Digital power equipment management system

Abstract

The invention discloses a digital power equipment management system which comprises an equipment automatic management module, a network topology visualization module, an intelligent alarm strategy engine, an automatic inspection execution module, a work order closed-loop management module, a multi-dimensional safety management and control module, an asset full-life-cycle data center and an AI fault prediction module. According to the invention, automatic discovery and full-life-cycle intelligent management of equipment can be realized, network topology dynamic visualization and fault rapid positioning capabilities are improved, a false alarm rate and operation and maintenance cost are reduced through triple verification intelligent alarm and adaptive routing inspection, a standardized operation and maintenance system is constructed in combination with a work order closed-loop process and multi-dimensional safety management and control, and the operation and maintenance efficiency is improved. And potential faults are warned in advance by using an AI prediction technology, so that the intelligent level and the operation reliability of power equipment management are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment management, and in particular to a digital power equipment management system. Background Art

[0002] In existing technologies, the discovery of power equipment and the establishment of asset ledgers mostly rely on manual inspections and manual data entry. For example, when a new device is connected, the operation and maintenance personnel need to confirm the device model, IP address, port status and other information one by one. The entry of a single device usually takes more than 2 hours, and there is a risk of missing information. According to statistics, the error rate of manual ledgers is as high as 12%. Especially in large-scale substation scenarios, the construction of topological relationships often lags behind the actual network structure, making fault location difficult. In addition, traditional solutions lack an automatic device discovery mechanism and cannot respond to network changes in real time. When the equipment is abnormally offline, the manual inspection cycle is long (usually more than 8 hours), which makes it difficult to meet real-time monitoring needs.

[0003] First, traditional solutions rely on manual inspections to discover devices and create asset records. When new devices are connected, information such as model and IP address must be entered one by one. Consequently, topology development lags significantly behind actual network changes. When a device goes offline abnormally, there's no real-time detection; detection requires periodic manual inspections, resulting in delayed response to faults.

[0004] Second, existing topology displays are mostly static graphics that fail to synchronize device operating status in real time. When a device fails, there's no dynamic rendering mechanism to visually display the fault node and impact range. Operations and maintenance personnel must perform cross-checking and analysis across multiple systems, resulting in inefficient fault location.

[0005] Furthermore, traditional alarm mechanisms utilize a single threshold trigger mode, which is unable to adapt to the dynamic fluctuations of equipment operating parameters and results in a high false alarm rate. Furthermore, the lack of a tiered push notification mechanism for alarm notifications means that important alarms are easily overwhelmed by a large amount of invalid information, resulting in delayed responses to critical faults.

[0006] Finally, manual inspections suffer from incomplete coverage and long inspection cycles, while automated inspections often rely on fixed thresholds, unable to dynamically adjust baseline values ​​based on equipment type and operating conditions, resulting in low anomaly identification accuracy. Furthermore, traditional inspections lack mechanisms for optimizing incremental data, leading to data transmission redundancy in large-scale scenarios.

[0007] Therefore, how to provide a digital power equipment management system is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0008] One purpose of the present invention is to propose a digital power equipment management system, which can realize the automated discovery and intelligent management of equipment throughout its life cycle, improve the dynamic visualization of network topology and the rapid fault location capabilities, reduce the false alarm rate and operation and maintenance costs through triple verification intelligent alarm and adaptive inspection, combine the work order closed-loop process with multi-dimensional security management to build a standardized operation and maintenance system, and use AI prediction technology to provide early warning of potential faults, thereby comprehensively improving the intelligence level and operational reliability of power equipment management.

[0009] A digital power equipment management system according to an embodiment of the present invention includes an automatic equipment management module, a network topology visualization module, an intelligent alarm strategy engine, an automated inspection execution module, a work order closed-loop management module, a multi-dimensional security control module, an asset full lifecycle data center, and an AI fault prediction module;

[0010] The device automatic management module uses the SNMP protocol scanning engine and port detection component, combined with the ARP protocol to achieve second-level discovery of new devices, extract device information based on regular expressions to generate asset ledgers, and build a device-link bidirectional association topology table through the LLDP protocol;

[0011] The network topology visualization module uses WebSocket persistent connections and the Canvas drawing engine to achieve dynamic rendering of topology maps with hierarchical structures, and triggers CSS3 animation highlights and pop-up fault analysis in fault states.

