NFC (Near Field Communication) information interaction and management method for intelligent signboard of power equipment

Abstract

The invention provides an NFC information interaction and management method for an intelligent signboard of power equipment, and the method comprises the steps: extracting field problem feedback data from an obtained synchronous data flow, carrying out the mode recognition and priority sorting of the feedback data through a support vector machine algorithm, and determining a high-priority problem list; according to the determined high-priority problem list, a deep neural network algorithm is adopted to carry out prediction modeling on list data, the future equipment fault trend is judged, and a predictive maintenance suggestion data set is obtained; the obtained predictive maintenance suggestion data set is fused with the real-time interaction content, a closed-loop feedback instruction is generated in a background system, and guidance information update for field operation is obtained; and extracting verification parameters from the obtained guidance information update, performing integrity verification on the parameters through a hash function, judging the reliability of the updated data, and obtaining the finally confirmed closed-loop management information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information management, and particularly relates to an NFC information interaction and management method of an intelligent signboard of a power device. BACKGROUND

[0002] In the modern power industry, device management and operation and maintenance efficiency are directly related to the stability and safety of energy supply, and are important foundations for ensuring the smooth operation of society. With the continuous expansion of the power system scale, the number of devices has increased dramatically. How to achieve accurate acquisition and real-time interaction of device information has become an urgent need for industry development. Research in this field not only concerns technological innovation, but also has key significance for improving operation and maintenance quality and reducing safety risks.

[0003] However, many current device management methods have obvious shortcomings in practical application. Traditional management methods often rely on paper records or simple electronic tags, lack of dynamic connection between devices and personnel, resulting in information transmission lag, and on-site operation and background data difficult to synchronize. This information island phenomenon makes it difficult for maintenance personnel to quickly obtain targeted guidance when facing complex scenarios, increasing the likelihood of operational errors.

[0004] The deeper technical challenges are how to establish a secure and reliable interaction channel between physical devices and digital systems, and how to achieve intelligent adaptation of information according to different scenarios and personnel identities. If the connection between physical devices and digital systems lacks effective security mechanisms, there is a risk of data leakage or tampering; and if the information push content cannot be dynamically adjusted according to the on-site environment and personnel permissions, the interaction process may become chaotic or even ineffective. These two problems are closely related: a secure interaction channel is the foundation, and only on this basis can intelligent matching of information be further achieved, otherwise any customized push may lose its meaning due to security vulnerabilities. SUMMARY

[0005] The present application provides an NFC information interaction and management method of an intelligent signboard of a power device, mainly comprising: Through the operation state data and historical record data collected by the on-site device sensor, the collected data is distributed stored and verified using a blockchain encryption mechanism to obtain a set of device information data that has been securely authenticated; According to the obtained device information data set, a random forest algorithm is used to classify and process the data and detect anomalies to determine whether there are potential risk characteristics and obtain a risk assessment result after classification; The risk assessment result after classification is obtained, an encrypted channel is established between the mobile terminal interface and the background system in the inspection scene to determine the real-time push of the device state information, and the interaction content adapted to the personnel permissions is obtained; For the obtained interactive content, if it is detected that the data transmission delay exceeds a preset threshold, an edge computing module is used to preliminarily analyze the local data, judge the delay reason and optimize the transmission path, and obtain a low-delay synchronous data stream; Live problem feedback data is extracted from the obtained synchronous data stream, and the feedback data is subjected to pattern recognition and priority sorting through a support vector machine algorithm to determine a high-priority problem list; According to the determined high-priority problem list, a deep neural network algorithm is used to perform predictive modeling on the list data to judge future equipment failure trends and obtain a predictive maintenance recommendation data set; Through the obtained predictive maintenance recommendation data set and real-time interactive content, a closed-loop feedback instruction is generated in the background system to obtain guidance information updates for on-site operations; Verification parameters are extracted from the obtained guidance information updates, and the parameters are subjected to integrity checking through a hash function to judge the reliability of the update data, and finally confirmed closed-loop management information is obtained.

[0006] The technical scheme provided by the embodiment of the application can include the following beneficial effects: The application stores and verifies the equipment operation data in a distributed manner through blockchain encryption technology, ensures the data unalterability and security, fundamentally solves the data leakage and tampering risks, and at the same time, classifies and detects anomalies of the data through a random forest algorithm, and performs pattern recognition and fault prediction through a support vector machine and a deep neural network algorithm, thereby realizing accurate identification of potential risks of the equipment and scientific prediction of future failure trends; In terms of information interaction, the application establishes an encrypted channel between the mobile terminal and the background system, pushes adaptive equipment state information according to personnel permissions, and optimizes the data transmission path through edge computing to ensure the real-time and accuracy of the information, in addition, the closed-loop feedback mechanism fuses the predictive maintenance recommendations with the real-time interactive content to generate guidance information for on-site operations, thereby further improving the operation and maintenance efficiency and accuracy; The application not only improves the intelligent level of power equipment management, but also ensures the reliability of the data and the scientificity of the operation and maintenance decisions through dynamic information updating and integrity checking, and through the application, the operation and maintenance management of the power industry will be more efficient, safe and accurate, thereby providing a strong guarantee for the stable operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0007] Fig. 1 A flowchart of the NFC information interaction and management method of the power equipment intelligent signboard of the application; Fig. 2 A flowchart of step S105 in embodiment 1; Fig. 3A flowchart for step S106 in Example 1. DETAILED DESCRIPTION

[0008] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be described in detail below with reference to the drawings and specific embodiments; As Figs. 1-3 The NFC information interaction and management method of the power equipment intelligent signboard in this embodiment can specifically include: Step S101, through the operation state data and historical record data collected by the field device sensor, the collected data is distributed stored and verified by using the blockchain encryption mechanism, and the device information data set after security authentication is obtained.

