault based on the hierarchical relationship in the wide table, such as starting from the alarm of a business device and looking up for possible power supply or environmental problems, thereby speeding up the fault diagnosis and repair process. In terms of resource management and optimization, the wide table provides a comprehensive perspective on equipment capacity, configuration information and working status, which helps the operation and maintenance team to perform resource scheduling and performance optimization, for example, monitoring the current power consumption of the rack to prevent overload. In terms of security monitoring, combined with video surveillance data and static location information of the equipment, the security status of key areas can be monitored in real time, and unauthorized access or other security threats can be automatically identified. In terms of preventive maintenance, based on historical and real-time data in the associated wide table, advanced AI prediction models (such as time series analysis models such as ARIMA and ETS) can be used to predict the future status of the equipment. It helps to identify possible performance degradation or failure trends in advance, implement preventive maintenance, and reduce unexpected downtime and maintenance costs.

[0068] Through the above steps S202 to S206, the purpose of real-time monitoring and analysis is achieved, thereby realizing the technical effect of automated fault warning, and further solving the technical problem in related technologies where the data of each discipline is relatively independent, resulting in poor accuracy in fault analysis.

[0069] In step S204 of the above-mentioned data processing method, the associated wide table corresponding to each device in the network range is determined based on the real-time data and the static data, including: obtaining the device code in the static data, and obtaining the first association relationship between the real-time data; based on the device code, mapping the real-time data of the device corresponding to the device code to the static data to obtain the first mapping data; based on the first association relationship, determining the hierarchical relationship between each device in the first mapping data, wherein the hierarchical relationship includes the power supply relationship between the front end of the device to the end end of the device; based on the hierarchical relationship and the first mapping data, determining the associated wide table.

[0070] In some embodiments of the present application, real-time data is collected from monitoring interfaces such as the B interface, covering the real-time performance indicators (such as voltage, current, temperature, etc.) and operating status of the equipment. Through the equipment coding, the first association relationship is obtained according to the data flow relationship between the real-time data. According to the equipment coding, the real-time data is mapped to the corresponding equipment record in the static data to generate the first mapping data. Based on the first association relationship, the physical and logical hierarchical structure of the equipment is analyzed, especially the power supply relationship. For example, a power supply hierarchy is formed between the queue head cabinet (as an intermediate node for power distribution) and the directly connected power supply equipment and downstream business equipment. A wide table is a data structure that integrates information from multiple data sources into the same data row to form a comprehensive record containing multiple attributes. In an embodiment of the present application, the wide table will contain equipment code, equipment type, location information, real-time performance data, operating status, and related upper and lower level equipment information, etc. Specifically, the determined hierarchical relationship and the first mapping data are filled into the wide table. Each device has an entry in the wide table, which contains not only static information, but also the latest performance indicators and status, as well as the codes of other devices associated with it, thus forming an associated wide table that comprehensively reflects the device status and interdependencies.

[0071] In step S204 of the above-mentioned data processing method, after determining the association wide table corresponding to each device in the network range based on real-time data and static data, the method further includes: obtaining the hierarchical relationship between each device in the association wide table and the information of each device; determining each device information as a node in the association topology diagram, and determining the connection relationship between the nodes based on the hierarchical relationship, wherein when there is a hierarchical relationship between adjacent nodes, the corresponding nodes are connected with lines; and converting the association wide table into an association topology diagram based on the node and connection relationship.

[0072] In some embodiments of the present application, when constructing an association topology diagram, each row of device information in the association wide table corresponds to a node in the diagram. Nodes are the basic elements of a topology diagram and represent specific entities, such as power devices, header cabinets, business servers, etc. The hierarchical relationship in the association wide table guides the connection method between nodes. Specifically, if two devices have a direct superior-subordinate relationship in the wide table (such as a business device directly powered by a header cabinet), then the two nodes will be connected by a line in the association topology diagram to represent the logical or physical relationship between them. The hierarchical structure between devices can be clearly displayed through the direction of the line or the hierarchical label, that is, which device is upstream (such as a power device) and which device is downstream (such as a business device). This method helps to quickly understand the power supply path and resource dependencies of the entire network. Use a visualization tool to draw a topology diagram based on the nodes and connection relationships in the above steps. Each node represents one or a group of devices in the wide table, and the lines represent the hierarchy and power supply relationship between them. The association topology diagram should be able to dynamically adjust according to the real-time update of the association wide table to reflect changes in device status. When a device's alarm status, performance indicators, or hierarchical relationships change, the node attributes and connection relationships in the topology map should also be updated accordingly. The resulting associated topology map is an intuitive, dynamic network view that not only displays the static location and type of devices, but also their real-time status and dynamic relationships, facilitating subsequent troubleshooting, resource scheduling, and other tasks. Furthermore, the topology map can highlight critical devices or abnormal status through color coding, icons, or annotations.