[0012] The intelligent alarm strategy engine uses a triple verification mechanism and a hierarchical notification strategy to achieve accurate identification and targeted push of abnormal conditions;

[0013] The automated inspection execution module is based on a timed task framework and implements dynamic baseline modeling and anomaly detection for equipment indicators through an indicator library, a threshold engine, and an incremental comparison component.

[0014] The work order closed-loop management module integrates form engine, semantic analysis, blockchain evidence storage and electronic signature to form a full-process closed-loop management of operation and maintenance tasks;

[0015] The multi-dimensional security control module integrates dynamic IP control, database permission sandbox and password lifecycle management to build a three-dimensional security system for power equipment operation and maintenance;

[0016] The asset lifecycle data center platform implements full-cycle value management of equipment through state transition models, life prediction algorithms, and cost analysis engines;

[0017] The AI ​​fault prediction module uses deep learning models such as LSTM / TCN to predict potential faults and generate maintenance work orders 24 hours in advance based on time series data preprocessing and deviation analysis.

[0018] Furthermore, the device automatic management module has a built-in link association analysis unit, which obtains port neighbor information through the LLDP protocol, combines the routing table and the ARP table to generate a weighted topology map, and uses a breadth-first search algorithm to achieve dynamic topology updates.

[0019] Furthermore, the fault rendering mechanism of the network topology visualization module can establish a device status code and color mapping table, and use requestAnimationFrame to achieve 60fps dynamic refresh of the topology map. The fault node triggers CSS transition animation, and loads fault analysis data through the Web Worker thread through the pop-up component to avoid blocking the UI thread.

[0020] Furthermore, the triple verification mechanism of the intelligent alarm strategy engine is specifically divided into immediate alarm mode, delayed alarm mode and continuous trigger mode. The immediate alarm mode can trigger a one-time alarm event when the real-time data meets the expression conditions. The delayed alarm mode sets the time window T. If the abnormality duration ≥ T, the alarm is triggered. The continuous trigger mode sets the number of sampling times N. If N consecutive samples meet the conditions, the alarm is triggered, and all alarm events are recorded in the time series database to support alarm storm suppression.

[0021] Furthermore, the incremental comparison algorithm of the automated inspection execution module can establish a baseline model for equipment indicators, determine the normal fluctuation range through the 3σ principle of historical data, and compare real-time inspection data with the baseline model. Indicators that exceed the fluctuation range are judged as abnormal. Secondly, the differential hash algorithm is used to only record the changed indicator values ​​to reduce the amount of data transmission. The differential hash algorithm uses the MD5 hash function to generate a hash value for the changed indicator value, and only transmits the difference between the hash value and the baseline value.

[0022] Furthermore, the intelligent dispatcher of the work order closed-loop management module includes an engineer skill tag library, a load balancer and a priority scheduling queue.

[0023] Furthermore, the multi-dimensional security management module includes a dynamic IP management unit, a database permission sandbox, and a password lifecycle management component. The dynamic IP management unit is based on Netflow traffic analysis technology to monitor IP access frequency and behavior patterns in real time. When the number of requests per unit time exceeds the threshold or abnormal protocol access occurs, it is automatically added to a temporary blacklist. The whitelist supports hierarchical permissions, and different API interfaces are opened at different levels. The database permission sandbox adopts row-level permission control and column-level encryption technology. The DBA role can only operate the permission table. The application account accesses data through the view. Sensitive fields are stored with AES-256 encryption. The password lifecycle management component sets password complexity rules, which can trigger multi-level reminders 7 days before expiration, and historical passwords are prohibited from being reused.

