Intelligent equipment management method and equipment management system thereof

Abstract

The invention discloses an intelligent equipment management method and an equipment management system thereof, and relates to the technical field of equipment management.The method comprises the steps that key operation parameters of equipment are collected through multiple types of sensors, and data processing is executed; transmitting the encrypted data to a central node in real time through a communication module, and completing data cleaning and redundant backup; performing training, prediction, diagnosis and result generation on the equipment state by using a decision tree model in an artificial intelligence engine; according to the intelligent analysis result, fault warning, authority verification and control instruction issuing execution are carried out through the terminal module; and collecting execution feedback data, continuously optimizing the decision tree model, and realizing equipment management closed loop and intelligent evolution. The invention provides an intelligent equipment management method and system integrating multi-source sensing, decision tree intelligent analysis, security authentication and multi-level control, real-time monitoring, fault prediction and automatic control of the equipment state are realized, and the intelligence and reliability of equipment management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and particularly relates to an intelligent equipment management method and its equipment management system. Background Art

[0002] With the development of informatization and intelligence, the requirements for real-time performance, intelligence, and security in modern equipment management are constantly increasing. The traditional manual inspection and regular maintenance methods are difficult to meet the management requirements of modern warfare or industrial equipment for rapid response, fault prediction, and remote control. Therefore, the equipment management technology based on sensor data collection, intelligent analysis and decision-making, and remote control has gradually become a research hotspot.

[0003] The existing equipment management systems generally have the following problems: lack of a unified data collection standard, making it difficult to guarantee data accuracy and integrity; data analysis mostly relies on simple threshold judgment or fixed logic, lacking the in-depth understanding and prediction ability of equipment status changes; the instruction issuing and response path do not have an intelligent scheduling mechanism, resulting in response delays and high misjudgment rates in case of sudden failures; the information security protection system is imperfect, making it vulnerable to illegal access or data loss risks. These problems directly restrict the practicability and scalability of intelligent equipment management systems.

[0004] In view of the above problems, the present invention proposes an intelligent equipment management method and its equipment management system, which integrates key technologies such as multi-modal perception, decision tree intelligent analysis, security authentication mechanism, and multi-level terminal interaction, constructs a closed-loop process from data collection, real-time transmission, intelligent judgment, automatic control to model self-learning and optimization, effectively improves the intelligent level and fault prediction ability of the system, and realizes the equipment management goal of high security, high reliability, and high response. Summary of the Invention

[0005] The present invention provides an intelligent equipment management method and its equipment management system for the above problems, so as to solve the problems of low intelligence level, weak prediction ability, poor response efficiency, and insufficient security protection in the prior art for equipment management.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent equipment management method, including the following steps: Step S1, collecting key operation parameters of the equipment through multiple types of sensors and performing data processing; In step S1, the following sub-steps are further included: S1-1, starting the security authentication process, and the authentication unit of the security protection module authenticates the equipment sensors accessing the system; S1-2, obtaining the key parameters of the equipment through the sensors of the perception module, and using the key parameters as the original data, where the key parameters include temperature, pressure, vibration, and current.

[0007] Step S2, the encrypted data is transmitted to the central node in real time through the communication module, and data cleaning and redundant backup are completed; In step S2, the following sub-steps are further included: S2-1, the communication module transmits the sensor data to the central node in real time through the wired network, and the data is encrypted using a security protocol during the transmission process to prevent tampering and leakage; S2-2, after the data is transmitted to the computing and decision-making module, the data management module preprocesses and stores the data. The preprocessing includes data verification, format conversion, and outlier filtering. The cleaned data is written into the database for long-term storage and used for historical trend analysis and model training; The redundant unit of the security protection module synchronously backs up the key data to the standby storage during the storage process to prevent data loss caused by a single point of failure.