[0073] In step S206 of the above data processing method, the network range is detected based on the associated wide table, including: obtaining the associated topology map and real-time alarm data corresponding to professional equipment within the network range; using a causal discovery algorithm to perform root cause analysis on the associated topology map and real-time alarm data to determine the actual fault cause of the real-time alarm data.

[0074] In some embodiments of the present application, a causal discovery algorithm is a data analysis method used to discover causal relationships between variables in complex data sets. In the context of telecommunications room operations and maintenance, the algorithm analyzes the power supply dependencies between devices and the current alarm status to infer the root cause of the alarm. Based on the correlation topology diagram and combined with real-time alarm data, the causal discovery algorithm analyzes which alarms are directly caused by device failures and which may be caused by anomalies in upstream devices that indirectly affect downstream devices. During this analysis, the algorithm considers the hierarchical relationships between devices and the temporal correlation of alarms to determine the path and source of the fault propagation. For example, when a service alarm occurs, the algorithm automatically searches for alarms in the same station, room, and column for the previous device of the same type, based on the alarm type. This algorithm then extends the analysis to determine whether there are alarms in the previous device of the same type, all the way back to the power supply line. Through the causal discovery algorithm's analysis, the system outputs one or more most likely true causes of the fault. These causes typically point to one or more upstream devices, whose failures lead to cascading alarms in downstream devices.

[0075] For example, a service device on the network reports an alarm indicating a network connection loss. By correlating wide tables, we can identify the line transformer (LTG) to which the service device is connected, the UPS connected to the LTG, and the power distribution panel connected to the UPS. Using a causal discovery algorithm for analysis, if voltage instability is detected on the UPS or a circuit breaker alarm is detected on the power distribution panel, the algorithm identifies the circuit breaker on the power distribution panel as the root cause of the entire chain failure. Maintenance personnel can then prioritize addressing the power distribution panel issue rather than directly repairing the service device or UPS, thereby restoring network service more quickly. In this way, the correlating wide tables combined with the root cause analysis of the causal discovery algorithm enable intelligent monitoring of all specialized equipment within the network, improving the accuracy of fault detection and significantly optimizing the troubleshooting process.

[0076] In actual applications, the fault tracing method can be embedded in the fault production dispatch process. When maintenance personnel receive a fault ticket, they can automatically obtain the cause of the fault from the work order without manually asking other professionals. If the fault is caused by their own profession, it will be repaired immediately. If the fault is caused by other professions, they will wait for the source fault to be handled and check whether the fault is restored.

[0077] In step S206 of the above-mentioned data processing method, the network range is detected according to the associated wide table, including: determining the device location information of the target device from the associated wide table, and determining the target acquisition device based on the device location information, wherein the target acquisition device is used to collect data of the target area; obtaining the video stream data collected by the target acquisition device, and obtaining the list of persons allowed to enter the target area and the corresponding real face image set from the target area application form, wherein the target device is located in the target area of the network range; identifying the first face image in the video stream data, and comparing the first face image with the face images in the real face image set to obtain a comparison result; when the comparison result indicates that the first face image is different from all the face images in the real face image set, it is determined that there is a human intrusion in the target area.

[0078] In some embodiments of the present application, the device location information of the target device, including the computer room, floor, area, etc. where it is located, is retrieved and determined from the associated wide table based on the device code or other identifier. Based on the acquired device location information, the target acquisition device, such as a camera, covering the target area is determined. The target acquisition device is responsible for capturing activities in the target area in real time and providing video stream data for subsequent analysis. Specifically, the target acquisition device (camera) converts the captured video signal into digital video stream data and transmits it to the analysis system in real time. A list of people allowed to enter the area is obtained from the target area application form (such as a form for entering the computer room based on actual needs such as fault repair work orders, daily risk operation orders, insurance re-insurance orders, power on and off orders, etc.), and at the same time, the real facial images of these people are collected to form a set of real facial images. These images are used for subsequent facial comparison to confirm the identity of the people in the video. A face recognition algorithm is used to capture the first face image from the video stream data. The algorithm analyzes the facial features in the video frame and extracts them in preparation for comparison. The first facial image is compared with all images in the collection of real facial images to check for faces with a similarity exceeding a threshold, which is used to determine the probability of being the same person. If the first facial image does not match any image in the collection of real facial images, that is, the comparison result indicates that there are no similar facial images, this indicates that the person in the video is not on the list of people allowed to enter the target area. In this case, the system will determine that there is a human intrusion in the target area, triggering a security warning mechanism, notifying relevant personnel for on-site verification, or initiating a pre-set security response process to prevent security risks that may be posed by unauthorized visitors.