[0009] The operation state data and historical record data are collected by the field device sensor, and the collected data is preliminarily hashed by using the blockchain encryption mechanism, and the encrypted data block is obtained; According to the encrypted data block, consensus verification is performed in the distributed storage node, and the consensus verification compares the node hash value by using the proof of work mechanism. If the verification is passed, the encrypted data block is linked to the main chain of the blockchain, and the verification data chain is obtained; Obtain the node information in the verification data chain, and detect the abnormality of the device operation state. If an abnormality is detected, generate an alarm label by fusing the historical record data, and obtain the labeled data chain; Through the labeled data chain, integrate the security authentication label and the maintenance prediction extension attribute, calculate the authentication hash value by using the SHA-256 algorithm, the input of the SHA-256 algorithm is the integrated byte sequence in the labeled data chain, and the output is a fixed length hash value, and the security authentication data set is determined; Extract the device information fusion attribute from the security authentication data set, generate an extension maintenance prediction report, and obtain the device information data set after security authentication; Specifically, in one embodiment, the operation state data and historical record data are collected by the field device sensor. First, a plurality of sensors need to be deployed in the industrial equipment field, such as temperature sensors, vibration sensors and pressure sensors. These sensors monitor the running parameters of the equipment in real time, such as speed, temperature change and load condition. At the same time, the historical record data can be extracted from the device log system, including the past maintenance record and fault event data. This collection process ensures that the data covers the current state and historical performance of the equipment, providing basic input for subsequent processing. In this way, continuous data collection can be realized on the industrial production line to avoid data loss. Specifically, the operation state data collection involves the integration of sensors and device control systems. For example, on an industrial pump, a vibration sensor collects vibration amplitude data once per second, while historical data is retrieved from the database from the past year's operating logs; this integration helps capture dynamic changes in the equipment and ensures the timeliness and accuracy of the data. It should be noted that the data collected by the sensor needs to be standardized, such as using JSON format, to facilitate subsequent transmission and processing; In one possible implementation, if the device is under high load, the sensor can adjust its sampling frequency to adapt to changes, thereby improving the representativeness of the data. Furthermore, a core innovation is the use of a blockchain encryption mechanism for distributed storage and verification of the collected data. This blockchain encryption mechanism utilizes blockchain's distributed ledger technology combined with encryption algorithms to securely process the data. The specific process includes first hashing the collected data to generate a unique identifier, and then encrypting the data content using asymmetric encryption algorithms such as RSA. Distributed storage means that the data is not stored on a single server but distributed across multiple nodes, with each node maintaining a copy of the ledger. The verification process relies on consensus mechanisms, such as proof-of-work or proof-of-stake, to ensure that all nodes reach a consensus on the consistency of the data. If the data is tampered with, the hash value will not match, thus detecting the anomaly. This mechanism is particularly useful in industrial equipment monitoring because it prevents data forgery and ensures the authenticity of equipment information. In one embodiment, sensor data from a generator set is encrypted and uploaded to a blockchain network, where multiple factory nodes participate in the verification. Once the verification is successful, the data is marked as trustworthy. For example, during the verification process, if data inconsistency is detected, the system will trigger a retransmission mechanism to re-collect and encrypt the data from the sensor. This distributed verification not only improves security but also reduces the risk of single point of failure. Through the blockchain, each data block is linked to the previous block, forming an irreversible chain to ensure the integrity of historical data. Preferably, smart contracts can be introduced to automatically execute verification rules, such as setting thresholds to check for data anomalies. Once the threshold is exceeded, the contract automatically refuses to store the data. In another implementation, for different industrial equipment such as compressors, the collected data can be uploaded to the blockchain in batches. First, the data is initially encrypted, and then distributed nodes verify the integrity of the batches. This scenario demonstrates the versatility of the technology and is applicable to various equipment monitoring needs, rather than being limited to a specific type. Understandably, the above process yields a securely certified equipment information dataset. This dataset includes verified operational status data and historical data, forming a structured collection, such as database entries with equipment IDs as keys. In industrial applications, this dataset can be used for predictive maintenance, such as analyzing vibration data trends to identify potential failure risks in advance, thereby extending equipment lifespan and improving production efficiency. Furthermore... In one embodiment, the output format of the dataset can be an encrypted file, accessible to authorized users, to ensure privacy; this approach supports the technical solution of the claims and provides a secure and reliable data foundation. Step S102: Based on the obtained equipment information dataset, the random forest algorithm is used to classify and detect anomalies in the data, determine whether there are potential risk characteristics, and obtain the risk assessment results after classification.

[0010] Using the device information set, the random forest method is used to classify the data and obtain classification labels; Based on the classification labels, the threshold in the anomaly detection method is obtained, potential risk characteristics are determined, and an anomaly set is obtained. For the set of outliers, relevant indicators are extracted from the risk assessment method to determine the risk level and obtain preliminary assessment results; Using the preliminary assessment results, the model parameters were adjusted through cross-validation to obtain the classified risk assessment results; Specifically, in one implementation, the process of acquiring the device information dataset first involves collecting relevant data from the monitoring system; Specifically, this data includes equipment operating parameters such as temperature, voltage, and network traffic; these are collected in real time through sensors or loggers to ensure that the dataset covers both normal and abnormal states of the equipment. For example, in the field of industrial equipment monitoring, the dataset can be derived from machines and equipment on the production line, containing historical operating records to support subsequent analysis. Further, a random forest algorithm is used to classify the data. This algorithm achieves ensemble learning by constructing multiple decision trees, each trained on a random subset of the dataset. The specific process includes splitting the data into training and test sets, then generating bootstrap samples for tree construction. In the classification stage, each decision tree votes on the input data and outputs the majority class label. For example, in equipment risk assessment, data is divided into normal operation category and potential failure category, and the equipment status is determined by algorithm; It should be noted that the core of the random forest algorithm lies in its random feature selection mechanism. When constructing each decision tree, a subset is randomly selected from all features for node splitting, which helps to reduce overfitting and improve generalization ability. In the application of equipment information datasets, for example, when selecting temperature and voltage as key features, the algorithm calculates the Gini impurity of these features to determine the split point, thereby classifying high-risk data points. In one possible implementation, when performing anomaly detection, the random forest can be extended into an isolated forest variant to identify outliers in the dataset. Specifically, the isolated forest constructs isolation trees by randomly partitioning the data space, and outliers have shorter path lengths because they are easy to isolate; for device information, an anomaly score is calculated for each data point, and if the score exceeds a preset threshold, it is determined to be an anomaly. For example, in network device monitoring scenarios, anomaly detection can identify sudden traffic spikes, indicating potential intrusion risks; Preferably, determining whether potential risk features exist involves analyzing the feature importance of the random forest model. The specific process includes calculating the contribution of each feature to the model's prediction and evaluating it using a permutation importance method: randomly shuffling the value of a feature and observing the degree of decrease in model accuracy; the greater the decrease, the higher the importance. In the equipment dataset, for example, if the voltage fluctuation feature is highly important, it indicates that it is a risk indicator. Through this step, potential risk features such as abnormal temperature patterns are extracted to support risk assessment. For example, in an embodiment of industrial equipment monitoring, the dataset is first preprocessed to remove noise, and then a random forest model is trained. The model input is a multi-dimensional feature vector, including device ID, timestamp, and operating metrics. The training process is divided into several stages: when generating the initial tree, 100 decision trees are created using random subsampling; subsequently, the tree depth parameter is adjusted through cross-validation to ensure that the classification accuracy meets the requirements. This embodiment demonstrates the robustness of the algorithm when processing large-scale datasets, effectively distinguishing between normal and fault modes; furthermore, a risk assessment is obtained based on the classification results. Specifically, the model outputs the category probability for each data point, for example, the probability of normal is 0.8 and the probability of risk is 0.2; then, it judges based on a probability threshold such as 0.5. If the risk probability is higher than the threshold, it is marked as high risk. In the field of equipment monitoring, this assessment result can be used to generate alarm reports to help maintenance personnel intervene in a timely manner. Understandably, in another embodiment, for the information dataset of mobile devices, anomaly detection focuses on location and battery consumption data; the random forest algorithm first extracts features such as location change rate, and then constructs a tree ensemble for classification; anomaly judgment is made by calculating the isolation path length of data points, and if the path is shorter than the average, it is regarded as a potential risk feature, such as the risk of device theft; this embodiment expands the applicability of the technical solution in portable device scenarios; Specifically, risk assessment results can also be obtained by combining visual output; For example, a risk heatmap is generated to show the risk distribution of equipment groups; the overall risk index, such as the average anomaly score, is calculated using the prediction results of random forests; this process ensures the objectivity of the assessment and provides a basis for decision-making in equipment monitoring. In one embodiment, the entire process from data acquisition to result output is logically coherent: after acquisition, feature engineering is performed, then the model is trained and anomalies are detected, and finally an evaluation report is output; this method achieves efficient risk identification in the field of equipment safety and can reduce the probability of failure. Step S103: Obtain the classified risk assessment results, establish an encrypted channel with the backend system through the mobile terminal interface in the inspection scenario, determine the real-time pushed equipment status information, and obtain the interactive content with appropriate personnel permissions.