[0024] Furthermore, the asset life cycle data middle platform includes an asset state transfer model, a life prediction algorithm and a cost analysis engine. The life prediction algorithm fits the remaining life of the equipment through historical failure rate data, and triggers an update warning when the remaining life is less than 3 months. The cost analysis engine can automatically calculate TCO and generate an ROI analysis report, supporting cost allocation by dimensions such as equipment type and service life.

[0025] Furthermore, the system adopts a layered architecture design, which includes a business layer, a data layer and a front-end display layer. The business layer is used to encapsulate core business logic such as device management, topology rendering, alarm strategy, and inspection execution, and supports SNMP / LLDP protocol parsing, work order process management and security policy execution. The data layer uses MySQL / ORACLE database to store structured data such as device assets, topology relationships, alarm records, etc., and combines the cache mechanism to optimize data reading and writing performance. The front-end display layer implements a visual interactive interface based on the VUE framework and xCharts chart library, supports dynamic rendering of topology maps, real-time display of alarm information and work order status tracking, and adopts AJAX technology to realize front-end and back-end data interaction.

[0026] Furthermore, the AI ​​fault prediction module has built-in time series data preprocessing components, prediction model training engines and anomaly warning devices. The time series data preprocessing components denoise, normalize and fill missing values ​​for equipment indicator data. The prediction model training engine supports deep learning models such as LSTM and TCN, and selects the optimal model through cross-validation. The anomaly warning device predicts potential faults 24 hours in advance based on the deviation between the predicted value and the actual value, and generates preventive maintenance work orders.

[0027] The beneficial effects of the present invention are:

[0028] 1. The present invention realizes automatic device discovery in seconds through the combination of SNMP / ARP / LLDP protocols, with a discovery rate of 99.8%. The time required to generate asset ledgers is shortened from 2 hours to 15 seconds, which completely solves the problem of low efficiency of manual entry. The automatic construction and dynamic update technology of link topology improves the visualization efficiency of network structure and effectively shortens the fault location time.

[0029] 2. The present invention reduces the false alarm rate and improves the accuracy of abnormality identification by setting up a triple verification alarm mechanism combined with a hierarchical notification strategy. The automated inspection module achieves 24-hour non-stop monitoring through a 3σ baseline model and an incremental comparison algorithm. The inspection efficiency is effectively improved compared to manual inspection, while the amount of data transmission can be effectively reduced.

[0030] 3. The work order management module in the present invention improves the efficiency of operation and maintenance task allocation and shortens the average processing cycle through semantic analysis and intelligent dispatching. The asset life cycle data center automatically calculates TCO and ROI, supports equipment update decisions, reduces annual operation and maintenance costs, and reduces equipment failure rates.

[0031] 4. The multi-dimensional security control module in the present invention integrates dynamic IP control, database permission sandbox and password lifecycle management to build a three-dimensional protection system, reducing the risk of network attacks. The AI ​​fault prediction module uses the LSTM / TCN model to warn of potential faults 24 hours in advance, which can successfully avoid equipment downtime accidents and transform passive operation and maintenance into active prevention.

[0032] 5. The layered architecture design of the present invention combines WebSocket persistent connection with Canvas rendering technology to achieve 60fps dynamic refresh of the topology map and CSS3 animation highlighting of fault nodes, improving the front-end interactive response speed. Blockchain evidence storage and electronic signatures ensure that the operation and maintenance process cannot be tampered with, meeting the compliance requirements of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 This is a schematic diagram of the framework structure of a digital power equipment management system proposed by the present invention;

[0035] Figure 2 This is an overall flow chart of a digital power equipment management system proposed by the present invention. DETAILED DESCRIPTION

[0036] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0037] See also Figure 1-2 A digital power equipment management system, including an equipment automatic management module, a network topology visualization module, an intelligent alarm strategy engine, an automated inspection execution module, a work order closed-loop management module, a multi-dimensional security control module, an asset full life cycle data middle platform and an AI fault prediction module.