[0008] Step S3, use the decision tree model in the artificial intelligence engine to train, predict, diagnose and generate results for the equipment status; In step S3, the following sub-steps are further included: S3-1, based on the historical operation data and fault records, use the decision tree model for fault diagnosis and status prediction. First, calculate the information entropy of the training data set. It is defined that the data set D contains n types of statuses, and the information entropy is specifically defined as shown in the formula:

[0009] where, is the information entropy of the data set D, D is the data set to be divided currently, n is the total number of status categories, is the proportion of samples belonging to the k-th category in the data set, is the logarithmic function with base 2; Use the candidate feature as the node for division. The candidate feature is the key parameter collected by the sensor, and calculate the information gain obtained by each feature for dividing the data set. Assume that the feature A has V possible values , let represent the subset of the data set with the value of on the feature A, and represent the number of samples in the data set and the subset respectively, then the information gain Gain(D,A) of the feature A is specifically shown in the formula:

[0010] where, is the information gain brought by the feature to the data set , is the information entropy of the data set before division, is a candidate partitioning feature, is the number of possible value types of feature A, is the v-th possible value of feature A, is a subset 's information entropy; The greater the information gain, the greater the improvement in purity obtained by using feature A for partitioning. Based on this, during the decision tree training process, the feature with the highest information gain is recursively selected as the partitioning node, continuously generating branches until the stopping condition is met; S3-2. Use the trained decision tree model to perform real-time inference and judgment on newly collected equipment data. The artificial intelligence engine unit calls the fault prediction function of the decision tree model and calculates the data at the current moment uploaded by the sensor one by one according to the decision tree rules. Specifically: Starting from the root node of the decision tree, traverse along the corresponding branches according to the values of each sensing parameter, and output the prediction result when reaching the leaf node.

[0011] Step S4. According to the intelligent analysis result, perform fault warning, permission verification, and control instruction issuance and execution through the terminal module; In step S4, the following sub-steps are also included: S4-1. When an abnormal state is detected in step S3, the system issues a warning through a multi-level control terminal, where the abnormal state includes potential faults, abnormal operation, and equipment anomalies; S4-2. When the decision requires active intervention in the equipment operation, issue a control instruction to the equipment through the terminal interaction module; Before the instruction is issued, the authentication unit of the security protection module verifies the operator's permission and the integrity of the instruction to prevent unauthorized control actions; the instruction is sent to the equipment actuator through the communication network; if the main communication channel fails, the redundant unit automatically switches to the backup communication link to ensure reliable delivery of the instruction; after the equipment receives the instruction, its internal control unit performs the corresponding actions to achieve intelligent management and control of the on-site equipment; S4-3. After execution, the system records the current decision instruction and the equipment response result in the data management module as a basis for future analysis and model optimization; when a fault shutdown occurs, record the fault cause and the sensor reading information at the time of shutdown.

[0012] Step S5. Collect execution feedback data, continuously optimize the decision tree model, and achieve a closed-loop and intelligent evolution of equipment management.

[0013] In step S5, the following sub-steps are also included: S5-1. Collect the actual response and subsequent operation data of the equipment in step S4 through the data management module, mark them, and incorporate them into the historical database; S5-2. Use the updated database through the artificial intelligence engine unit to readjust the decision tree model parameters, and prune the decision tree or add new branch nodes according to the new fault samples.