[0011] An encrypted channel is established between the mobile terminal interface and the back-end system. The classified risk assessment results are obtained from the encrypted channel to determine the equipment status information in the inspection scenario. Based on the device status information, data is transmitted to the mobile terminal using a real-time push mechanism to obtain preliminary interactive content adapted to the user's permissions; If the initial interaction involves an anomaly alert, the priority level of the anomaly alert is determined by comparing the frequency and severity of anomalies in historical data records through the permission verification process. Obtain the priority level, generate an extended alarm notification containing processing suggestions for the inspection scenario, and obtain the final interactive content adapted to the personnel's permissions; Specifically, in one implementation, the risk assessment process in the inspection scenario first involves obtaining the classified risk assessment results; Specifically, this result was obtained by classifying and processing the inspection data; For example, in the inspection of power equipment, the system collects equipment operation data such as temperature and vibration, and then applies a classification algorithm to divide these data into low-risk, medium-risk and high-risk categories. It should be noted that the classification criteria include preset thresholds and comparison with historical data to ensure the accuracy of the assessment. This approach enables inspection personnel to identify potential problems in a timely manner and achieve proactive equipment maintenance. Furthermore, based on the above risk assessment results, establishing an encrypted channel with the back-end system through the mobile terminal interface becomes a key step in the inspection scenario. For example, mobile terminals such as smartphones or tablets are equipped with a dedicated interface that uses the TLS protocol to initialize the connection process. In the specific implementation, the mobile terminal first sends a connection request, the background system verifies the terminal's identity and generates a session key, and then exchanges the key through asymmetric encryption to establish a channel. In one embodiment, the channel ensures the confidentiality and integrity of data transmission. For example, at the inspection site, personnel scan the device's QR code to trigger a connection, and the backend system responds in real time to avoid the risk of data leakage. This encryption mechanism is particularly important in inspection operations because it prevents external interference, ensures secure information transmission, and thus supports continuous device monitoring. Preferably, after establishing an encrypted channel, the system determines the real-time pushed device status information; In one possible implementation, the device status information includes current operating parameters and abnormal alarms, and the push logic is based on the classification of risk assessment results; For example, if the assessment results show a high risk, the system immediately pushes detailed status information, such as the device load exceeding the threshold, to the mobile terminal; this determination process is achieved through the real-time monitoring module of the back-end server, which periodically queries the device sensor data and matches it with the assessment results to determine the content to be pushed. It is understandable that obtaining the appropriate user's permissions for the interactive content is a subsequent step, and this content is dynamically generated based on the user's role; Specifically, the system queries the personnel permission database. For example, inspectors are allowed to view basic status, while administrators have permissions that extend to historical data analysis. Interactive content, such as graphical interfaces or reports, is pushed through an encrypted channel to ensure that only authorized information is displayed. In one embodiment, the classification process is further elaborated to obtain the risk assessment results. The classified risk assessment results are derived from multi-dimensional data analysis. For example, in industrial equipment inspection, the system first collects raw data including acoustic signals and visual images, and then extracts features such as frequency distribution and color changes. These features are input into the classification model, and the model generates risk levels by comparing them with standard templates. It should be noted that this model is based on machine learning training, and the training data comes from historical inspection records to ensure the reliability of the classification. This method improves the assessment accuracy and reduces human error in inspection operations. For example, when equipment vibration is abnormal, the system automatically classifies it as medium risk and records the assessment basis to provide data support for subsequent maintenance. Through this detailed classification, inspection efficiency is optimized, and the business process from data collection to result output is ensured to be continuous. For example, the process of establishing an encrypted channel can be extended to different inspection sub-scenarios. In high-voltage line inspection, the mobile terminal interface supports GPS positioning integration, and location verification is incorporated during channel establishment to enhance security. Specifically, the terminal sends encrypted location data, the backend system decrypts it to confirm the legitimacy of the location, and then completes the channel setup. This approach not only protects data but also adapts to the mobility requirements of inspections. Furthermore, the determination of real-time push equipment status information can be combined with a timestamp mechanism, such as including the update time of the equipment status in the push content, to ensure that personnel receive the latest information. In inspection operations, this helps to respond quickly to emergencies. In one embodiment, the generation of interactive content adapted to user permissions involves role-based access control, such as predefined system permission rules that filter sensitive data when generating content; this adaptation improves the secure distribution of information. Preferably, the integration of the entire process in the inspection system reflects its versatility. For example, in substation inspection, the process from risk assessment to interactive content is seamlessly connected, supporting multi-person collaboration. Through these steps, the system achieves efficient equipment management. In one possible implementation, the principles and business processes of high-difficulty topics such as encrypted channels are explained in detail. The establishment of encrypted channels is based on key exchange protocols, such as the Diffie-Hellman method. However, in simplified applications, the terminal generates a public key and sends it to the backend. The backend responds by generating a shared key for symmetric encryption. During the business process, at the inspection site, after personnel log in to the mobile terminal, the interface automatically detects the network environment. If it is a public Wi-Fi, the encryption layer is strengthened to prevent eavesdropping. Specifically, the channel establishment is divided into initialization, verification, and maintenance phases: during initialization, certificates are exchanged to verify identity; during the verification phase, digital signatures are checked; and during the maintenance phase, keys are periodically refreshed. This detailed process ensures that the channel operates stably in dynamic inspection environments, such as real-time transmission of equipment status data like current fluctuations without tampering, thereby achieving uninterrupted monitoring in business operations and reducing losses caused by security risks. The flexibility of this mechanism is reflected in the adjustable encryption strength, which can be dynamically configured according to the inspection risk level, enhancing the system's adaptability. In step S104, if the data transmission delay exceeds a preset threshold, the edge computing module is used to perform a preliminary analysis of the local data to determine the cause of the delay and optimize the transmission path to obtain a low-latency synchronous data stream.

