Industrial internet platform system and use method

Abstract

The invention provides an industrial internet platform system and a use method, and belongs to the technical field of industrial internet of things, and the system comprises a collection task configuration and equipment access module which configures and executes a data collection task, and collects equipment energy consumption and operation state data through a multi-protocol adapter; the energy consumption data analysis module is used for storing the equipment energy consumption data into a database according to a time sequence, carrying out aggregation calculation and then displaying the equipment energy consumption data to a front-end interface; the equipment management module is used for identifying an equipment state, synchronizing the equipment state to a front-end interface and managing equipment firmware upgrading; the alarm management module is used for configuring an alarm rule and generating an alarm event and giving an alarm when a trigger condition in the alarm rule is met; and the operation and maintenance service module is used for carrying out fault root cause analysis in combination with a preset association relationship between equipment when the alarm event starts operation and maintenance linkage, and generating and issuing an operation and maintenance work order. Through multi-protocol access, mass equipment management and intelligent operation and maintenance are realized, energy consumption monitoring is optimized, and management efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of industrial internet of things, and particularly relates to an industrial internet platform system and a use method. BACKGROUND

[0002] With the development of the Internet of Things, the industrial internet platform has become a key infrastructure for the digital transformation of Internet of Things enterprises. At present, industrial equipment is facing the problems of protocol fragmentation, complex management of massive devices, serious data island phenomenon, and low operation and maintenance efficiency, and the traditional industrial internet platform solution has the following problems: Firstly, the existing industrial internet platform mostly adopts a single protocol access solution, which cannot be compatible with multiple industrial protocols such as Modbus, MQTT, and TCP, resulting in difficulties in cloud access for different brands of devices, especially for inventory device modification, which usually requires custom gateway development, resulting in high implementation cost and long cycle. Secondly, the traditional Internet of Things system lacks a unified device lifecycle management framework, and device registration, grouping, and permission allocation operations depend on manual configuration, and when the device scale reaches ten thousand, the management efficiency decreases dramatically, although some Internet of Things systems use methods such as hierarchical management models, but there are still problems of rough permission strength and inflexible resource allocation. Thirdly, most industrial Internet of Things platforms only provide basic data storage functions, and lack comprehensive analysis functions for time series data, such as some platforms that propose aggregation algorithms based on time windows, but do not solve the problem of combining multi-dimensional statistical analysis and business scenarios. Finally, the existing industrial Internet of Things platform mostly uses a simple threshold alarm method for fault diagnosis, which has a high false alarm rate and lacks a closed-loop processing process, and some early warning solutions do not consider device correlation, and cannot achieve real intelligent diagnosis. SUMMARY

[0003] In a first aspect, the embodiments of the present application provide an industrial internet platform system, comprising: a collection task configuration and device access module configured to configure and execute a data collection task, and to synchronize device energy consumption data and device operating status data collected from a target device through a multi-protocol adapter; an energy consumption data analysis module configured to store the collected device energy consumption data in a database according to a time sequence, and to perform aggregation calculation using a preset sliding window, and to display the calculation results to a front-end interface; a device management module configured to identify device status according to the collected device operating status data and to synchronize the device status to a front-end interface, and to manage device firmware upgrades; an alarm management module configured to configure alarm rules, and to generate an alarm event when the device operating status data meets a trigger condition in the alarm rules, and to alarm according to an alarm mode set in the corresponding alarm rules; The operation and maintenance service module is configured to, when operation and maintenance linkage is enabled in an alarm rule triggered by an alarm event, perform fault root cause analysis using an abnormality propagation algorithm in combination with a preset correlation between devices, generate an operation and maintenance work order, and distribute the operation and maintenance work order in a preset manner.

[0004] Further, the collection task configuration and device access module comprises: The task configuration unit is configured to configure a task source, an execution type, an interval time, and a management Internet of Things point of a collection task; the collection task comprises an energy consumption data collection task and an operation status collection task. The Modbus transparent transmission analysis unit is configured to convert RS485 signals uploaded by a device into JSON format device energy consumption data and device operation status data and provide the data to the device management module and the data analysis module. The protocol conversion unit is configured to interact with a communication module of a heterogeneous device through an AT instruction interface, automatically select an optimal communication protocol from a protocol library according to a handshake response time of the corresponding device, and provide interaction data to the device management module and the data analysis module; the protocol library is configured with an MQTT protocol, a TCP protocol, and an HTTP protocol.

[0005] Further, the collection task configuration and device access module further comprises: The device manual addition unit is configured to add a corresponding device according to device basic information and device fingerprint information input by a user in response to an addition request of the user; the device basic information comprises a device MAC address and a preset key; the fingerprint information comprises a manufacturer ID and a hardware version number. The two-dimensional code binding unit is configured to extract device basic information by scanning a two-dimensional code configured on a device and verify the device basic information. The access service unit is configured to match device fingerprint information input by a user or extracted from a two-dimensional code of a device with a preset protocol template, establish a shadow channel for the device using the matched protocol template, and store data of the corresponding device.