[0014] An intelligent equipment management system, comprising: A sensing module, a communication module, a computing and decision-making module, a terminal interaction module, a security protection module, and a data management module; The sensing module is the information source of the entire system, consisting of various sensors and data acquisition devices deployed on the equipment, and is used to obtain the operation status data of the equipment. The data collected by it is sent upstream through the communication module, enabling subsequent modules to monitor and analyze the equipment; The communication module serves as the data transmission hub between the modules of the system, providing wired and wireless network communication functions, transmitting the data collected by the sensing module to the computing and decision-making module, and sending the decision control instructions to the terminal interaction module; The computing and decision-making module is the brain of the system, including an artificial intelligence engine unit and a computing and processing unit, and is used to deeply process and make decision inferences on the collected data; the artificial intelligence engine unit has a built-in decision tree intelligent algorithm model, providing functions of fault diagnosis, performance evaluation, and prediction decision-making; The terminal interaction module serves as the interaction interface between people and the system as well as between the system and the actuator, consisting of multi-level control terminals and a man-machine interface, including various terminal devices at the field level and the remote level, presenting the analysis results of the computing and decision-making module to the user in an intuitive manner, and receiving user input and confirmation operations: The security protection module includes an authentication unit and a redundancy unit, providing security and reliability guarantees for each module of the system; the authentication unit is responsible for the identity authentication and access control of the system, ensuring that only authorized devices and users can access the data of the sensing module and issue control instructions, and the redundancy unit provides a backup and fault tolerance mechanism for the key components of the system; The data management module, as an independent functional module, runs through the sensing module, the computing and decision-making module, and the terminal interaction module, assuming the responsibilities of data storage, organization, and service provision, and includes various types of data storage units.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The present invention adopts a security authentication mechanism to authenticate the identity of the access sensors, combines an encrypted communication protocol and a redundancy backup mechanism to build a multi-level security protection system; especially introduces a synchronous backup and disaster tolerance mechanism during the data storage process, solves the problems of easy data tampering and easy data loss, and significantly improves the security and robustness of the system.

[0016] The present invention uses historical operation data to train a decision tree model, supports classification prediction and dynamic diagnosis of equipment status, has stronger learning and generalization capabilities, can identify potential faults in advance and give fault types and confidence levels, effectively extending the service life of equipment and reducing the risk of sudden downtime.

[0017] The present invention not only supports status identification and alarm, but also can link terminal modules to realize automatic control, and feed back the execution results to the data management module, so as to support continuous training and strategy optimization of the model and build a complete data closed loop; this mechanism realizes the full process automation from "monitoring-decision-making-control-feedback", which far exceeds the traditional human-machine intervention response process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It is understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but is only for selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0021] Please refer to Figure 1 , Figure 1 This is a flow chart of an intelligent equipment management method provided by an embodiment of the present invention, comprising the following steps: Step S1, collecting key operating parameters of equipment through multiple types of sensors and performing data processing.

[0022] S1-1. Initiate the security authentication process. The authentication unit of the security protection module conducts identity authentication on the equipment sensors accessing the system.

[0023] S1-2. Obtain the key parameters of the equipment through the sensors of the sensing module and use the key parameters as the original data. The key parameters include temperature, pressure, vibration, and current. The original data is obtained by various types of sensors, such as position sensors, environmental sensors, etc., covering all aspects of the equipment operation state. During the acquisition process, the sensors can combine self-calibration and filtering functions to improve the data quality.

[0024] Step S2. Transmit the encrypted data to the central node in real time through the communication module and complete data cleaning and redundant backup.

[0025] S2-1. The communication module transmits the sensor data to the central node in real time through the wired network and encrypts the data using a security protocol during the transmission process to prevent tampering and leakage.

[0026] S2-2. After reaching the calculation and decision-making module, the data management module preprocesses and stores the data. The preprocessing includes data verification, format conversion, and outlier filtering. The cleaned data is written into the database for long-term storage for historical trend analysis and model training.

[0027] In addition, the redundant unit of the security protection module synchronously backs up the key data to the backup storage during the storage process to prevent data loss caused by a single-point failure and enhance the system reliability.

[0028] Step S3. Use the decision tree model in the artificial intelligence engine to train, predict, diagnose, and generate results for the equipment state.

[0029] S3-1. Based on the historical operation data and fault records, use the decision tree model for fault diagnosis and state prediction. First, calculate the information entropy of the training data set. It is defined that the data set D contains n types of states, such as normal, warning, and fault. The specific definition of the information entropy is as shown in the formula:

[0030] Among them, is the information entropy of the data set D, D is the data set to be divided currently, n is the total number of state categories, is the proportion of samples belonging to the k-th class in the data set, is the logarithmic function with base 2.