[0079] By extracting the target device's location information from the associated wide table, locating the target acquisition device (i.e., camera), and combining video stream data with the real facial images of authorized personnel, facial recognition technology is used for real-time comparison. This effectively monitors the presence of unauthorized personnel in the target area, facilitating the security of critical facilities such as communications rooms and data centers. This also enables automated and intelligent security monitoring, significantly improving work efficiency and responsiveness while reducing labor costs and error rates.

[0080] In step S206 of the above data processing method, the network range is detected based on the associated wide table, including: obtaining intrusion rules of the target area, and obtaining human activity data collected by the target collection device; based on the intrusion rules, using the target detection algorithm to detect the human activity data to obtain the detection results; if the detection results indicate that the human activity data is abnormal, generating abnormal information.

[0081] In some embodiments of the present application, the intrusion rules specifically specify which human activities are defined as abnormal or not allowed, such as the number of people occurring at a specific time or in a specific area, the type of activity (such as moving, long-term stay), etc. These intrusion rules are formulated based on the static data and security policies in the OSS resource system and can be adjusted according to actual conditions to ensure close monitoring of key areas such as computer rooms or data centers. Human activity data may include but is not limited to information such as the position, movement direction, and residence time of the human body. Through video analysis, this data can be extracted to form a structured data set. The target detection algorithm is specifically used to identify and locate specific objects in an image. In the embodiments of the present application, it is mainly used to identify and analyze human activities. The target detection algorithm processes the collected human activity data, identifies the human body in the video, and analyzes whether its behavior complies with the intrusion rules. The target detection algorithm compares the human activity data with the intrusion rules of the target area to check whether the human activity exceeds the preset normal range. For example, if the rule stipulates a maximum number of people in the area within a specific time, the algorithm will check whether the number of people in the video violates this rule. When detection results indicate anomalies in human activity data, such as excessive crowds, unauthorized entry, or prolonged stays, the system identifies a security risk. Upon confirmation of an anomaly, the system automatically generates an anomaly message, including a detailed description of the anomaly, the time of occurrence, and the location. This message is then sent to security management or maintenance personnel, triggering appropriate security responses, such as on-site verification, video playback, and alarm notifications. This allows for swift action to prevent potential security threats.

[0082] By combining device location information from a wide-scale correlation table to extract and apply specific intrusion rules and analyzing human activity data using object detection algorithms, the security status of target areas within the network can be automatically monitored and assessed. This approach improves security monitoring efficiency, reduces reliance on manpower, and enables immediate detection and response to potential intrusion incidents, providing strong technical support for security strategies in communications rooms and data centers.

[0083] In step S206 of the above-mentioned data processing method, the network range is detected based on the associated wide table, including: determining the temperature data and capacity data of the professional equipment from the associated wide table, wherein the temperature data includes the periodicity, temperature trend and noise data of the temperature data; using the detection model to predict the temperature data and capacity data to obtain a prediction result, wherein the temperature prediction result in the prediction result is used to determine the air conditioning adjustment strategy.

[0084] In some embodiments of the present application, in the associated wide table, the record of each device not only contains location information and status, but also real-time performance data, including temperature data and capacity data. Temperature data reflects the ambient temperature of the device when it is operating, while capacity data is a measure of the current power usage of the device. Temperature data has periodicity (such as day and night temperature difference, seasonal changes), temperature trend (temperature changes over time), and noise data (random fluctuations). Capacity data reflects the load condition of the device and is an important indicator for assessing whether the device is approaching or exceeding its design capacity. The raw data obtained through the associated wide table may contain missing values or outliers and requires preprocessing, such as using a moving average to fill missing values and correct outliers. The periodicity, trend, and noise data of the temperature data are extracted as input features of the detection model. These features help the detection model understand the patterns of temperature changes and background noise. Models suitable for time series prediction are selected, such as ARIMA (autoregressive integrated moving average model) and ETS (exponential smoothing state space model). These models can capture the time dependence of data and make predictions. The preprocessed temperature and capacity data are fed into the detection model. Based on historical data, the detection model predicts future temperature and capacity trends. Specifically, the detection model predicts temperature changes over a period of time, including the predicted temperature value and the possible fluctuation range. If the prediction indicates that the temperature will exceed comfortable or safe ranges, action is required to adjust the air conditioning system. Based on the temperature prediction results, the system automatically generates air conditioning adjustment strategies, such as preemptively initiating cooling programs, adjusting fan speeds, and changing temperature set points. These strategies ensure that equipment operates within optimal temperature environments, thereby reducing the risk of equipment failure due to overheating. The capacity data prediction results help operations and maintenance personnel plan power resources, avoid overloads, and ensure stable equipment operation. For example, real-time service power data is collected and compared with the maximum rack capacity in the OSS resource system (e.g., every 10 minutes) to promptly identify capacity limit violations. From the service performance data interface, obtain the power data of business equipment in a 10-minute dimension. According to the business resource interface, the power data of the same column of equipment is accumulated and added to form the maximum actual power within the time period of a single rack. Combined with the rated power of this rack obtained from the OSS resource system, it is generally 10 kW. If it is found to exceed the limit, a hidden danger list is generated, and on-site maintenance personnel are notified point-to-point to arrange a focused inspection of the business equipment and prepare a cutover plan. The business equipment is cutovered within a certain period of time to reduce the load value and ensure stable operation.