[0012] By analyzing the interactive content, the data transmission delay is obtained, and if the delay exceeds a preset threshold, a delay exceeding the limit signal is obtained. For the aforementioned delay exceeding the standard signal, an edge computing module is used to obtain preliminary analysis results from local data to determine the cause of the delay. The preliminary analysis results are obtained by checking the local network load and device response time. Based on the cause of the delay, the transmission path parameters are obtained, the degree of path congestion is determined, and an optimized path scheme is obtained, wherein the transmission path parameters are extracted from the current routing table and bandwidth utilization. By using the optimized path scheme, a synchronous data stream is obtained, low-latency transmission is determined, and a real-time interactive response is achieved. Specifically, in one implementation, for the obtained interactive content, data transmission latency is first detected by monitoring network transmission parameters; Specifically, the system collects the sending and receiving timestamps of data packets in real time and calculates the difference between the actual transmission time and the expected time. If this difference exceeds a preset threshold, such as 50 milliseconds, a subsequent optimization mechanism is triggered. This detection process ensures the real-time nature of interactive content, such as avoiding screen lag in online video conferencing that affects user experience. In this way, the system can promptly identify potential problems, providing a basis for further analysis. Furthermore, when a delay exceeding the threshold is detected, an edge computing module is used to perform preliminary analysis of local data. An edge computing module refers to a computing unit deployed on user devices or network edge nodes that can process data without relying on a central server. For example, in a real-time data sharing scenario, this module analyzes locally cached interactive content, such as audio and video frames, and extracts key metrics including network bandwidth utilization and packet loss rate. This preliminary analysis helps to quickly locate problems without uploading all data to the cloud, thereby reducing additional latency. It should be noted that the determination of the cause of the delay is achieved through logical reasoning of the analysis results by the edge computing module; Specifically, the module compares local data with historical transmission records. For example, if the packet loss rate is higher than normal, it is determined to be caused by network congestion; if the bandwidth utilization is low but the latency is high, it may be due to an inefficient routing path. Through this multi-dimensional analysis, the system can accurately identify the cause, such as determining whether the latency in online game interaction is due to the server being too far away. This judgment process involves statistical modeling of local data, but does not introduce complex calculations. It is based only on simple threshold comparison and pattern matching to ensure efficient execution. Preferably, after determining the cause of the delay, optimizing the transmission path becomes a key step; In one embodiment, the edge computing module selects an alternative route based on the judgment result, such as switching to a low-load network node or using a multipath transmission protocol. The specific process includes evaluating the latency indicators of the available paths and dynamically adjusting the data flow to a better path. In video conferencing services, this may mean switching from the original path to a direct connection path to the edge server, thereby reducing end-to-end latency. Through this optimization, the system achieves adaptive path adjustment and improves overall transmission efficiency. For example, in another possible implementation, for obtaining a synchronous data stream, the system integrates the above steps to generate a low-latency stream; Specifically, the optimized path will synchronize local analysis results with remote data in real time, ensuring that interactive content such as real-time chat messages is transmitted without delay. Understandably, this approach is applicable in multiple scenarios within the same field. For example, in online education platforms, teacher-student interaction data is kept synchronized through edge optimization to avoid teaching interruptions. In one embodiment, the edge computing module is further extended to multi-device interaction scenarios, where it can coordinate multiple local nodes to perform joint analysis. For example, when multiple users participate in a group video, if any node detects a delay, the module will share the analysis data, collectively determine the cause, and optimize the sharing path. This collaborative approach enhances the robustness of the system and can bring a more stable data flow experience to the business, such as reducing synchronization deviations in group meetings. Specifically, the effect of the above process is that, through local processing of edge computing, the system can obtain low-latency synchronous data streams without increasing network burden; in real-time monitoring applications, this means that interactive content such as sensor data can be synchronized in a timely manner to meet high real-time requirements without relying on remote computing resources. Step S105: Extract on-site problem feedback data from the obtained synchronous data stream, and use the support vector machine algorithm to perform pattern recognition and priority sorting on the feedback data to determine a list of high-priority problems.