[0006] Further, the energy consumption data analysis module comprises: The data storage unit is configured to construct a time series database and store device energy consumption data received in real time. The data aggregation unit is configured to perform sliding processing using a preset window size on stored device energy consumption data in a stream processing manner, and perform aggregation calculation in each window. The report management unit is configured to construct a data display template using an XML Schema description language, receive a configuration of the data display template by a user, and generate a data display visualization interface instance. A data export service unit is configured to convert the aggregated computing result into an Excel / CSV format using a stream processing method and display the result in a corresponding data display visualization interface instance.

[0007] Further, the device management module comprises: A device shadow service unit is configured to store device running state data in the form of key-value pairs and synchronize the device running state data with a front-end interface in real time using a write-copy-on-write method through a WebSocket channel. A device upgrade management unit is configured to generate a firmware upgrade package using a differential compression algorithm based on the difference between an old firmware and a new firmware of a device and distribute the firmware upgrade package to the corresponding device to implement online firmware upgrade of the device. A device access control unit is configured to perform device access control based on user roles according to preset access permissions, and the granularity of the access permissions is a device point level. A device map service unit is configured to extract positioning data from device running state data uploaded by a device, determine a geographic location, and render the device on a map using a preset icon through an integrated map service API. A device health degree evaluation unit is configured to calculate a device health score based on device online duration, alarm frequency, and maintenance records and push maintenance suggestions.

[0008] Further, the alarm management module comprises: An alarm rule configuration unit is configured to configure trigger conditions, alarm levels, notification channels, and operation and maintenance linkage switches of alarm rules, and the trigger conditions are set based on the values of the Internet of Things points in the device running state data. An alarm triggering and judging unit is configured to receive device states identified based on the device running state data in real time and compare the device states with alarm rules to generate an alarm event when the trigger conditions are met. An alarm notification unit is configured to send alarm information to specified users or user groups according to the alarm levels corresponding to the alarm events and the configured notification channels. An operation and maintenance linkage unit is configured to send alarm events and related device states to an operation and maintenance service module to trigger the generation of operation and maintenance work orders when the operation and maintenance linkage switches in the alarm rules corresponding to the triggered alarm events are turned on.

[0009] Further, the operation and maintenance service module comprises: An intelligent diagnosis unit is configured to receive alarm events and device state data related to the alarm events, construct a device association topology graph based on preset association relationships between devices, calculate the fault influence weights of each device node, and generate a fault diagnosis report when the weight exceeds a dynamic threshold; the dynamic threshold is adjusted according to the historical work order accuracy at a set frequency. a work order generation unit configured to generate a maintenance work order containing fault location and repair suggestions according to the fault diagnosis report; a maintenance personnel matching unit configured to match the maintenance work order with an optimal maintenance personnel based on the skill tags, geographical location and historical processing success rate of the maintenance personnel using a KNN algorithm; a work order dispatching and tracking unit configured to dispatch the maintenance work order to the matched maintenance personnel while starting a processing countdown, and automatically upgrade to the next level of responsibility person for maintenance work order pushing when not processed within the timeout.

[0010] In a second aspect, the embodiments of the present application further provide a use method of the industrial internet platform system according to the first aspect, comprising the following steps: S1. configuring a data collection task, defining target equipment, a set of collection points and execution rules; S2. executing the data collection task, and synchronously collecting equipment energy consumption data and equipment running state data from the target equipment through a multi-protocol adapter; S3. storing the collected equipment energy consumption data in time sequence to a database and using a preset sliding window for aggregation calculation, and then displaying the calculation results to a front-end interface; S4. identifying the equipment state according to the collected equipment running state data, and synchronizing the equipment state with the front-end interface in real time and managing equipment firmware upgrade based on the equipment state; S5. configuring an alarm rule, and when the equipment running state data meets the triggering condition in the alarm rule, generating an alarm event, and performing alarm notification according to the alarm mode set in the corresponding alarm rule; S6. when the alarm rule triggered by the alarm event is enabled for operation linkage, combining a preset inter-device association relationship, using an abnormal propagation algorithm to perform root cause analysis on the fault, generating a maintenance work order and dispatching it to a designated maintenance personnel.

[0011] Further, in step S1, the task source, execution type, interval time and required Internet of Things points of the collection task are configured; The specific steps of step S2 are as follows: S21. constructing a multi-protocol adapter; S22. responding to the user's addition request, adding the corresponding access equipment according to the device basic information and device fingerprint information input by the user, or extracting the device basic information by scanning the two-dimensional code configured on the access equipment, and verifying the device basic information; S23. determining whether the access equipment is a device for RS485 signal transmission through the multi-protocol adapter; If yes, go to step S24; If no, go to step S25; S24. The Modbus protocol of the collected data of the access device is parsed and converted into device energy consumption data and device running state data in JSON format, and step S26 is entered; S25. The communication module of the access device is interacted through the AT instruction interface, and the optimal communication protocol is automatically selected from the protocol library according to the handshaking response time of the access device to obtain the device energy consumption data and the device running state data; S26. The device fingerprint information input by the user or extracted from the two-dimensional code of the access device is matched with the preset protocol template, and the shadow channel of the access device is established using the matched protocol template to store the data of the corresponding access device; Step S3 is specifically as follows: S31. The device data frequency and the current processor load are monitored in real time, the window size is calculated according to the device data frequency, and when the current processor load is greater than a set threshold, the number of parallel processing threads in the sliding window is reduced in a preset manner; S32. The timing database is constructed, and the real-time received device energy consumption data is stored; S33. The stored device energy consumption data is processed in a streaming manner, the calculated window size is used for sliding, and the calculated parallel processing threads are used for data aggregation calculation in each window; S34. The data display template is constructed using XML Schema description language, and the user's configuration of the data display template is received to generate a data display visualization interface instance; S35. The aggregation calculation result is converted into Excel / CSV format using a streaming manner, and is displayed in the corresponding data display visualization interface instance.