[0085] In addition, through the modular configuration capabilities of each system in the new generation system, the above-mentioned early warning methods from collection, data processing, model building, fitting analysis, and early warning output are all configured in each new generation system, existing in the form of atomic API interfaces, and finally integrated into the personal workbench for unified call presentation, providing maintenance personnel with a flexible, editable, self-definable, and personal maintenance charter interface, thereby improving R&D and maintenance efficiency.

[0086] The specific implementation process is as follows:

[0087] 1. Create and collect atomic capabilities and form APIs for calling: Relying on the powerful atomic capability editing capabilities of the operation and maintenance center, resource data, B-interface dynamic environment data, professional alarm data, professional performance data, etc. are created into APIs for use. Scheduled and real-time collection tasks are configured, and alarms are used as trigger sources to collect all data from related devices in the topology in each time period as basic data to be processed and called.

[0088] 2. Establish an end-to-end fault tracing linkage model calling atoms: The collected data and the established power professional upstream topology are used as input parameters. Through the self-learning end-to-end alarm topology causal relationship, the current alarm and the alarms in the similar time period are analyzed in real time, and a high-level knowledge graph is constructed to recommend possible alarm propagation paths to find the root cause.

[0089] 3. Establish a key area intrusion linkage model to call atoms: Use the target detection algorithm to configure the monitoring area, and analyze the real-time monitoring screen and device information in a linkage manner to detect the real-time video stream, detect the position of the human body in the video, track the human body, and determine whether the human body enters or leaves the intrusion area.

[0090] 4. Establish a dynamic temperature field and capacity warning linkage model: Use historical temperature data and real-time capacity data for more than 10 days as input parameters, call the ARIMA and ETS prediction models to predict future performance data, compare the predicted value with the resource rated data, and if it exceeds the threshold, issue a warning and output a reminder for maintenance personnel to pay close attention.

[0091] 5. Configure the workbench presentation page: Integrate the above APIs and manage the workbench for unified presentation and configuration, so that on-site maintenance personnel can select different stations, computer rooms, and equipment dimensions for graphical presentation according to the actual situation of their chartered flights, truly achieving one map for each person and a glance at key maintenance information.

[0092] When the method in this application is implemented, it is necessary to form an end-to-end association of the entire power supply, integrate a wide table of all professional data, collect static resource data and collected data of professional equipment and dynamic environment equipment, establish a linkage model, introduce end-to-end fault tracing, key area intrusion linkage, ARIMA and ETS prediction models to further fit and process the end-to-end associated data to form a dynamic early warning. The following will explain in detail the linkage early warning of a cloud professional equipment.

[0093] The specific implementation process is as follows:

[0094] 1. Establish static resource acquisition interface capabilities to obtain computer room static data in the OSS system, mainly involving two tables: equipment static table and station computer room static table.

[0095] 2. Relying on the power supply, cooling, and environmental data collected by interface B and combined with the resource data in the previous step, a wide table with end-to-end correlation of all professional equipment is formed. The static data of each professional equipment is included in the management of this wide table based on the computer room.

[0096] For example, the attributes of a piece of static data include: business XX node, YWY-JS-JS-NJ-JS-01DXX-D01-01XX-HP-RX86XX-XXX, rack server, Nanjing XX01 Data Room, SZNJ01, Ning_A_Qinhuai District Data Center, 2022-01-20, in use, LTG001.

[0097] 3. Decouple professional equipment from network management, collect real-time data directly by controlling the underlying data, and configure data collection capabilities;

[0098] For example, the current collection interface is the post interface, and its name is url:http: / / 132.XXX.XXX.XXX:1XXXX / cloud / jsdx / alarmday.