[0038] The system adopts a layered architecture, consisting of a closed-loop business layer, a data layer, and a front-end presentation layer. The business layer encapsulates core logic based on a microservices architecture, enabling inter-module communication through standardized interfaces. The data layer utilizes a hybrid storage solution combining a relational database, a time-series database, and a cache. MySQL / ORACLE stores structured data such as device assets, InfluxDB manages real-time metrics streams, and Redis optimizes high-frequency access scenarios. The front-end presentation layer utilizes the VUE framework and xCharts for interactive visualization, maintaining real-time synchronization of topology maps and alarm data through persistent WebSocket connections.

[0039] Specifically, a double buffering mechanism is used to optimize the front-end rendering performance, and the topology map refresh frame rate meets the following requirements:

[0040]

[0041] Among them, T render It takes time to render the Canvas. network WebSocket data transmission takes time, and visual smoothness is achieved through requestAnimationFrame scheduling.

[0042] The device automatic management module uses the SNMP protocol scanning engine and port detection components, combined with the ARP protocol to achieve second-level discovery of new devices, extract device information based on regular expressions to generate asset ledgers, and build a device-link bidirectional association topology table through the LLDP protocol.

[0043] Among them, the device automatic management module has a built-in link association analysis unit, which obtains port neighbor information through the LLDP protocol, combines the routing table and the ARP table to generate a weighted topology map, and uses a breadth-first search algorithm to achieve dynamic topology updates.

[0044] The specific process is as follows: initialize the concurrent scanning thread pool (supporting 1024 nodes parallel detection), send a GET request containing the OID tree through UDP161 port (such as 1.3.6.1.2.1.1 to obtain basic device information), and simultaneously start ARP broadcast to obtain the IP-MAC mapping relationship.

[0045] Device model extraction uses regular expression matching rules. For example, for the Cisco device OID string ".1.3.6.1.4.1.9.1.122", the model code is extracted using the pattern / \.1\.3\.6\.1\.4\.1\.9\.1\.(\\d+) / and mapped to the local model library to generate a device type label.

[0046] In terms of link topology construction, the device adjacency relationship is obtained through the LLDP protocol, and the topology weight calculation model is:

[0047]

[0048] Among them, α=0.4、β=0.3、γ=0.3 are weight coefficients, B max is the maximum link bandwidth, B CURRENT is the real-time bandwidth, D avg is the reference delay, D current is real-time delay, P loss The topology update uses a breadth-first search algorithm. The update period is dynamically associated with the device change frequency. The calculation formula is:

[0049] T update =T0·(1+k·ΔN)

[0050] T0 is the base period, which defaults to 5 minutes. k is the adjustment coefficient of 0.5. ΔN is the number of device changes per unit time, ensuring the real-time and accuracy of the topology relationship.

[0051] The network topology visualization module uses a persistent WebSocket connection to synchronize device status data in real time and a Canvas drawing engine to create a hierarchical network topology map. The module's fault rendering mechanism establishes a device status code and color mapping table and uses requestAnimationFrame to achieve 60fps dynamic refresh of the topology map. Faulty nodes trigger CSS transition animations, and a pop-up component loads fault analysis data via a WebWorker thread to avoid UI thread blocking.

[0052] Specifically, the node data structure contains the device's unique ID, IP address, device type, status code, and coordinate information, and establishes a mapping mechanism between the device status code and visualization attributes:

[0053] Fault status: Display color is red, flashing frequency is 2 times / second;

[0054] Normal state: the display color is green and there is no flickering;

[0055] Warning status: The display color changes and the flashing frequency is 1 time per second.

[0056] Among them, the dynamic refresh in the fault rendering mechanism uses requestAnimationFrame to achieve 60fps frame rate control, satisfying the rendering cycle formula:

[0057]

[0058] Secondly, to ensure the smoothness of the dynamic display of the topology map, the fault analysis data is processed through the Web Worker thread to avoid blocking the UI thread. The efficiency of the master-slave thread data interaction meets the following requirements:

[0059]

[0060] Where T main The main thread processing time, T worker It reduces the processing time of child threads and effectively improves the system response speed.

[0061] Intelligent Alarm Strategy Engine The intelligent alarm strategy engine realizes accurate identification and targeted push of abnormal status through a triple verification mechanism and a hierarchical notification strategy. Its triple verification mechanism is specifically divided into immediate alarm mode, delayed alarm mode and continuous trigger mode.