[0013] The system obtains on-site problem feedback data from the synchronous data stream, maps the feedback data to a high-dimensional vector space using the support vector machine algorithm, calculates the decision boundary, performs pattern recognition on the feedback data, and obtains the classification pattern of the feedback data. Based on the obtained classification pattern, the feedback data is prioritized and a list of high-priority issues is determined. Obtain the problem items from the high-priority problem list, determine the timeliness of the problem items by associating them with the timestamps in the on-site feedback data, and update the high-priority problem list if the timeliness exceeds a preset threshold. Relevant feedback data is extracted from the updated list of high-priority issues, and clustering methods are used to group the relevant feedback data according to their similarity to obtain a set of issue groups. For the obtained problem group set, the newly added feedback data in the synchronous data stream is integrated to determine the final high-priority problem list; Specifically, in one implementation, extracting on-site problem feedback data from the obtained synchronous data stream can be achieved through a real-time data parsing module; Specifically, the module first receives synchronous data streams from field devices, which typically contain feedback information in the form of text, voice, or images, such as a problem description submitted by a user through a mobile application in a customer support scenario. It should be noted that synchronous data streams refer to data continuously transmitted during real-time communication to ensure the timeliness of feedback. The extraction process includes filtering irrelevant noise, such as removing duplicate entries or invalid characters, and separating core feedback data through rule-based preprocessing steps, such as identifying keywords like "fault" or "delay" to locate the problem content. This extraction method helps to quickly capture on-site problems in the business process, avoid data loss, and thus provide a reliable foundation for subsequent analysis. In one possible implementation, if the data stream involves multiple input sources, such as combining sensor data, a timestamp synchronization mechanism can be used to ensure that the extracted feedback data corresponds to the on-site events. Furthermore, pattern recognition of the feedback data through the support vector machine algorithm is the core step of this solution. Support vector machine is a supervised learning algorithm used for classification and regression tasks, and here it is applied to identify patterns in the feedback data, such as distinguishing problem types like technical faults or user inquiries. For example, the first step is to construct feature vectors by extracting key features from the feedback data, such as word frequency statistics or sentiment polarity values. These feature vectors represent a quantitative description of the feedback. The principle of the algorithm is to find a hyperplane that separates data points of different patterns to maximize the interval, thereby achieving accurate identification. In business scenarios of on-site problem feedback, such as when customer service centers handle user complaints, support vector machines can train a model using historical feedback data as a training set. Through iterative optimization, the parameters are adjusted so that the model can identify recurring patterns such as feedback related to "network interruption". Specifically, the training process includes data labeling, feature selection, and cross-validation. For example, the labeling feedback is "high-frequency problem" or "isolated event." Then, kernel functions are used to handle non-linear patterns to ensure the model is robust in complex business environments; preferably; In one embodiment, a radial basis function kernel can be introduced to improve recognition accuracy. This approach can effectively distinguish similar but different patterns when processing large amounts of feedback data, thereby providing accurate input for priority ranking. This detailed pattern recognition process not only improves the automation level of feedback processing, but also reduces delays caused by manual intervention in business operations. Based on the above pattern recognition, priority ranking is further performed to determine a list of high-priority issues. In one embodiment, the sorting process uses the confidence score output by the support vector machine to quantitatively evaluate the identified patterns, for example, marking patterns with a confidence score higher than a threshold as high priority; Specifically, priority calculation can be weighted and sorted based on business impact factors, such as the frequency of problem occurrence or the degree of potential loss. For example, in a field service scenario, network problems affecting multiple users will be placed at the top of the list. It should be noted that this sorting is not a simple numerical comparison, but a dynamic adjustment based on pattern recognition results to ensure that the list reflects real-time business needs; in another implementation, the entire process can be applied to the feedback processing of online education platforms. For example, feedback can be extracted from the data stream of classroom questions submitted synchronously by students, and patterns such as "difficulty in understanding content" or "technical connectivity issues" can be identified through support vector machines. Then, a high-priority list can be generated for teachers to respond quickly. This scenario demonstrates the versatility of the solution, covering different sub-applications within the same domain; further expansion is possible. In one possible implementation, if the feedback data contains multimodal information, such as text combined with images, the extraction step can be extended to a fusion process, for example, separating the modalities first and then extracting features uniformly, with the support vector machine adjusting the input dimensions accordingly; this flexibility enhances the applicability of the solution in complex business environments. For example, the effect of priority sorting is that it enables dynamic updates to the problem list. For instance, when a new feedback pattern is identified as having a high impact, the list is automatically rearranged, resulting in more efficient resource allocation in the business. In one embodiment, the pattern recognition of the support vector machine algorithm can be combined with an online learning mechanism, which allows the model to update its parameters in real time when processing new data streams. For example, in a customer service system, the recognition accuracy can be gradually optimized when encountering new feedback patterns. This mechanism ensures long-term business stability. Understandably, the above extraction, identification, and sorting steps form a closed loop, providing an integrated solution from data input to prioritized output in the field of on-site problem feedback. Step S106: Based on the determined list of high-priority issues, a deep neural network algorithm is used to predict and model the list data to determine future equipment failure trends and obtain a predictive maintenance suggestion dataset.