[0031] Use the candidate features as nodes for division. The candidate features are the key parameters collected by the sensors. Calculate the information gain obtained by each feature for dividing the data set. Assume that the feature A has V possible values , let Indicates the subset of the dataset with the value of on feature A, and represent the number of samples in the dataset and the subset respectively. Then the information gain Gain(D,A) of feature A is specifically as shown in the formula:

[0032] Among them, is the information gain brought by feature to the dataset , is the information entropy of the dataset before partitioning, is the candidate partitioning feature, is the number of possible value types of feature A, is the v-th possible value of feature A, is the subset 's information entropy.

[0033] The greater the information gain, the greater the improvement in purity obtained by using feature A for partitioning. Based on this, in the decision tree training process, the feature with the highest information gain is recursively selected as the partitioning node, continuously generating branches until the stopping condition is met. The stopping condition is that the data purity of the leaf node is high enough or there are no remaining features to be divided.

[0034] The decision tree model trained by the above algorithm will be used as the knowledge base for equipment health status assessment.

[0035] S3-2, Real-time fault prediction and status assessment. Use the trained decision tree model to perform real-time inference and judgment on newly collected equipment data. The artificial intelligence engine unit calls the fault prediction function of the decision tree model and calculates the data at the current moment uploaded by the sensor one by one according to the decision tree rules. Specifically: Start from the root node of the decision tree, traverse along the corresponding branches according to the values of each sensing parameter. When reaching the leaf node, output the prediction result, such as determining the equipment status as "normal operation" or giving the specific fault type and confidence level. If the model outputs a fault warning or a performance degradation signal, corresponding decision information is generated.

[0036] The entire analysis process is completed in real time in the calculation decision module, which can detect abnormal signs at an early stage and make diagnostic decisions, providing a basis for subsequent control.

[0037] Step S4, According to the intelligent analysis result, perform fault warning, permission verification and control instruction issuance and execution through the terminal module.

[0038] S4-1, Information warning and presentation. If the abnormal state is detected in step S3, the system issues a warning through the multi-level control terminal, where the abnormal state includes potential faults, abnormal operations and equipment abnormalities; For example, alarm information is displayed on the local operation terminal, relevant equipment is highlighted on the interface of the remote monitoring center, and notifications are pushed to maintenance personnel through mobile terminals. The alarm information includes the fault location, severity, and recommended disposal measures, etc., facilitating timely response by personnel.

[0039] S4-2. When the decision requires active intervention in the equipment operation, control instructions are sent to the equipment through the terminal interaction module. The control instructions include emergency shutdown, switching to redundant component operation, or adjusting operation parameters. Before the instructions are issued, the authentication unit of the security protection module verifies the operator's authority and the integrity of the instructions to prevent unauthorized control actions. The instructions are sent to the equipment actuator through the communication network. If the main communication channel fails, the redundant unit automatically switches to the standby communication link to ensure reliable delivery of the instructions. After the equipment receives the instructions, its internal control unit performs corresponding actions to achieve intelligent management and control of on-site equipment.

[0040] S4-3. After execution, the system records the current decision instructions and the equipment response results in the data management module as the basis for future analysis and model optimization. If a fault shutdown occurs, information such as the fault cause and sensor readings at the time of shutdown is recorded to enrich the fault database.

[0041] Step S5. Collect execution feedback data and continuously optimize the decision tree model to achieve a closed-loop and intelligent evolution of equipment management.

[0042] S5-1. In this step, the data management module collects the data of the actual response and subsequent operation of the equipment in step S4, marks it, and incorporates it into the historical database.

[0043] S5-2. The artificial intelligence engine unit uses the updated database to retrain or adjust the parameters of the decision tree model, such as pruning the decision tree according to new fault samples or adding new branch nodes, continuously improving the model's recognition accuracy for equipment aging and new fault modes.

[0044] This feedback mechanism enables the system to have self-learning ability, gradually optimize the decision-making strategy, and thus form a closed-loop data stream of state perception - real-time analysis - scientific decision-making - precise execution.