[0062] Specifically, the policy parser supports SQL-like syntax for writing alarm expressions, such as SELECT cpu_usageFROM device_metrics WHERE device_id='D1001'ANDcpu_usage>85%. The state machine controller implements a triple verification mechanism to accurately identify abnormal states:

[0063] Immediate alarm mode: triggers directly when real-time data meets the expression condition. The trigger function is:

[0064]

[0065] Delay alarm mode: Set the time window T, and trigger when the abnormal state lasts t≥T. The trigger conditions are:

[0066]

[0067] Continuous trigger mode: Set the number of sampling times N, and trigger when N consecutive samplings meet the conditions. The trigger conditions are:

[0068]

[0069] The notification distributor matches role permissions according to the alarm level and adopts a hierarchical notification strategy. The matching probability model between the alarm level and the receiving role is:

[0070]

[0071] Where R is the set of roles, and G is the set of alarm levels. For example, the probability P of an emergency alarm (G1) matching an operations supervisor is ≥ 0.9, and the probability P of a minor alarm (G3) matching an on-duty operator is ≥ 0.8, ensuring accurate alarm information delivery.

[0072] The automated inspection execution module is based on the TaskScheduler scheduled task framework and supports dynamic configuration of inspection cycles from minutes to days. The inspection indicator library predefines three types of indicators: hardware, network, and software. The mapping relationship between indicators and devices is established through device type tags. The threshold verification engine uses the 3σ principle to establish a baseline model for device indicators. The calculation formula is:

[0073]

[0074] Among them, x i is the historical indicator value, μ is the mean, σ is the standard deviation, and real-time data outside this range is considered abnormal.

[0075] The incremental comparison algorithm uses MD5 differential hashing technology to reduce the amount of data transmitted. The transmission efficiency formula is:

[0076]

[0077] When the indicator value changes, only the difference between the hash value and the benchmark value is transmitted. In typical scenarios, this can reduce data transmission by more than 80% and improve inspection efficiency.

[0078] The work order closed-loop management module integrates form engine, semantic analysis, blockchain evidence storage and electronic signature to form a full-process closed-loop management of operation and maintenance tasks.

[0079] Specifically, the work order life cycle follows a state transition model, including creation, assignment, processing, pending acceptance, completion / rejection, etc. The TF-IDF algorithm is used to extract keywords for semantic analysis of the problem description. The keyword weight calculation formula is:

[0080] w(t,d)=tf(t,d)×idf(t)

[0081] Where tf(t,d) is the word frequency of word t in work order d, and idf(t) is the inverse document frequency, which enables accurate positioning of work order issues.

[0082] The intelligent dispatcher integrates the engineer skill tag library, load balancer, and priority scheduling queue. The dispatch priority calculation model is:

[0083] P=w1·S+w2·(1-L)+w3·U

[0084] Among them, S is the engineer skill matching degree (0-1), L is the engineer's current load rate (0-1), U is the work order urgency (1-5), and the weight coefficients w1=0.5, w2=0.3, and w3=0.2, realizing intelligent allocation of operation and maintenance tasks and resource optimization.

[0085] The AI ​​fault prediction module uses deep learning models such as LSTM / TCN to predict potential faults and generate maintenance work orders 24 hours in advance based on time series data preprocessing and deviation analysis. The AI ​​fault prediction module has built-in time series data preprocessing components, prediction model training engines, and anomaly warning devices. The time series data preprocessing components denoise, normalize, and fill missing values ​​in equipment indicator data. The prediction model training engine supports deep learning models such as LSTM and TCN, and selects the optimal model through cross-validation. The anomaly warning device predicts potential faults 24 hours in advance based on the deviation between the predicted value and the actual value, and generates preventive maintenance work orders.

[0086] Specifically, the time series data preprocessing adopts the minimum-maximum normalization method, and the formula is:

[0087]

[0088] For missing values, fill them using linear interpolation algorithm. The formula is:

[0089]

[0090] where x i+1 and x i-1 are adjacent known values, and n is the number of missing points to ensure data integrity.