[0014] Historical fault records corresponding to high-priority issues are obtained through list data collection. The historical fault records are used as input to train a deep neural network, wherein the deep neural network processes the input through multiple hidden layers to obtain a preliminary trend model for equipment status monitoring. Fault trend analysis features are extracted from the preliminary trend model to identify potential future fault points; By combining the potential future failure points with risk assessment indicators obtained from the failure trend analysis, a maintenance recommendation generation sequence is determined, and a predictive maintenance recommendation dataset is obtained. If the sequence exceeds a preset threshold, the model parameters are adjusted to correct the dataset, wherein the model parameters include the learning rate and the number of hidden layer nodes; By integrating trend judgment criteria into the revised dataset, the final predictive maintenance suggestion dataset is obtained. Specifically, in one implementation, it is first necessary to obtain device operating data to determine a list of high-priority issues; Specifically, historical fault records, such as parameters like temperature, vibration, and current, are collected from sensor data of industrial equipment. These data are then prioritized based on criteria like fault frequency and impact severity to create a high-priority problem list. This list includes potential equipment fault types, such as bearing wear or motor overheating, ensuring subsequent modeling focuses on key issues. Furthermore, based on this high-priority problem list, a deep neural network algorithm is used for predictive modeling. A deep neural network is a multi-layered artificial neural network structure used to process complex data patterns. In this embodiment, the network includes an input layer, multiple hidden layers, and an output layer. The input layer receives data from the problem list, such as historical fault feature vectors. These vectors consist of numerical indicators, such as vibration amplitude values ​​and occurrence timestamps. Hidden layers process the data using activation functions like ReLU to learn nonlinear relationships. Specifically, the input data is first normalized to ensure a uniform numerical range. Then, the outputs of each layer are calculated through forward propagation, and the weights are adjusted through backpropagation to minimize prediction errors. Through multiple iterations of training, the model can capture potential patterns of equipment failures, such as identifying the correlation between abnormal vibrations and bearing failures. The key to this modeling process lies in the depth design of the network, typically with 3 to 5 hidden layers. The number of nodes in each layer is adjusted according to the data size, such as 128 nodes for a medium-sized dataset, thereby achieving accurate prediction of future failures. It should be noted that when training a deep neural network, the data is divided into a training set and a validation set; For example, 80% of historical data is used for training, and the remainder is used to validate model performance; this division helps to avoid overfitting and ensure the model's generalization ability. In one possible implementation, an optimization algorithm such as Adam is used to update network parameters with a learning rate set to 0.001 to gradually converge the loss function. In this way, the model extracts patterns from a list of high-priority issues and constructs a framework for predicting future states. Based on the modeling results, the future equipment failure trend is further determined. Specifically, the model outputs a probability distribution, representing the likelihood of a specific failure occurring within a future time window; For example, for an industrial pump, the model might predict that the bearing failure rate will rise to 60% over the next 30 days. The judgment process involves setting a threshold; for example, a probability exceeding 50% is considered a high-risk trend. This trend analysis is achieved through time series processing, which combines historical data with predicted values ​​to form a continuous failure curve, thereby providing a basis for maintenance decisions. Preferably, after determining the trend, a predictive maintenance suggestion dataset is generated. This dataset includes suggested actions, such as "replacing bearings" or "increasing lubrication checks," and is associated with schedules and priorities. By integrating the model output, a structured dataset is formed, for example, stored in tabular form, with each row corresponding to a problem item and columns including fault type, predicted probability, and maintenance action. This dataset supports the daily management of industrial equipment and improves reliability. In another implementation, the same process is applied to different industrial equipment scenarios, such as compressor maintenance. First, a list of high-priority problems is determined, such as abnormal air pressure. Then, a deep neural network is used to model and predict trends to generate maintenance suggestions. This versatility is reflected in the adaptability of the algorithm, which can handle data from various equipment without major modifications. For example, in the maintenance of a conveyor belt system, a high-priority issue list focuses on the risk of belt breakage; a deep neural network learns from vibration and tension data to predict breakage trends, and if the trend indicates an increasing risk, it suggests that the dataset include "check the tension regulator immediately"; this implementation demonstrates the flexible application of the technology in the same field; further extended, a dropout mechanism can be introduced into the model training to enhance robustness; Specifically, some nodes are randomly dropped in the hidden layer, for example, by 0.2. This helps prevent overfitting and ensures the stability of prediction accuracy under noisy data. Through this optimization, the reliability of judging fault trends is improved, and the final maintenance suggestion dataset is more practical. Understandably, this technical solution can achieve early warning of equipment failure through predictive modeling of deep neural networks. In industrial production environments, this helps to reduce downtime and increase equipment lifespan. For example, in continuously operating factories, trend-based maintenance recommendations can significantly reduce the failure rate. In one embodiment, for multi-device systems, such as multiple machines on a production line, the problem list can be aggregated and processed; the deep neural network is then extended to a multi-input model, processing data from each device simultaneously, judging the overall trend, and generating a comprehensive maintenance suggestion dataset; this approach demonstrates the scalability of the solution and is suitable for large-scale industrial scenarios. Step S107: By integrating the obtained predictive maintenance suggestion dataset with real-time interactive content, a closed-loop feedback instruction is generated in the background system to obtain updated guidance information for on-site operations.

[0015] By combining the predictive maintenance suggestions with the real-time interactive content, data fusion processing is used to match and associate the historical data points in the predictive maintenance suggestions with the current status indicators in the real-time interactive content to obtain a fused dataset and determine the abnormal patterns in the fused dataset. Based on the fused dataset and the anomaly pattern, an initial template for the closed-loop feedback instruction is obtained. The closed-loop feedback instruction is generated in the background system. The relevant adjustment parameters of the anomaly pattern are filled in through the template to obtain an optimized version of the instruction. If the optimized version of the instruction meets the preset threshold, the on-site operation guidance is adjusted by replacing the original items of the on-site operation guidance with parameters in the optimized version of the instruction, and the completeness of the adjusted guidance content is judged. By combining the adjusted guidance content and the completeness, along with the information update mechanism and the dataset acquisition method, an update sequence is generated. The consistency of each step of the sequence is verified by the completeness, and the sequence verification result is obtained. Based on the sequence verification results, the interaction content analysis and feedback instruction optimization are integrated to output the final operation guidance update. By integrating and analyzing the results and optimizing the elements, the updated guidance information for on-site operations is determined. Specifically, in one implementation, the process of fusing the obtained predictive maintenance suggestion dataset with real-time interactive content first requires understanding the concept of a predictive maintenance suggestion dataset. This dataset typically originates from historical equipment operating data and the output of predictive models. For example, in the field of wind power equipment maintenance, the dataset contains predicted values ​​for equipment vibration, temperature, and wear, which are collected by sensors and analyzed by machine learning models. Real-time interactive content refers to the immediate feedback input by on-site operators, such as the current operating status of the equipment or abnormal observations. Fusing this content aims to improve the accuracy of maintenance decisions. Specifically, fusion can be achieved through a weighted average algorithm, which combines the numerical values ​​of the predicted dataset with the qualitative descriptions of the real-time content to form a comprehensive evaluation index; furthermore, when the background system generates a closed-loop feedback instruction, the system will perform logical judgments based on the fused data. For example, if the fusion results show that the equipment vibration exceeds the threshold, the system generates instructions such as "stop operation immediately and check the bearings". The generation process of the closed-loop feedback instructions involves a feedback loop mechanism: first, the system receives the fused data; second, it applies a rule engine to analyze potential risks; finally, it outputs instructions and records the execution results for use in the next iteration. This closed loop ensures that the instructions are continuously optimized and avoids the bias of a single prediction. In the scenario of wind power tower maintenance, this process can be applied to blade inspection, integrating historical wind load predictions with on-site wind speed interaction to generate instructions to adjust the timing of maintenance. Preferably, the updated guidance information for on-site operations is obtained through real-time push of instructions; the back-end system converts the generated instructions into actionable guidance, such as text notifications or visual interface updates. It should be noted that the key to this update lies in timeliness; the system uses message queuing technology to ensure that instructions reach the field equipment within seconds; for example; In one possible implementation, when fused data indicates that the device is overloaded, the system updates the guidance information to "reduce the speed to 80% and monitor the temperature", thus forming a complete chain from prediction to execution; For example, in the routine maintenance of wind turbine generators, the versatility of this technical solution is reflected in its application in different sub-scenarios; For example, for generator components, the fused dataset might include rotor speed prediction, while real-time interaction provides the operator with noise observations; the system generates feedback instructions such as "increase lubrication check frequency" and updates guidance information to guide specific steps; this approach supports adjustments to various parameters, but is always limited to the field of wind power equipment. Understandably, the technical effect of this fusion and feedback process is to improve maintenance efficiency and reduce equipment failure rate through a closed-loop mechanism; in practical applications, the system can process parallel data from multiple devices to ensure the synchronization of guidance information updates. Specifically, regarding the details of fusion, the construction of the predictive maintenance recommendation dataset involves data cleaning and feature extraction steps; For example, noise data is cleaned and removed, and feature extraction calculates indicators such as average vibration amplitude. These are then fused with real-time interactive content, such as descriptions of "the device making abnormal noises," to generate vector representations for subsequent instruction generation. In one embodiment, the rule engine of the backend system can be configured as a multi-layer structure, with the first layer assessing the risk level and the second layer matching preset instruction templates to generate closed-loop feedback. This design enhances the system's flexibility and is suitable for the maintenance needs of wind farms of different sizes. Furthermore, the output form of guidance information updates may include mobile alerts to ensure that on-site operators respond to the fusion results in a timely manner. For example, in a blade maintenance scenario, if a potential crack is detected after fusion, the system generates the instruction "Deploy UAV for inspection" and updates the guidance to a detailed inspection path diagram; In one embodiment, the technical objective of this process is to achieve seamless integration of prediction and real-time data, thereby reducing downtime during wind power equipment maintenance and providing reliable operational support. Step S108: Extract verification parameters from the obtained guidance information update, perform integrity verification on the parameters using a hash function, determine the reliability of the updated data, and obtain the final confirmed closed-loop management information.