[0012] Further, step S4 is specifically as follows: S41. The device running state data is stored in the form of key-value pairs, and the device running state data is synchronized with the front-end interface in real time using the WebSocket channel through copy-on-write; S42. It is judged whether there is a new firmware version for the accessed device, and when there is, a firmware upgrade package is generated using a differential compression algorithm to reflect the difference between the old firmware and the new firmware of the device, and is issued to the corresponding device for online firmware upgrade; S43. The access device is accessed based on the user role according to a preset access permission, and the granularity of the access permission is a device point level; Step S5 is specifically as follows: S51. The trigger condition, alarm level, notification channel and operation and maintenance linkage switch of the alarm rule are configured; the trigger condition is set based on the value of the Internet of Things point in the device running state data; S52. Real-time monitoring of the device status, comparing the device status with the configured alarm rules, and generating an alarm event when the device status meets the triggering condition of any alarm rule; S53. According to the alarm level corresponding to the alarm event and the pre-configured notification channel, sending alarm information to the specified user or user group; S54. Determine whether the operation and maintenance linkage switch in the alarm rule corresponding to the triggered alarm event is turned on; If turned on, go to step S6; If not turned on, end; Step S6 is specifically as follows: S61. Receive the alarm event and the device status data related to the alarm event, construct a device association topology graph combining the pre-set association relationship between devices, calculate the fault influence weight of each device node, and generate a fault diagnosis report when the weight exceeds the dynamic threshold; the dynamic threshold is adjusted according to the historical work order accuracy at a set frequency; S62. According to the fault diagnosis report, generate an operation and maintenance work order containing fault positioning and repair suggestions; S63. Based on the skill label, geographical location and historical processing success rate of the operation and maintenance personnel, use the KNN algorithm to match the optimal operation and maintenance personnel for the operation and maintenance work order; S64. Distribute the operation and maintenance work order to the matched operation and maintenance personnel, start the processing countdown at the same time, and automatically upgrade the next level of responsibility person to push the operation and maintenance work order when the timeout is not processed.

[0013] From the above technical solutions, the present application has the following advantages: The industrial internet platform system and use method provided by the present application realize fast access and unified management of heterogeneous devices, reduce device access complexity, improve device management efficiency, and reduce manual intervention; through real-time monitoring of device status, timely discovery and processing of faults, fault root cause analysis and operation and maintenance personnel matching through intelligent algorithms, operation and maintenance efficiency is improved, and stable operation of the device is ensured; in-depth analysis of device energy consumption data, intuitive data display and export functions are provided, thereby optimizing energy consumption management for users; fine-grained access control based on user roles ensures device data security, reduces data loss and device failure risk; reduces human and material resources investment in user device management and operation and maintenance, improves device operation efficiency, prolongs device service life, and reduces enterprise operating costs. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0015] Figure 1 A schematic diagram of an industrial internet platform system of the present application.

[0016] Figure 2 A flowchart of a method for using an industrial internet platform system of the present application. DETAILED DESCRIPTION

[0017] Various embodiments of the present disclosure will be described in detail below with reference to the industrial internet platform system, and various embodiments of the present disclosure will be more fully described. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to encompass all adjustments, equivalents and / or alternatives falling within the spirit and scope of various embodiments of the present disclosure.

[0018] Illustratively, with the rapid development of the Internet of Things, the industrial internet platform has become a key support for Internet of Things companies to realize digital transformation. However, current industrial equipment faces many challenges in the process of access and management, such as fragmentation of access protocols, complex management of massive devices, widespread existence of data island phenomenon, and low operation and maintenance efficiency, etc. When dealing with these problems, the existing industrial internet platform exposes the following deficiencies: First, most existing industrial internet platforms only support a single access protocol, making it difficult to be compatible with diversified industrial protocols such as Modbus, MQTT, TCP, etc. This leads to difficulties in the cloud access process of different brands of devices, especially for the transformation of inventory devices, which often requires custom development of gateways, not only high implementation cost, but also long cycle. Second, traditional Internet of Things systems generally lack a unified device lifecycle management framework. Key operations such as device registration, grouping, and permission allocation mostly rely on manual configuration. When the number of devices reaches tens of thousands, the management efficiency will decrease significantly. Although some Internet of Things systems attempt to use a hierarchical management model to optimize, there are still problems such as coarse granularity of permission control and inflexible resource allocation. Third, most industrial Internet of Things platforms can only provide basic data storage functions, lacking the ability to comprehensively analyze time series data. For example, although some platforms introduce aggregation algorithms based on time windows, they fail to effectively solve the problem of combining multi-dimensional statistical analysis with specific business scenarios. Finally, existing industrial Internet of Things platforms mostly use simple threshold alarm methods for fault diagnosis, which have a high false alarm rate and lack effective closed-loop processing procedures. In addition, some early warning schemes fail to fully consider the correlation between devices, making it difficult to achieve real intelligent diagnosis.