[0099] 4. Based on the wide table data, the real-time collected data is associated. The associated attributes are: device name, device code, device type, computer room, computer room code, station, station code, device access time, device current status, previous column header, collection attribute name, and real-time collection value.

[0100] For example: business XX node, YWY-JS-JS-NJ-JS-01DXX-D01-01XX-HP-RX86XX-XXX, rack server, Nanjing XX01 data room, SZNJ01, Ning_A_Qinhuai District Data Center, 2022-01-20, in use, LTG001, chip temperature, 51 degrees.

[0101] 5. Configure daily collection tasks, update wide table static data and real-time data to ensure data timeliness, configure external output capabilities, the input parameter is the device code, the output parameter is the affiliated computer room, affiliated station, previous column header, collection attribute name, real-time collection value, and finally complete the associated management of all network devices.

[0102] 6. Configure the B-interface power supply equipment scheduled data collection task, mainly for the terminal cabinet, distribution panel, power supply, and high-voltage incoming line.

[0103] For example: Power supply equipment collection data: 48V battery pack voltage #001: 53.6;----48V battery pack voltage #002: 53.6;----Battery pack current #001: 0;----Battery pack current #002: 0;----Rectifier module current #001: 32.99;----Rectifier module current #002: 32.43;----Rectifier module current #003: 32.84;----Rectifier module current #004: 32.68;----Rectifier module current #005: 32.8;----Rectifier module current #006: 32.86;----Rectifier module current #007: 32.66;----Rectifier module current #008: 32.87;----Rectifier module current #009: 32.51 ... Rectifier module current #010: 32.44;----Rectifier module temperature #001: 37.18;----Rectifier module temperature #002: 37.95;----Rectifier module temperature #003: 36.23;----Rectifier module temperature #004: 37.02;----Rectifier module temperature #005: 37.77;----Rectifier module temperature #006: 35.78;----Rectifier module temperature #007: 36.06;----Rectifier module temperature #008: 37.19;----Rectifier module temperature #009: 36.96;----Rectifier module temperature #010: 36.75;----48V DC system total DC voltage #001: 53.595;----48V DC system total load current #001: 327.

[0104] 7. Configure the regular collection interface of professional resources and performance data to obtain professional equipment rack and performance data in real time.

[0105] 1) Resource data interface: data_dev,

[0106] For example: Obtained resource data field: IT equipment naming example: YWY-XX-JS-NJ-XX-01D04XX-D01-01U17-HP-RX86XX-SEV.

[0107] 2) Alarm data acquisition interface: alarm_dev, uses the device IP for mapping, and the interface adopts the post port docking method to obtain the unique ID, title, status and IP address of the device alarm; for example:

[0108]

[0109]

[0110] 3) Performance data docking interface: performance_dev, using the device IP address to obtain the device name, IP address, collection time, type, and real-time value; for example:

[0111]

[0112] 8. Establish an end-to-end fault tracing linkage model. First, obtain the power supply topology of the station and form a connection from the head cabinet to the station’s household line.

[0113] For example: CT cloud zabbix_132.115.***.***, LTG001, UPS001, GYPD001, forming 4 key equipment code associations: business equipment code, terminal cabinet code, UPS code, distribution cabinet incoming line code.

[0114] 9. Through the resource interface above, obtain the rack, U position, and computer room information where the business equipment is located, and then automatically supplement the power supply connection in the previous step.

[0115] For example: YWY-XX-JS-NJ-XX-01D04XX-D01-01U17-HP-RX86XX-SEV, CT Cloud zabbix_132.115.***.***, LTG001, UPS001, GYPD001. After IP association, the corresponding racks are: D01, U17, CT Cloud zabbix_132.115.***.***, LTG001, UPS001, GYPD001.

[0116] 10. The TTPM causal discovery algorithm is introduced, and the power supply relationship is output as an input parameter into the model and the alarm data is accessed in real time. This allows all alarms occurring in the same time period to automatically analyze the root cause of the fault according to the power supply correlation. That is, when a business alarm occurs, it will automatically find out whether there is an alarm in the previous type of equipment in the same station, the same computer room, and the same column based on the type of alarm. Then, it will extend the search to find out whether there is an alarm in the previous power supply equipment, and trace it back to the final power supply line.

[0117] For example, if the input parameters are the alarm data in steps 6 and 7 and the topology data in step 9, the output will be:

[0118] {'node':'Low-voltage power distribution','daltime':'2024-11-14 16:43:20','alarmuniqueid':'96ab3757703b468222318f13a53c43b2','event':'Low-voltage power distribution'} indicates that the root cause is a fault in the low-voltage power distribution part.