[0045] Please refer to Figure 2 , Figure 2 which is an architecture diagram of an intelligent equipment management system provided by an embodiment of the present invention, including: a sensing module, a communication module, a computing and decision-making module, a terminal interaction module, a security protection module, and a data management module; The perception module is the information source of the entire system. It consists of various sensors and data acquisition devices deployed on the equipment and is used to obtain the operating status data of the equipment. The data collected by it is sent upstream through the communication module, enabling subsequent modules to monitor and analyze the equipment. Among them, the types of sensors can be selected according to the equipment type, including temperature sensors, pressure sensors, accelerometers, vibration sensors, current and voltage sensors, position and attitude sensors, which are used to perceive the environmental and performance parameters of the equipment in real time; the data acquisition device converts the analog signals of the sensors into digital data and performs preliminary filtering and calibration.

[0046] As the data transmission hub between the modules of the system, the communication module provides wired and wireless network communication functions, reliably and efficiently transmits the data collected by the perception module to the calculation and decision-making module, and sends the decision control instructions to the terminal and the equipment actuator to ensure real-time information sharing.

[0047] The communication module supports wired networks such as industrial Ethernet and CAN bus, as well as wireless communication methods such as Wi-Fi, 4G / 5G cellular networks, and wireless sensor networks. In the implementation process, standard protocols and middleware are used to ensure the interconnection and interoperability between different devices. At the same time, combined with encrypted communication and network monitoring, it cooperates with the security protection module to provide the confidentiality and integrity of data transmission.

[0048] In addition, it also supports network status monitoring and switching. When the main communication link is abnormal, it can quickly switch to the backup link to improve the robustness of system communication.

[0049] The calculation and decision-making module is the brain of the system. It includes an artificial intelligence engine unit and related calculation and processing units, which are used to deeply process and make decision inferences on the collected data.

[0050] The artificial intelligence engine unit is built-in with intelligent algorithm models such as decision trees, providing functions of fault diagnosis, performance evaluation, and prediction and decision-making, corresponding to the data analysis process in method step S3.

[0051] The calculation and decision-making module may also include a rule engine, which is used to correct or supplement the intelligent decision by combining expert rules. It is implemented through a high-performance processor or an industrial server and can be deployed on a cloud server, an edge computing gateway, or a local controller, depending on the application requirements.

[0052] As the interaction interface between people and the system and between the system and the actuator, the terminal interaction module consists of multi-level control terminals and human-machine interfaces, including various terminal devices at the field level and the remote level. It presents the analysis results of the calculation and decision-making module to the user in an intuitive way and receives user input or confirmation operations: Specifically, the on-site operation terminal enables on-site equipment operation and maintenance personnel to view the status and receive alarms; the remote monitoring terminal enables managers to centrally monitor the operation status of multiple pieces of equipment; and the mobile terminal enables operation and maintenance personnel to obtain alarm and status information at any time. On the other hand, the control unit of this module is also connected to the actuator of the equipment, and can apply the control instructions from the decision-making module to the actual equipment to complete the precise execution link.

[0053] The multi-level terminals work together through permission division and network connection. For example, the on-site terminal can directly stop the machine in case of emergency, while the remote terminal is used to approve major operations or overall maintenance plans.

[0054] The security protection module is a module that ensures the security and reliability of each module in the system, including an authentication unit and a redundancy unit. The authentication unit is responsible for the identity authentication and access control of the system to ensure that only authorized devices and users can access the data of the sensing module or issue control instructions. The redundancy unit provides backup and fault tolerance mechanisms for the key components of the system to improve the overall reliability. The security protection module also covers functions such as abnormal monitoring and emergency handling. For example, when it detects that a certain sensor fails, it notifies the operation and maintenance personnel to replace it in time, and enables the data of the redundant sensor to be used as a substitute. Through authentication control and fault tolerance redundancy, the security protection module builds a solid barrier for the stable operation of the system, ensuring that each link of equipment management is safe, controllable and highly available.