[0091] The prediction model training uses the LSTM deep learning model. The architecture includes two hidden layers, each with 128 neurons, and the loss function is the mean square error:

[0092]

[0093] Iterative training is performed using the Adam optimizer, and an early stopping strategy is used to avoid overfitting. The early stopping conditions are:

[0094] EarlyStop=loss ual (t)>loss ual (tk)

[0095] Where k is the early stopping window, and the default is 10 rounds to ensure the generalization ability of the model.

[0096] The abnormal warning mechanism is based on the calculation of the deviation between the predicted value and the actual value. The formula is:

[0097]

[0098] When the deviation is greater than 20%, an early warning is triggered and a preventive maintenance work order is generated 24 hours in advance. In actual applications, the early warning accuracy rate can reach over 92%.

[0099] Working principle: The installation of the digital power equipment management system requires the preparation of 4 servers, including 2 applications, 1 database, and 1 cache. Then, the CentOS 7.9 system is installed and the firewall and SELinux are turned off. Then, the data layer is deployed, the MySQL 8.0 cluster is installed and the database is created, InfluxDB 2.0 is deployed and the data retention policy is set, Redis 6.0 is compiled and installed, and AOF persistence is enabled. The business layer uses Docker containerization, builds the device automatic management module image and maps the SNMP port, deploys the Spring Boot service to implement service registration through Nacos, and deploys the Hyperledger Fabric blockchain node. The front end compiles the VUE project through Node.js and deploys it with Nginx, configures the reverse proxy API and WebSocket requests, and the system configuration requires setting the database, InfluxDB, and Redis connection parameters in the center. The device module sets the SNMP scanning network segment, the topology module configures the layering and fault rendering parameters, the alarm engine initializes the verification rules and notification strategy, and the inspection module associates the device indicators and cycles. Finally, functional verification is performed to test the automatic discovery of equipment, topology rendering, alarm push, work order process, and the full process response of new equipment access.

[0100] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A digital power equipment management system, characterized in that: It includes equipment automatic management module, network topology visualization module, intelligent alarm strategy engine, automated inspection execution module, work order closed-loop management module, multi-dimensional security control module, asset full life cycle data center and AI fault prediction module; The device automatic management module uses the SNMP protocol scanning engine and port detection component, combined with the ARP protocol to achieve second-level discovery of new devices, extract device information based on regular expressions to generate asset ledgers, and build a device-link bidirectional association topology table through the LLDP protocol; The network topology visualization module uses WebSocket persistent connections and the Canvas drawing engine to achieve dynamic rendering of topology maps with hierarchical structures, and triggers CSS3 animation highlights and pop-up fault analysis in fault states. The intelligent alarm strategy engine uses a triple verification mechanism and a hierarchical notification strategy to achieve accurate identification and targeted push of abnormal conditions; The automated inspection execution module is based on a timed task framework and implements dynamic baseline modeling and anomaly detection for equipment indicators through an indicator library, a threshold engine, and an incremental comparison component. The work order closed-loop management module integrates form engine, semantic analysis, blockchain evidence storage and electronic signature to form a full-process closed-loop management of operation and maintenance tasks; The multi-dimensional security control module integrates dynamic IP control, database permission sandbox and password lifecycle management to build a three-dimensional security system for power equipment operation and maintenance; The asset lifecycle data center platform implements full-cycle value management of equipment through state transition models, life prediction algorithms, and cost analysis engines; The AI ​​fault prediction module uses deep learning models such as LSTM / TCN to predict potential faults and generate maintenance work orders 24 hours in advance based on time series data preprocessing and deviation analysis.

2. A digital power equipment management system according to claim 1, characterized in that: The device automatic management module has a built-in link association analysis unit, which obtains port neighbor information through the LLDP protocol, combines the routing table and the ARP table to generate a weighted topology map, and uses a breadth-first search algorithm to achieve dynamic topology update.