[0119] 11. The fault tracing model is embedded in the fault production dispatch process. When maintenance personnel receive a fault ticket, they can automatically obtain the cause of the fault from the work order without manually asking other professionals. If the fault is caused by their own profession, they will repair it immediately. If the fault is caused by other professions, they will wait for the source fault to be handled and check whether the fault has been restored.

[0120] 12. Establish a key area intrusion linkage model to realize immediate warning if any abnormal personnel move the equipment on the key core equipment site, reminding maintenance personnel to pay attention. First, automatically associate the rack information field in the resource table with the camera location field of the same station and computer room to form a full coverage association of cameras based on the equipment rack dimension;

[0121] For example: Select XX station and computer room for configuration, click the AI task management configuration interface, select the regional intrusion application, set the time period for deployment and the trigger retention time. The current setting is 00:00-23:59 full-day monitoring. Combined with the risk operation process, power on and off process, and maintenance process, normal alarms are removed and effective alarms are retained.

[0122] 13. Configure personnel intrusion rules, associate them with fault repair work orders, daily risk operation orders, heavy insurance orders, power on and off orders, and other orders that actually need to enter the computer room. Automatically update the camera intrusion rules every day to quickly determine whether there is any unauthorized intrusion in this area.

[0123] 14. Introduce a video target detection algorithm, and output the real-time video data of the camera and intrusion rules as input parameters to the AI model. If any abnormal operation of the equipment occurs, it will promptly alert local maintenance personnel to check immediately and avoid failures caused by improper human operation. For example, the intrusion detection results are shown in the following table, and a warning screenshot is output:

[0124]

[0125] 15. Establish a dynamic temperature field and capacity warning linkage model to realize temperature field and capacity linkage warning.

[0126] 1) Realize the linkage between the core equipment and the surrounding computer room temperature field, automatically turn on or configure the air conditioner according to the business situation to ensure that the actual temperature felt by the equipment is in the "comfortable" range, reducing the failure rate of business equipment. First, process the missing values and outliers in the business temperature performance data, and use the moving average to fill the empty data and outliers. Next, start the configuration of the anomaly detection task, introduce the ARIMA and ETS prediction models to extract the periodicity, trend and noise in the normalized performance data through STL, fit the model to the processed data, and make predictions through the generated model. The prediction results are output to the judgment model for judgment, and the final output is how to control the status of the air conditioner in the computer room;

[0127] 2) Implement early warning of core equipment rack capacity, collect business power data in real time, and compare it with the maximum capacity of the rack in the OSS resource system in real time to promptly identify capacity over-limit risks. From the business performance data interface, obtain the power data of business equipment in a 10-minute dimension. According to the business resource interface, accumulate and add the power data of the equipment in the same column to form the maximum actual power within the time period of a single rack. Combined with the rated power of this rack obtained from the OSS resource system, it is generally 10KW. If an over-limit is found, a hidden danger list is generated, and on-site maintenance personnel are notified point-to-point to arrange key inspections of business equipment and prepare cutover plans. Cutover of business equipment within a certain period of time to reduce the load value and ensure stable operation.

[0128] For example, the maximum capacity values collected on the same day for four racks in the computer room are compared with the rated values:

[0129]

[0130] 16. Create atomic capabilities and form APIs for calling; establish an end-to-end fault tracing linkage model to call atoms; establish a key area intrusion linkage model to call atoms; establish a dynamic temperature field and capacity warning linkage model atomic.

[0131] 17. Configuration workbench presentation page: Integrate the above APIs and manage the workbench for unified presentation configuration, so that on-site maintenance personnel can select different stations, computer rooms, and equipment dimensions for graphical presentation based on the actual situation of their charter flights.

[0132] For example, IDC computer room maintenance personnel configure the most important rack capacity warnings, computer room environment, rack business real-time alarms, and video warnings in Nanjing IDC computer room charter maintenance on one map, allowing them to view environmental changes in the maintenance area at a glance and promptly identify abnormalities with red dots.

[0133] Through the above process, the data processing method provided by the embodiment of the present application has the following advantages:

[0134] 1) In terms of power supply assurance, this solution addresses the pain point of maintenance personnel being unable to promptly detect abnormal trends in important power supply conditions in the computer room before an alarm is generated. Real-time dynamic analysis of alarms, performance, and video data is performed. Combined with the one-screen integration capabilities of the intelligent workbench, this solution uses automated means to monitor power supply hazards in real time, which would otherwise require manual on-site inspections to detect in advance.