[0019] To solve the above problems, the embodiment provides an industrial internet platform system, which realizes intelligent management of the whole life cycle of the equipment through device access, management, data analysis and operation and maintenance service, and improves the overall operation efficiency and management level of the system.

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0021] Please refer to Figure 1 As shown in FIG. 1, the system includes: The collection task configuration and device access module is configured to configure and execute a data collection task, and to synchronize the collection of device energy consumption data and device operating state data from the target device through a multi-protocol adapter; It should be noted that by configuring and executing the data collection task, efficient collection of device energy consumption data and operating state data is realized, which provides accurate and timely data support for subsequent data analysis, device management and operation and maintenance service; the multi-protocol adapter can be compatible with multiple industrial protocols, breaking the device data island and realizing fast access and data synchronization of heterogeneous devices; The energy consumption data analysis module is configured to store the collected device energy consumption data in time sequence to a database and perform aggregation calculation using a preset sliding window, and then display the calculation result to the front-end interface; It should be noted that the energy consumption data analysis module helps users to mine potential rules in the energy consumption data, realizes fine management and optimization of energy consumption, reduces energy consumption cost, and improves energy utilization efficiency; The device management module is configured to identify device states according to the collected device operating state data and synchronize the device states to the front-end interface, and manage device firmware upgrades; It should be noted that the device management module enables users to keep abreast of the running status of the device at any time and take timely measures; at the same time, through the management of device firmware upgrades, the device is ensured to run the latest version of firmware, the performance and security of the device are improved, and the service life of the device is prolonged; The alarm management module is configured to configure alarm rules, and when the device operating state data meets the triggering condition in the alarm rules, to generate an alarm event and alarm according to the alarm mode set in the corresponding alarm rules; It should be noted that the alarm management module realizes real-time monitoring and analysis of the device operating state data, improves the timeliness and effectiveness of device monitoring, shortens the fault response time, reduces the risk of device failure, and ensures the stable operation of the device; The operation and maintenance service module is configured to, when operation and maintenance linkage is enabled in an alarm rule triggered by an alarm event, use an abnormality propagation algorithm to analyze a fault root cause in combination with a preset correlation between devices, generate an operation and maintenance work order, and distribute the operation and maintenance work order in a preset manner. It should be noted that the operation and maintenance service module realizes automation and intelligentization of operation and maintenance work, improves operation and maintenance efficiency, shortens fault repair time, ensures stable operation of devices, and reduces the impact of device faults on production.

[0022] The embodiment realizes collection, analysis, monitoring, alarm, and operation and maintenance full-link management of device energy consumption data and running state data, and improves overall operation efficiency and management level of the system.

[0023] Further, as a refinement and expansion of the specific implementation of the above embodiment, in order to completely describe the specific implementation process in the embodiment, another industrial internet platform system is provided, which comprises: The acquisition task configuration and device access module is configured to configure and execute a data acquisition task, and synchronize acquisition of device energy consumption data and device running state data from a target device through a multi-protocol adapter. The acquisition task configuration and device access module comprises: The task configuration unit is configured to configure a task source, an execution type, an interval time, and a management Internet of Things point of a collection task; the collection task comprises an energy consumption data collection task and a running state collection task. The Modbus transparent transmission analysis unit converts RS485 signals uploaded by a device into JSON format device energy consumption data and device running state data, and provides the data to a device management module and a data analysis module. The protocol conversion unit is configured to interact with a communication module of a heterogeneous device through an AT instruction interface, automatically select an optimal communication protocol from a protocol library according to a handshake response time of the corresponding device, and provide interaction data to the device management module and the data analysis module; the protocol library is configured with an MQTT protocol, a TCP protocol, and an HTTP protocol. The device manual addition unit is configured to respond to an addition request of a user, and add a corresponding device according to device basic information and device fingerprint information input by the user; the device basic information comprises a device MAC address and a preset key; the fingerprint information comprises a manufacturer ID and a hardware version number. The two-dimensional code binding unit is configured to extract device basic information by scanning a two-dimensional code configured on a device, and verify the device basic information. The access service unit is configured to match device fingerprint information input by a user or extracted from a two-dimensional code of a device with a preset protocol template, and then use the matched protocol template to establish a shadow channel for the device to store data of the corresponding device. An energy consumption data analysis module is configured to store the collected device energy consumption data in a time sequence into a database, aggregate the data using a preset sliding window, and display the calculation results on a front-end interface. The energy consumption data analysis module includes: A data storage unit is configured to build a time sequence database and store the real-time received device energy consumption data. A data aggregation unit is configured to use a preset window size to perform sliding processing on the stored device energy consumption data in a stream processing manner, and perform aggregation calculation within each window. A report management unit is configured to build a data display template using an XML Schema description language, receive user configuration of the data display template, and generate a data display visualization interface instance. A data export service unit is configured to convert the aggregation calculation results into Excel / CSV format using a stream processing method, and display the results in the corresponding data display visualization interface instance. A device management module is configured to identify device status based on collected device running status data and synchronize the data to a front-end interface, and manage device firmware upgrades. The device management module includes: A device shadow service unit is configured to store device running status data in the form of key-value pairs, and use a WebSocket channel to synchronize the data to the front-end interface in real time through copy-on-write. A device upgrade management unit is configured to generate a firmware upgrade package using a differential compression algorithm based on the differences between the old firmware and the new firmware of a device, and distribute the package to the corresponding device to achieve online firmware upgrade. A device access control unit is configured to control device access based on user roles and preset access permissions, and the granularity of the access permissions is at the device point level. A device map service unit is configured to extract positioning data from device running status data uploaded by a device, determine the geographic location, and render the device on a map using a preset icon through an integrated map service API. A device health assessment unit is configured to calculate a device health score based on device online duration, alarm frequency, and maintenance records, and push maintenance recommendations. Specifically, the device health score calculation formula is as follows:

[0024] wherein, is the health score, is the total offline duration of the device in the past 7 days, is the total number of alarms triggered by the device in the past 7 days, is the number of days since the last maintenance, 、 、 are weight coefficients, for example takes 5, takes 3, takes 0.5; An alarm management module is configured to configure alarm rules, and when the device running state data meets the trigger condition in the alarm rules, generate an alarm event, and alarm according to the alarm mode set in the corresponding alarm rule; The alarm management module comprises: An alarm rule configuration unit is configured to configure the trigger condition, alarm level, notification channel and operation and maintenance linkage switch of the alarm rule; the trigger condition is set based on the value of the Internet of Things point in the device running state data; An alarm triggering and judging unit is configured to receive the device state identified according to the device running state data in real time, and compare it with the alarm rule, and generate an alarm event when the trigger condition is met; An alarm notification unit is configured to send alarm information to a specified user or user group according to the alarm level corresponding to the alarm event and the configured notification channel; An operation and maintenance linkage unit is configured to send the alarm event and related device state to the operation and maintenance service module when the operation and maintenance linkage switch in the alarm rule corresponding to the triggered alarm event is turned on, so as to trigger the generation of an operation and maintenance work order; An operation and maintenance service module is configured to use an abnormal propagation algorithm to perform fault root cause analysis, generate an operation and maintenance work order, and issue the operation and maintenance work order according to a preset mode when the operation and maintenance linkage is enabled in the alarm rule triggered by the alarm event, in combination with the preset correlation relationship between devices; The operation and maintenance service module comprises: An intelligent diagnosis unit is configured to receive the alarm event and the device state data related to the alarm event, construct a device correlation topology graph in combination with the preset correlation relationship between devices, calculate the fault influence weight of each device node, and generate a fault diagnosis report when the weight exceeds a dynamic threshold; the dynamic threshold is adjusted according to the historical work order accuracy at a set frequency; The dynamic threshold The adjustment formula is:

[0025] wherein, is the weight threshold after the new round of adjustment, is the weight threshold before the last round of adjustment, is the work order accuracy after the last adjustment based on the implementation effect of the historical work order, is the work order target accuracy, for example, set to 0.95, is the learning rate, for example, set to 0.1; The work order generation unit is configured to generate an operation and maintenance work order containing fault positioning and repair suggestions according to the fault diagnosis report; The operation and maintenance personnel matching unit is configured to use a KNN algorithm to match the optimal operation and maintenance personnel for the operation and maintenance work order based on the skill tags, geographical positions and historical processing success rates of the operation and maintenance personnel. Specifically, the formula for matching the optimal operation and maintenance personnel for the operation and maintenance work order using the KNN algorithm is as follows:

[0026] wherein, is the comprehensive distance between the operation and maintenance personnel and the work order requirements, and the smaller the value, the higher the matching degree; is the skill tag matching degree, and the value is between 0 and 1, with 1 indicating complete matching; is the normalized geographical position distance, is the historical processing success rate, 、 、 is a weight coefficient, and satisfies , for example takes 0.5, takes 0.3, takes 0.2; The work order dispatching and tracking unit is configured to dispatch the operation and maintenance work order to the matched operation and maintenance personnel, start a processing countdown at the same time, and automatically upgrade the next level of responsibility person to push the operation and maintenance work order when the operation and maintenance work order is not processed within the timeout.

[0027] As shown in Figure 2 , the following is an embodiment of a method for using the industrial internet platform system provided by the embodiments of the present disclosure. The method and the industrial internet platform system of each embodiment described above belong to the same inventive concept. Details not described in the embodiment of the method for using the industrial internet platform system can be referred to the embodiments of the industrial internet platform system described above.