[0055] As an independent functional module, the data management module runs through sensing, decision-making and terminals, and is responsible for data storage, organization and service provision. It includes various types of data storage units, such as local databases, real-time databases and cloud databases, etc.

[0056] Its functions include: receiving and storing the raw data from the sensing module and the real-time data transmitted by the communication module; maintaining the historical data warehouse to store the long-term operation records of the equipment, maintenance and repair logs, and fault case libraries; providing data query and analysis interfaces to enable the calculation and decision-making module to efficiently obtain the data sets required for training models; and performing data life cycle management, such as regularly archiving old data, deleting redundant data and data compression.

[0057] In addition, the data management module is responsible for data consistency and accuracy verification. For example, it synchronizes multi-source data using timestamps, corrects sensor drift, and ensures unit and format uniformity when fusing data from multiple devices.

[0058] This module can also generate reports or visualization data as needed for the terminal interaction module to call. Since the data involved in equipment management may be huge and diverse, the data management module is usually deployed on high-performance data servers or distributed storage systems, and database management systems or big data platform technologies are used to ensure the efficiency and reliability of data reading and writing.

[0059] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent equipment management method, characterized in that, It includes the following steps: Step S1, collecting key operation parameters of the equipment through multi-type sensors and performing data processing; Step S2, transmitting the encrypted data to the central node in real time through the communication module, and completing data cleaning and redundant backup; Step S3, using the decision tree model in the artificial intelligence engine to train, predict, diagnose and generate results for the equipment status; Step S4, according to the intelligent analysis results, performing fault warning, permission verification and control instruction issuance and execution through the terminal module; Step S5, collecting execution feedback data, continuously optimizing the decision tree model, and realizing the closed-loop and intelligent evolution of equipment management.

2. An intelligent equipment management method according to claim 1, characterized in that: In step S1, the following sub-steps are further included: S1-1, starting the security authentication process, and the authentication unit of the security protection module authenticates the equipment sensors accessing the system; S1-2, obtaining the key parameters of the equipment through the sensors of the sensing module, and taking the key parameters as the original data, and the key parameters include temperature, pressure, vibration and current.

3. An intelligent equipment management method according to claim 1, characterized in that: In step S2, the following sub-steps are further included: S2-1, the communication module transmits the sensor data to the central node in real time through the wired network, and encrypts the data using the security protocol during the transmission process to prevent tampering and leakage; S2-2, after the data is transmitted to the calculation and decision module, the data management module preprocesses and stores the data. The preprocessing includes data verification, format conversion and outlier filtering. The cleaned data is written into the database for long-term storage and used for historical trend analysis and model training; The redundant unit of the security protection module synchronously backs up the key data to the standby storage during the storage process to prevent data loss caused by single-point failure.

4. An intelligent equipment management method according to claim 1, characterized in that: In step S3, the following sub-steps are further included: S3-1, based on the historical operation data and fault records, using the decision tree model for fault diagnosis and status prediction. First, calculate the information entropy of the training data set. It is defined that the data set D contains n types of states, and the specific definition of information entropy is shown in the formula: Among them, H(D) is the information entropy of the dataset D, D is the dataset to be partitioned currently, n is the total number of state categories, and p k is the proportion of samples belonging to the k-th category in the dataset, and log2 is the logarithmic function with base 2; Partition with candidate features as nodes. The candidate features are the key parameters collected by sensors. Calculate the information gain obtained by each feature for partitioning the dataset. Suppose feature A has V possible values a 1 , a 2 , …, a V , let D v represent the subset in the dataset where the value of feature A is a V . |D| and |D v | respectively represent the number of samples in the dataset and the subset. Then the information gain Gain(D, A) of feature A is specifically shown as the formula: Among them, Gain(D,A) is the information gain brought by feature A to dataset D, H(D) is the information entropy of the dataset before partitioning, A is the candidate partitioning feature, V is the number of possible value types of feature A, and a v is the v-th possible value of feature A, and H(D v ) is the information entropy of the subset D v ; The greater the information gain, the greater the improvement in purity obtained by using feature A for partitioning. Accordingly, the decision tree training process recursively selects the feature with the highest information gain as the partitioning node, continuously generates branches until the stop condition is met; S3-2, using the trained decision tree model to perform real-time inference and judgment on the newly collected equipment data. The artificial intelligence engine unit calls the fault prediction function of the decision tree model, and calculates each piece of data at the current moment uploaded by the sensor according to the decision tree rules one by one. Specifically: Starting from the root node of the decision tree, traverse along the corresponding branches according to the values of each sensing parameter, and output the prediction result when reaching the leaf node.