[0135] 2) In terms of core equipment security, the method in the embodiment of the present application can use automated collection and topology association capabilities to automatically monitor and inspect blind spots in real time, effectively avoiding unmanned control areas.

[0136] 3) In terms of customer experience, we provide a more flexible and stable suite of proprietary maintenance products, helping customers fully understand the operational status of their services and objectively presenting operator maintenance work to users in a numerically quantified manner. By enabling maintenance personnel to independently configure user needs, even small requests can be optimized within three days, improving customer experience and reducing reliance on manufacturers.

[0137] Figure 3 is a structural diagram of a data processing device according to an embodiment of the present application, such as Figure 3 As shown, the device includes:

[0138] an acquisition module 30 for acquiring real-time data of each professional device within the network range through a target interface, and acquiring static data within the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, and wherein the static data is used to represent the attributes and resource allocation information of each device within the network range;

[0139] A determination module 32 is configured to determine, based on real-time data and static data, an associated wide table corresponding to each device in the network range;

[0140] The detection module 34 is used to detect the network range according to the associated width table.

[0141] In the determination module in the above-mentioned data processing device, the determination module is also used to obtain the device code in the static data, and to obtain the first association relationship between the real-time data; based on the device code, the real-time data of the device corresponding to the device code is mapped to the static data to obtain the first mapping data; based on the first association relationship, the hierarchical relationship between the devices in the first mapping data is determined, wherein the hierarchical relationship includes the power supply relationship between the front end of the device to the end end of the device; based on the hierarchical relationship and the first mapping data, the association wide table is determined.

[0142] In the determination module in the above-mentioned data processing device, the determination module is also used to obtain the hierarchical relationship and device information between each device in the association wide table; determine each device information as a node in the association topology diagram, and determine the connection relationship between the nodes based on the hierarchical relationship, wherein when there is a hierarchical relationship between adjacent nodes, the corresponding nodes are connected with lines; convert the association wide table into an association topology diagram based on the node and connection relationship.

[0143] In the detection module in the above-mentioned data processing device, the detection module is also used to obtain the associated topology map and real-time alarm data corresponding to professional equipment within the network range; a causal discovery algorithm is used to perform root cause analysis on the associated topology map and real-time alarm data to determine the actual fault cause of the real-time alarm data.

[0144] In the detection module in the above-mentioned data processing device, the detection module is also used to determine the device location information of the target device from the associated wide table, and determine the target acquisition device based on the device location information, wherein the target acquisition device is used to collect data of the target area; obtain the video stream data collected by the target acquisition device, and obtain the list of people allowed to enter the target area and the corresponding real face image set from the target area application form, wherein the target device is located within the target area of the network range; identify the first face image in the video stream data, compare the first face image with the face images in the real face image set, and obtain a comparison result; when the comparison result indicates that the first face image is different from all face images in the real face image set, it is determined that there is a human intrusion in the target area.

[0145] In the detection module in the above-mentioned data processing device, the detection module is also used to obtain the intrusion rules of the target area and the human activity data collected by the target collection device; according to the intrusion rules, the target detection algorithm is used to detect the human activity data to obtain the detection results; when the detection results indicate that there is an abnormality in the human activity data, abnormal information is generated.

[0146] In the detection module in the above-mentioned data processing device, the detection module is also used to determine the temperature data and capacity data of professional equipment from the associated wide table, wherein the temperature data includes the periodicity, temperature trend and noise data of the temperature data; the detection model is used to predict the temperature data and capacity data to obtain a prediction result, wherein the temperature prediction result in the prediction result is used to determine the air conditioning adjustment strategy.

[0147] It should be noted that Figure 3 The data processing device shown is used to perform Figure 2 The data processing method shown, therefore the relevant explanations in the above data processing method are also applicable to the data processing device, and will not be repeated here.

[0148] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute program instructions to implement the following functions: obtaining real-time data of each professional device within the network range through a target interface, and obtaining static data in the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device in the network range; based on the real-time data and the static data, determining the associated wide table corresponding to each device in the network range; and detecting the network range based on the associated wide table.

[0149] It should be noted that the above electronic equipment is used to perform Figure 2 The data processing method shown, therefore the relevant explanations in the above data processing method are also applicable to the electronic device and will not be repeated here.

[0150] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the following data processing method by running the computer program: obtaining real-time data of each professional device within the network range through a target interface, and obtaining static data in the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, wherein the static data is used to represent the attributes and resource allocation information of each device in the network range; based on the real-time data and the static data, determining the associated wide table corresponding to each device in the network range; and detecting the network range based on the associated wide table.