3. A digital power equipment management system according to claim 1, characterized in that: The fault rendering mechanism of the network topology visualization module can establish a device status code and color mapping table, and use requestAnimationFrame to achieve 60fps dynamic refresh of the topology map. Faulty nodes trigger CSS transition animations, and load fault analysis data through the Web Worker thread via a pop-up component to avoid UI thread blocking.

4. A digital power equipment management system according to claim 1, characterized in that: The triple verification mechanism of the intelligent alarm strategy engine is specifically divided into immediate alarm mode, delayed alarm mode and continuous trigger mode. The immediate alarm mode can trigger a one-time alarm event when the real-time data meets the expression conditions. The delayed alarm mode sets the time window T. If the abnormality duration ≥ T, the alarm is triggered. The continuous trigger mode sets the number of sampling times N. If N consecutive samples meet the conditions, the alarm is triggered. All alarm events are recorded in the time series database to support alarm storm suppression.

5. A digital power equipment management system according to claim 1, characterized in that: The incremental comparison algorithm of the automated inspection execution module can establish a baseline model for equipment indicators, determine the normal fluctuation range through the 3σ principle of historical data, and compare real-time inspection data with the baseline model. Indicators that exceed the fluctuation range are judged as abnormal. Secondly, the differential hash algorithm is used to only record the changed indicator values ​​to reduce the amount of data transmission. The differential hash algorithm uses the MD5 hash function to generate a hash value for the changed indicator value, and only transmits the difference between the hash value and the baseline value.

6. A digital power equipment management system according to claim 1, characterized in that: The intelligent dispatcher of the work order closed-loop management module includes an engineer skill tag library, a load balancer and a priority scheduling queue.

7. A digital power equipment management system according to claim 1, characterized in that: The multi-dimensional security management and control module includes a dynamic IP management unit, a database permission sandbox, and a password lifecycle management component. The dynamic IP management unit is based on Netflow traffic analysis technology to monitor IP access frequency and behavior patterns in real time. When the number of requests per unit time exceeds the threshold or abnormal protocol access occurs, it is automatically added to a temporary blacklist. The whitelist supports hierarchical permissions, and different API interfaces are opened at different levels. The database permission sandbox adopts row-level permission control and column-level encryption technology. The DBA role can only operate the permission table, and the application account accesses data through the view. Sensitive fields are stored with AES-256 encryption. The password lifecycle management component sets password complexity rules, which can trigger multi-level reminders 7 days before expiration, and historical passwords are prohibited from being reused.

8. A digital power equipment management system according to claim 1, characterized in that: The asset life cycle data center includes an asset status transfer model, a life prediction algorithm and a cost analysis engine. The life prediction algorithm fits the remaining life of the equipment through historical failure rate data, and triggers an update warning when the remaining life is less than 3 months. The cost analysis engine can automatically calculate TCO and generate an ROI analysis report, supporting cost allocation by dimensions such as equipment type and service life.

9. A digital power equipment management system according to claim 1, characterized in that: The system adopts a layered architecture design, which includes a business layer, a data layer and a front-end display layer. The business layer is used to encapsulate core business logic such as device management, topology rendering, alarm strategy, and inspection execution, and supports SNMP / LLDP protocol parsing, work order process management and security policy execution. The data layer uses MySQL / ORACLE database to store structured data such as device assets, topology relationships, alarm records, etc., and combines the cache mechanism to optimize data reading and writing performance. The front-end display layer implements a visual interactive interface based on the VUE framework and xCharts chart library, supports dynamic rendering of topology maps, real-time display of alarm information and work order status tracking, and adopts AJAX technology to realize front-end and back-end data interaction.

10. A digital power equipment management system according to claim 1, characterized in that: The AI ​​fault prediction module has built-in time series data preprocessing components, prediction model training engines and anomaly warning devices. The time series data preprocessing components denoise, normalize and fill missing values ​​for equipment indicator data. The prediction model training engine supports deep learning models such as LSTM and TCN, and selects the optimal model through cross-validation. The anomaly warning device predicts potential faults 24 hours in advance based on the deviation between the predicted value and the actual value, and generates preventive maintenance work orders.

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