5. An intelligent equipment management method according to claim 1, characterized in that: In step S4, the following sub-steps are further included: S4-1. When an abnormal state is detected in step S3, the system issues an alarm through a multi-level control terminal, where the abnormal state includes potential faults, abnormal operations, and equipment anomalies. S4-2. When the decision requires active intervention in the equipment operation, a control instruction is sent to the equipment through the terminal interaction module. Before the instruction is issued, the authentication unit of the security protection module verifies the operator's authority and the integrity of the instruction to prevent unauthorized control actions. The instruction is sent to the equipment actuator through the communication network. If the primary communication channel fails, the redundant unit automatically switches to the backup communication link to ensure reliable delivery of the instruction. After receiving the instruction, the internal control unit of the equipment performs corresponding actions to achieve intelligent control of the on-site equipment. S4-3. After execution, the system records the current decision instruction and the equipment response result in the data management module as a basis for future analysis and model optimization. When a fault shutdown occurs, the cause of the fault and the sensor reading information at the time of shutdown are recorded.

6. A method for intelligent equipment management according to claim 1, characterized in that: In step S5, the following sub-steps are further included: S5-1. Collect the actual response and subsequent operation data of the equipment in step S4 through the data management module, mark it, and incorporate it into the historical database. S5-2. Use the updated database by the artificial intelligence engine unit to re-adjust the decision tree model parameters, prune the decision tree or add new branch nodes according to the new fault samples.

7. An intelligent equipment management system, which is applied to an intelligent equipment management method according to any one of claims 1-6, and is characterized in that It includes: A sensing module, a communication module, a computing and decision-making module, a terminal interaction module, a security protection module, and a data management module; The sensing module is the information source of the entire system, consisting of various sensors and data acquisition devices deployed on the equipment, used to obtain the operation status data of the equipment. The data collected by it is sent upstream through the communication module, enabling subsequent modules to monitor and analyze the equipment. The communication module serves as the data transmission hub between the system modules, providing wired and wireless network communication functions, transmitting the data collected by the sensing module to the computing and decision-making module, and sending the decision control instruction to the terminal interaction module. The computing and decision-making module is the brain of the system, including an artificial intelligence engine unit and a computing and processing unit, used for in-depth processing and decision-making reasoning of the collected data. The artificial intelligence engine unit is built-in with a decision tree intelligent algorithm model, providing functions of fault diagnosis, performance evaluation, and prediction decision-making. The terminal interaction module serves as the interaction interface between humans and the system as well as between the system and the actuator, consisting of a multi-level control terminal and a human-machine interface, including various terminal devices at the field level and remote level, presenting the analysis results of the computing and decision-making module to the user in an intuitive way, and receiving user input and confirmation operations. The security protection module includes an authentication unit and a redundant unit, providing security and reliability guarantees for the system modules. The authentication unit is responsible for the identity authentication and access control of the system, ensuring that only authorized devices and users can access the sensing module data and issue control instructions. The redundant unit provides a backup and fault tolerance mechanism for the key components of the system. The data management module, as an independent functional module, runs through the sensing module, the computing and decision-making module, and the terminal interaction module, assuming the responsibilities of data storage, organization, and service provision, and includes various types of data storage units.

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