[0151] It should be noted that the above non-volatile storage medium is used to execute Figure 2 The data processing method shown, therefore the relevant explanations in the above data processing method are also applicable to the non-volatile storage medium, and will not be repeated here.

[0152] An embodiment of the present application further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the data processing method in each embodiment of the present application.

[0153] The embodiments of the present application also provide a computer program, which, when executed by a processor, implements the steps of the data processing method in each embodiment of the present application.

[0154] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0155] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0156] 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 exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as 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.

[0157] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0158] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0159] 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 application 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 for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as 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.

[0160] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: include: Acquiring real-time data of each professional device within a network range through a target interface, as well as acquiring static data within the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, and wherein the static data is used to represent attributes and resource allocation information of each device within the network range; Determine, based on the real-time data and the static data, an associated wide table corresponding to each device in the network range; The network range is detected according to the associated width table.

2. The method according to claim 1, characterized in that Determining, based on the real-time data and the static data, an associated wide table corresponding to each device in the network range, including: Obtaining a device code in the static data and a first association relationship between the real-time data; According to the device code, mapping the real-time data of the device corresponding to the device code to the static data to obtain first mapping data; Determining, based on the first association relationship, a hierarchical relationship between the devices in the first mapping data, wherein the hierarchical relationship includes a power supply relationship between a front-end device and a back-end device; The associated wide table is determined according to the hierarchical relationship and the first mapping data.

3. The method according to claim 2, characterized in that According to the real-time data and the static data, After determining the associated wide table corresponding to each device in the network range, the method further includes: Obtaining the hierarchical relationship between the devices and the information of each device in the association wide table; Determine the device information as nodes in an association topology graph, and determine the connection relationship between the nodes based on the hierarchical relationship, wherein when a hierarchical relationship exists between adjacent nodes, the corresponding nodes are connected by lines; The association wide table is converted into the association topology graph according to the nodes and the connection relationships.

4. The method according to claim 3, characterized in that Detecting the network range according to the associated width table includes: Obtaining the associated topology map and real-time alarm data corresponding to the professional equipment within the network range; A causal discovery algorithm is used to perform root cause analysis on the associated topology diagram and the real-time alarm data to determine the actual fault cause of the real-time alarm data.

5. The method according to claim 1, wherein Detecting the network range according to the associated width table includes: Determining device location information of a target device from the associated wide table, and determining a target acquisition device based on the device location information, wherein the target acquisition device is used to acquire data of a target area; Obtaining video stream data collected by the target acquisition device, and obtaining a list of persons allowed to enter the target area and a corresponding set of real face images from a target area application form, wherein the target device is located within the target area of the network range; Identifying a first facial image in the video stream data, and comparing the first facial image with facial images in the set of real facial images to obtain a comparison result; If the comparison result indicates that the first facial image is different from all facial images in the real facial image set, it is determined that there is human intrusion in the target area.

6. The method according to claim 5, characterized in that Detecting the network range according to the associated width table includes: Obtaining intrusion rules for the target area and obtaining human activity data collected by the target collection device; According to the intrusion rules, a target detection algorithm is used to detect the human activity data to obtain a detection result; When the detection result indicates that the human activity data is abnormal, abnormal information is generated.

7. The method according to claim 1, characterized in that Detecting the network range according to the associated width table includes: Determining temperature data and capacity data of the professional equipment from the associated wide table, wherein the temperature data includes periodicity, temperature trend, and noise data of the temperature data; The temperature data and the capacity data are predicted using a detection model to obtain a prediction result, wherein the temperature prediction result in the prediction result is used to determine an air conditioning adjustment strategy.

8. A data processing device, characterized in that: include: an acquisition module, configured to acquire real-time data of each professional device within a network range through a target interface, and to acquire static data within the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, and wherein the static data is used to represent attributes and resource allocation information of each device within the network range; A determination module, configured to determine, based on the real-time data and the static data, an associated wide table corresponding to each device in the network range; A detection module is used to detect the network range according to the associated wide table.

9. An electronic device, characterized in that: include: a memory for storing program instructions; a processor, connected to the memory, and configured to execute program instructions for implementing the following functions: obtaining real-time data of each professional device within a network range through a target interface, and obtaining static data within the network range, wherein the target interface is a standard interface for power environment monitoring within the network range, and wherein the static data is used to represent attributes and resource allocation information of each device within the network range; and determining, based on the real-time data and the static data, an associated wide table corresponding to each device within the network range; The network range is detected according to the associated width table.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the data processing method according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the data processing method according to any one of claims 1 to 7 is implemented.

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