[0016] The verification parameters are extracted from the updated guidance information to obtain the verification parameters; the hash value of the verification parameters is calculated using a hash function and compared with a preset hash value to perform integrity verification on the verification parameters and determine the reliability of the updated data; If the reliability judgment passes, the closed-loop management information is determined; for the closed-loop management information, an extended audit log record is obtained, wherein the audit log record contains historical tracking of the reliability of updated data, and the audit log record is obtained. Based on the audit log records, the fusion parameter extraction application and hash function verification are performed, wherein the fusion is achieved by repeatedly applying the integrity verification to the audit log records to obtain the finally confirmed closed-loop management information; Specifically, in one implementation, the process of extracting verification parameters from the obtained guidance information update first requires identifying the structure of the update data; Specifically, guidance information updates typically include key elements such as version number, timestamp, and signature, which are extracted as verification parameters; For example, in the field of data management, when a system receives guidance information for a software update, it parses the data packet and extracts the embedded verification parameters, such as digital signatures and checksum values. This extraction process is implemented through a standard data parsing protocol, ensuring the parameters are complete and error-free. In this way, the system can initially screen out the parameters for subsequent verification, thus laying the foundation for judging data reliability. Furthermore, the core step is to perform integrity verification on the parameters using a hash function. A hash function is an algorithm that maps data of arbitrary length to a fixed-length value; here, it is used to detect whether the parameters have been tampered with. For example; In one possible implementation, the system first selects a hash algorithm such as SHA-256 to calculate a unique hash value for the extracted parameters. Then, it compares this calculated hash value with a pre-stored hash value in the updated data. If they match, it indicates that the parameters have not been modified; otherwise, it indicates a potential risk of tampering. In detail, this process includes inputting the parameter data into a hash function, which internally generates a digest value through multiple iterative operations, such as converting the parameter string to binary form and performing bitwise and modulo operations, ultimately outputting a 256-bit hash code. In data management scenarios, this verification effectively prevents malicious injection and ensures the authenticity of the updated information. It should be noted that the reliability of the updated data is determined based on the aforementioned verification results; Specifically, if the hashes are consistent, the system will further check other auxiliary conditions, such as the validity period of the timestamp, to comprehensively assess the reliability. For example, in software update management, if the verification passes and the timestamp is within the specified range, the data is considered reliable; otherwise, it is marked as unreliable. This judgment process emphasizes the continuity of the logical condition chain to ensure objective decision-making. Preferably, obtaining the final confirmed closed-loop management information involves integrating the judgment results into a complete management feedback loop; closed-loop management information refers to a self-feedback system from input to output, which here means that after the confirmation information is generated, it is fed back to the update source to achieve loop optimization; Specifically; In one embodiment, after determining reliability, the system generates closed-loop information containing confirmation codes and logs, for example, encoding the reliability status as a digital identifier and transmitting it back to the information provider via the network. Details of this process include information encapsulation steps: first, collecting the judgment results; second, adding a timestamp and signature; and finally, forming a structured data packet. Through this closed-loop mechanism, the system can continuously monitor the quality of updated data, bringing continuous optimization effects to the data management field, such as reducing the incidence of erroneous updates. In another embodiment, the extraction of verification parameters can be extended to multi-source update scenarios. For example, when the system receives multiple guidance information updates simultaneously, it will prioritize extracting shared parameters, such as the general version identifier, and then process the personalized parameters one by one; this extension demonstrates the versatility of the technical solution in complex data environments; and further extends the verification process; In one embodiment, the hash function can be combined with a salt value to enhance security; the salt value is a random string that is added before the parameter is hashed to resist rainbow table attacks; the specific process is to generate a salt value, concatenate the parameter, calculate the hash, and store the salt value for subsequent verification; this method is suitable for high-security scenarios in data management. Understandably, the reliability threshold can be adjusted according to business needs, for example, setting a hash matching rate of 100% as the pass standard; this adjustment enhances the flexibility of the solution. For example, in the generation of closed-loop management information, the system may include an error handling branch. If the judgment is unreliable, an alarm message is generated as part of the closed loop to ensure the comprehensiveness of the management information. In one embodiment, the entire process is integrated into a cloud data management system, with extraction and closed-loop implementation all achieved through API interfaces, demonstrating the modularity of the solution.

[0017] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for NFC information interaction and management of intelligent signboard of power equipment, characterized in that, The method comprises: Collecting the running state data and historical record data of the field device, performing distributed storage and verification on the collected data, and obtaining a device information data set that has passed security authentication; According to the obtained device information data set, performing classification processing and anomaly detection on the data, judging whether there is a potential risk feature, and obtaining a risk assessment result after classification; Obtaining the risk assessment result after classification, establishing an encrypted channel with the background system through a mobile terminal interface in the inspection scene, determining the device state information pushed in real time, and obtaining the interactive content adapted to the personnel authority; For the obtained interactive content, if it is detected that the data transmission delay exceeds a preset threshold, the local data is preliminarily analyzed, the delay reason is judged and the transmission path is optimized, and a low-delay synchronous data stream is obtained; Extracting field problem feedback data from the obtained synchronous data stream, performing pattern recognition and priority sorting on the feedback data, and determining a high-priority problem list; According to the determined high-priority problem list, a prediction model is established for the list data, the future device failure trend is judged, and a predictive maintenance suggestion data set is obtained; Through the obtained predictive maintenance suggestion data set and real-time interactive content fusion, a closed-loop feedback instruction is generated in the background system, and guidance information update for on-site operation is obtained. 2.The NFC information interaction and management method of the power equipment intelligent signboard according to claim 1, characterized in that, Obtaining the risk assessment result after classification comprises: Using a random forest method to classify the data through the device information set, obtaining a classification label; according to the classification label, obtaining a threshold in the anomaly detection method, judging the potential risk feature, and obtaining an anomaly point set; for the anomaly point set, extracting relevant indicators from the risk judgment method to determine the risk level, and obtaining a preliminary evaluation result; using the preliminary evaluation result, adjusting the model parameters through cross-validation method, and obtaining the risk assessment result after classification. 3.The NFC information interaction and management method of the power equipment intelligent signboard of claim 1, wherein, The interactive content adapted to the personnel authority comprises: Through the mobile terminal interface and the background system, an encrypted channel is established, the risk assessment result after classification is obtained from the encrypted channel, and the device state information in the inspection scene is determined; according to the device state information, a real-time push mechanism is used to transmit data to the mobile terminal, and preliminary interactive content adapted to the personnel authority is obtained; if the preliminary interactive content involves an abnormal alarm, the abnormal alarm priority level is judged by comparing the abnormal occurrence frequency and severity index in the historical data record through the permission verification process; the priority level is obtained, an extended alarm notification containing processing suggestions is generated for the inspection scene, and the final interactive content adapted to the personnel authority is obtained.

4. The NFC information interaction and management method of the power equipment intelligent signboard according to claim 1, characterized in that, Obtaining the low-delay synchronous data stream comprises: Through the interactive content, the data transmission delay is obtained, it is judged whether the delay exceeds the preset threshold, and a delay exceeding signal is obtained; for the delay exceeding signal, a preliminary analysis result is obtained from the local data, and the delay reason is determined, wherein the preliminary analysis result is obtained by checking the local network load and device response time; according to the delay reason, the transmission path parameters are obtained, the path congestion degree is judged, and an optimized path scheme is obtained, wherein the transmission path parameters are extracted from the current routing table and bandwidth utilization; through the optimized path scheme, a synchronous data stream is obtained, low-delay transmission is determined, and real-time interactive response is obtained.

5. The NFC information interaction and management method of the power equipment intelligent signboard according to claim 1, characterized in that, The determining of the high-priority problem list comprises: obtaining field problem feedback data from the obtained synchronous data stream, mapping the feedback data to a high-dimensional vector space, calculating a decision boundary for pattern recognition of the feedback data, and obtaining a classification mode of the feedback data; according to the obtained classification mode, performing priority sorting on the feedback data, and determining a high-priority problem list; obtaining a problem item in the high-priority problem list, judging the timeliness of the problem item by associating a time stamp in the field feedback data, and updating the high-priority problem list if the timeliness exceeds a preset threshold; extracting relevant feedback data from the updated high-priority problem list, grouping the relevant feedback data according to similarity, and obtaining a problem grouping set; for the obtained problem grouping set, fusing new feedback data in the synchronous data stream, and determining a final high-priority problem list.

6. The NFC information interaction and management method of the power equipment intelligent signboard according to claim 1, characterized in that, The obtaining of the predictive maintenance recommendation dataset comprises: obtaining historical fault records corresponding to the high-priority problem through list data collection; using the historical fault records as input to train a deep neural network, wherein the deep neural network processes the input through multiple hidden layers to obtain a preliminary trend model of equipment state monitoring; extracting fault trend analysis features from the preliminary trend model to determine future potential failure points; judging a maintenance recommendation generation sequence by combining the future potential failure points with risk assessment indicators obtained from the fault trend analysis basis, and obtaining a predictive maintenance recommendation dataset; if the sequence exceeds a preset threshold, adjusting the model parameters to correct the dataset, wherein the model parameters include learning rate and number of hidden layer nodes; integrating trend judgment basis through the corrected dataset to obtain a final predictive maintenance recommendation dataset.

7. The NFC information interaction and management method of the power equipment intelligent signboard according to claim 1, characterized in that, The obtaining of the guidance information update for the field operation comprises: matching and associating historical data points in the predictive maintenance recommendation with current state indicators in the real-time interactive content by the predictive maintenance recommendation and the real-time interactive content, obtaining a fusion dataset, and determining an abnormal mode in the fusion dataset; obtaining an initial template of the closed-loop feedback instruction according to the fusion dataset and the abnormal mode, generating the closed-loop feedback instruction in the background system, filling abnormal mode related adjustment parameters through the template to obtain an instruction optimization version; if the instruction optimization version meets a preset threshold, adjusting the field operation guidance, replacing original items of the field operation guidance with parameters in the instruction optimization version, and judging the completeness of the adjusted guidance content; generating an update sequence through the adjusted guidance content and the completeness, combining the information update mechanism and the dataset acquisition method, verifying the consistency of each link of the sequence through the completeness, and obtaining a sequence verification result; according to the sequence verification result, fusing the interactive content analysis and the feedback instruction optimization, outputting a final operation guidance update, and determining the guidance information update for the field operation through result integration analysis and optimization elements.

8. The NFC information interaction and management method of the power equipment intelligent signboard according to claim 1, characterized in that, The obtaining of the final confirmed closed-loop management information comprises: extracting verification parameters from the guidance information update to obtain the verification parameters; The hash value of the verification parameter is calculated by a hash function, and compared with a preset hash value to perform integrity check on the verification parameter, and to judge the reliability of the update data; If the reliability judgment passes, the closed-loop management information is determined; For the closed-loop management information, an extended audit log record is obtained, wherein the audit log record contains historical tracking of the reliability of the update data, and the audit log record is obtained; According to the audit log record, a fusion parameter extraction application and a hash function check are fused, wherein the fusion is obtained by repeatedly applying the integrity check to the audit log record to obtain the finally confirmed closed-loop management information.

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