[0028] The method comprises the following steps: S1. Configure a data collection task, define target equipment, a collection point set and execution rules; It should be noted that by configuring the data collection task, the target equipment, the collection point set and the execution rules are defined to ensure the accuracy and completeness of data collection, and improve the efficiency and quality of data collection; S2. Execute the data collection task, and synchronize the collection of equipment energy consumption data and equipment running state data from the target equipment through a multi-protocol adapter; It should be noted that this step realizes real-time collection and transmission of equipment data, provides timely and accurate data support for subsequent data analysis, equipment management and operation and maintenance services, breaks down the equipment data island, and promotes the sharing and utilization of equipment data; S3. The collected device energy consumption data is stored in the database in time sequence and aggregated using a preset sliding window, and the calculation result is displayed to the front-end interface; It should be noted that this step enables the user to intuitively understand the energy consumption of the device, mine potential rules in the energy consumption data, realize fine management and optimization of energy consumption, reduce energy consumption cost, and improve energy utilization efficiency; S4. Identify the device state according to the collected device running state data, and synchronize the device state with the front-end interface in real time and manage the device firmware upgrade based on the device state; It should be noted that this step enables the user to grasp the running condition of the device at any time, discover device faults or abnormalities in a timely manner, ensure that the device runs the latest version of the firmware, improve the performance and security of the device, and prolong the service life of the device; S5. Configure alarm rules, and when the device running state data meets the triggering condition in the alarm rules, generate an alarm event, and perform alarm notification according to the alarm mode set in the corresponding alarm rule; It should be noted that this step realizes real-time monitoring and intelligent alarm of the device running state, improves the timeliness and effectiveness of device monitoring, shortens the fault response time, reduces the risk of device failure, and ensures the stable operation of the device; S6. When the alarm rule triggered by the alarm event enables operation and maintenance linkage, the abnormal propagation algorithm is used to analyze the root cause of the fault based on the preset correlation relationship between devices, and an operation and maintenance work order is generated and assigned to the designated operation and maintenance personnel; It should be noted that this step realizes the automation and intelligentization of operation and maintenance work, improves the operation and maintenance efficiency, shortens the fault repair time, ensures the stable operation of the device, reduces the impact of device failure on production, and improves the intelligent level of device management.

[0029] This embodiment realizes the whole process operation from data collection task configuration, device data collection, energy consumption data analysis, device state monitoring and firmware upgrade, alarm management to operation and maintenance service, and realizes intelligent management and operation and maintenance of the device.

[0030] Further, as a refinement and extension of the above embodiment, in order to completely describe the specific implementation process in this embodiment, another use method of an industrial internet platform system is provided, which includes the following steps: S1. Configure a data collection task, define target devices, a set of collection points, and execution rules; In step S1, the task source, execution type, interval time, and required Internet of Things points of the collection task are configured; S2. Perform a data collection task, and synchronously collect device energy consumption data and device running state data from the target device through the multi-protocol adapter; the specific steps of step S2 are as follows: S21. Construct a multi-protocol adapter; S22. In response to a user's addition request, add the corresponding access device according to the device basic information and device fingerprint information input by the user, or extract the device basic information by scanning the two-dimensional code configured on the access device, and verify the device basic information; S23. Determine whether the access device is a device for RS485 signal transmission through the multi-protocol adapter; If yes, go to step S24; If no, go to step S25; S24. Perform Modbus protocol analysis on the access device data collection and convert it into JSON format device energy consumption data and device running state data, and go to step S26; S25. Interact with the communication module of the access device through the AT instruction interface, and automatically select the optimal communication protocol from the protocol library according to the handshake response time of the access device to obtain device energy consumption data and device running state data; S26. Match the device fingerprint information input by the user or extracted from the two-dimensional code of the access device with the preset protocol template, and then use the matched protocol template to establish a shadow channel for the access device for data storage of the corresponding access device; Exemplarily, taking the addition of a C-Tech single-phase electric meter in room 402 of building A of a certain commercial building as an example, the specific implementation process is as follows: Device addition operation: the operation and maintenance personnel scans the two-dimensional code on the electric meter through the "My Electric Meter" board function in the device management of the system, and the system automatically extracts the device MAC address (such as "00:1B:44:11:3A:B7") and the preset key, verifies the integrity of the device basic information through the SHA-256 algorithm, and ensures that the data has not been tampered with; Protocol matching and channel establishment: the access service unit matches the extracted device fingerprint information (manufacturer ID: a certain electrical company, hardware version number: V1.2) with the preset Modbus protocol template, establishes a shadow channel for the electric meter after successful matching, and synchronizes real-time voltage, current and other running state data to the time series database; Collection task configuration: create a meter reading rule interface in the meter reading rule management of the system, configure the task name as "402 room daily electric energy consumption collection", select the task source as "device" and specify the DN code of the electric meter, set the execution type as "fixed interval polling", select the time unit as "hour", input the interval time as "24" (execute at 2:00 am every day), select the operation mode as "data collection", and associate the Internet of Things point "total active power" ; Access effect: from scanning the two-dimensional code to collecting the task, the whole process takes less than 10 minutes, realizing no-code quick access, and the data collection delay is stable at 300-500 ms. In the electricity meter panel interface, the running data of the electricity meter (such as "total active power: 8.00W") can be viewed in real time, realizing no-sense access of the device; S3. The collected device energy consumption data is stored in the database according to the time sequence and aggregated by using a preset sliding window, and the calculation result is displayed to the front-end interface; The specific steps of step S3 are as follows: S31. Real-time monitoring of device data frequency and current processor load, and calculating window size according to device data frequency, and reducing the number of parallel processing threads in the sliding window according to a preset manner when the current processor load is greater than a set threshold; Specifically, the sliding window size is dynamically calculated according to the device data frequency , and the calculation formula is:

[0031] Among them, is the reference data frequency (for example, the value is 1 Hz), is the reference window size, (for example, the value of 60 corresponds to 1 minute of data points), is the upward rounding function; S32. Constructing a time series database, storing the real-time received device energy consumption data; S33. According to the flow processing mode, using the calculated window size for sliding, and using the calculated parallel processing threads for data aggregation calculation in each window; S34. Using XML Schema description language to build a data display template, and receiving user configuration of the data display template to generate a data display visualization interface instance; S35. The aggregation calculation result is converted into Excel / CSV format by using the flow processing mode, and is displayed in the corresponding data display visualization interface instance; Exemplarily, taking the daily energy consumption analysis of the electricity meter of room 402 of building A as an example, combining with the energy statistics function, the specific implementation process is as follows: Data collection and storage: the system collects the three-phase voltage (Vab, Vbc, Vca), three-phase current (Ia, Ib, Ic) and active power data of the electricity meter in real time through a multi-protocol adapter, generates a data record every 5 seconds, and stores it in the time series database. The data retention period is set to 90 days; Sliding window parameter calculation: real-time monitoring of device data frequency f=2Hz (1 data every 5 seconds, i.e. 0.2 data per second, here According to the window size calculation formula = 1 Hz, = 60), the window size is calculated (i.e. 2 minutes for a sliding window); Dynamic thread adjustment: when the processor load is monitored to reach 85% (set threshold 80%), the system automatically reduces the number of parallel processing threads in the sliding window from 10 to 5, avoiding resource overload and causing calculation delay; Aggregated calculation and visual display: the data aggregation unit aggregates the energy consumption data in 2-minute windows, calculates the average power, maximum power and total energy consumption in each window, and uses XML Schema to build a daily energy consumption trend display template through the report management unit. The energy consumption interface is displayed in a stacked column chart, with an energy consumption peak of 35 kWh during the 9:00-12:00 period. At the same time, the data export service unit supports converting the aggregated results into Excel format, and users can download the report containing detailed data for each window; Analysis effect: through dynamic sliding window calculation, the energy consumption data aggregation efficiency is improved by 40% compared with fixed window, and the energy consumption peak period can be accurately captured, providing data support for subsequent peak-shifting power consumption suggestions, and realizing one-stop statistical analysis; S4. Identify the device state according to the collected device running state data, and synchronize the device state with the front-end interface in real time and manage the device firmware upgrade based on the device state; The specific steps of step S4 are as follows: S41. Store the device running state data in the form of key-value pairs, and use the WebSocket channel to synchronize the device running state data with the front-end interface in real time through copy-on-write; S42. Determine whether there is a new firmware version for the connected device, and if so, use the difference compression algorithm to generate a firmware upgrade package for the difference between the old firmware and the new firmware of the device, and issue it to the corresponding device for online firmware upgrade; S43. Access control the connected device according to the user role and the pre-set access rights, and the granularity of the access rights is the device point level; Exemplarily, taking the health management and firmware upgrade of the electricity meter in room 402 of building A of a certain commercial building as an example, the specific implementation process is as follows in combination with the device management function of the system: Device health degree calculation: the device health degree evaluation unit calculates based on the running data of the electricity meter in the past 7 days, wherein the total offline time of the device in the past 7 days = 2 hours, the total number of alarm triggers ​=3 times (2 times of voltage fluctuation alarm, 1 time of communication delay alarm), distance from last maintenance day =90 days, substitute into the health score calculation formula =5, =3, =0.5), the calculation result is =100-(5x2+3x3+0.5x90)=100-(10+9+45)=36, it is determined that the equipment state is a failure risk; Health warning and maintenance suggestion pushing: the system displays the health score "36" on the equipment detail interface and marks it as red, and pushes the maintenance suggestion "it is suggested to check the voltage module and communication link immediately, and arrange maintenance within 3 days" to the operation and maintenance personnel account; Firmware upgrade detection and execution: the equipment upgrade management unit regularly detects the meter firmware version, finds that the current version V1.2 has a known problem of "voltage acquisition accuracy deviation", and there is a new firmware version V1.3 released; the system uses a differential compression algorithm to compare the differences between V1.2 and V1.3, and generates an upgrade package of only 200KB (the size of the complete firmware package is 1.5MB, and the compression rate is 86.7%); through the WebSocket channel, the upgrade instruction is issued to the meter, and the breakpoint resume transmission technology is used in the upgrade process to avoid network interruption causing upgrade failure; Access control: based on user roles, configure access permissions, for example, the "operation and maintenance personnel" role can only view the running data of the meter, receive health warnings, and perform firmware upgrade operations, and cannot modify the meter reading rules; the "administrator" role has full permissions and can configure access permissions at the device point level (such as limiting ordinary users to only view total active power and not view current details); Implementation effect: equipment health degree evaluation helps to discover hidden faults in advance, and after firmware upgrade, the voltage acquisition accuracy of the meter is improved from ±2% to ±0.5%, and the upgrade process does not affect normal data acquisition, realizing the performance guarantee requirements of high safety and strong stability; S5. Configure alarm rules, and when the device running state data meets the trigger condition in the alarm rule, generate an alarm event, and alarm and notify according to the alarm mode set in the corresponding alarm rule; The specific steps of step S5 are as follows: S51. Configure the trigger condition, alarm level, notification channel and operation and maintenance linkage switch of the alarm rule; the trigger condition is set based on the value of the Internet of Things point in the device running state data; S52. Real-time monitoring of equipment state, comparing the equipment state with the configured alarm rules, and when the equipment state meets the trigger condition of any alarm rule, generating an alarm event; ​S53. According to the alarm level corresponding to the alarm event and the pre-configured notification channel, send the alarm information to the specified user or user group; S54. Determine whether the operation and maintenance linkage switch in the alarm rule corresponding to the triggered alarm event is opened; If it is opened, go to step S6; If it is not opened, end; Exemplarily, taking the voltage overrun alarm of the electric meter in room 402 of building A as an example, combined with the alarm management function of the system, the specific implementation process is as follows: Alarm rule configuration: In the alarm center, configure the rule name as “402 room electric meter voltage overrun alarm”, set the trigger condition as “voltage > 240V” (based on the electric meter IoT point “three-phase voltage-Vab”), select the alarm level as “emergency”, check the notification channels “SMS + internal message + WeChat public number”, and open the “operation and maintenance linkage switch”; Alarm triggering and judgment: The system monitors the Vab voltage data of the electric meter in real time, and when it detects that the voltage reaches 245V (exceeding the 240V threshold), the alarm triggering and judgment unit immediately generates an alarm event, records the event ID, trigger time (such as “2024-05-20 14:30:22”), current voltage value and device DN code; Multi-channel alarm notification: The alarm notification unit sends information according to the configured channels, sends an SMS “emergency alarm: 402 room electric meter triggered voltage overrun alarm at 14:30, current voltage 245V, please handle in time” to the operation and maintenance personnel's mobile phone; At the same time, the system internal message and WeChat public number push the same content alarm information, ensuring that the operation and maintenance personnel know quickly; Abnormal influence prediction: Combined with the device network state interface function, if the electric meter is offline for more than 30 minutes after the alarm is triggered, the system automatically pushes “device offline influence warning” to the operation and maintenance personnel, prompts “402 room electric meter has been offline for 35 minutes, which will affect the meter reading task at 18:00 today, please repair first”, and marks the electric meter as “to be repaired” in the my device list; Implementation effect: The alarm from triggering to notification takes <10 seconds, multi-channel notification ensures no omission, the opening of the operation and maintenance linkage switch lays the foundation for subsequent work order generation, and realizes the function of intelligent prediction and diagnosis; S6. When the operation and maintenance linkage is enabled in the alarm rule triggered by the alarm event, combined with the pre-set correlation between devices, use the abnormal propagation algorithm to analyze the root cause of the fault, generate an operation and maintenance work order and distribute it to the specified operation and maintenance personnel; The specific steps of step S6 are as follows: S61. Receiving an alarm event and device state data related to the alarm event, combining a preset inter-device association relationship to construct a device association topology graph, calculating the fault influence weight of each device node, and generating a fault diagnosis report when the weight exceeds a dynamic threshold; the dynamic threshold is adjusted according to the historical work order accuracy at a set frequency; Specifically, the dynamic threshold The adjustment formula is:

[0032] Among them, is the weight threshold after the new round of adjustment, is the weight threshold before the last round of adjustment, is the last adjusted work order accuracy calculated based on the historical work order implementation effect, is the work order objective accuracy, for example, set to 0.95, is the learning rate, for example, set to 0.1; S62. According to the fault diagnosis report, generate an operation and maintenance work order containing fault positioning and repair suggestions; S63. Based on the skill label, geographical location and historical processing success rate of the operation and maintenance personnel, use the KNN algorithm to match the optimal operation and maintenance personnel for the operation and maintenance work order; Specifically, the formula for matching the optimal operation and maintenance personnel for the operation and maintenance work order using the KNN algorithm is as follows:

[0033] Among them, is the comprehensive distance between the operation and maintenance personnel and the work order requirements, the smaller the value, the higher the matching degree; is the skill label matching degree, the value is between 0-1, 1 indicates complete matching; is the normalized geographical location distance, is the historical processing success rate, 、 、 is the weight coefficient, satisfying , for example takes 0.5, takes 0.3, takes 0.2; S64. Distribute the operation and maintenance work order to the matched operation and maintenance personnel, start the processing countdown at the same time, and automatically upgrade the next level of responsibility person to push the operation and maintenance work order when it is not processed in time; Exemplarily, for the above voltage overrun alarm, combined with the operation and maintenance management function of the system, the specific implementation process is as follows: Fault Root Cause Analysis: The intelligent diagnostic unit receives alarm events and meter status data, and combines them with a preset "meter-distribution box-power supply line" topology diagram. It then uses an anomaly propagation algorithm to calculate the fault impact weight of each node. Specifically, the weight of the meter node in room 402 is 0.92, the distribution box node is 0.35, and the power supply line node is 0.28. The dynamic threshold is calculated using the following formula.

[0034] Depend on =0.8, =0.1, =0.95, =0.9, therefore =0.8+0.1×(0.95-0.9)=0.85; Since the weight of the meter node is 0.92>0.85, a fault diagnosis report is generated, the root cause is located as "the voltage acquisition module of the meter in room 402 is abnormal, not a problem with the upstream power supply line", and the repair suggestion is "replace the voltage sensor and check the wiring terminals". Maintenance work order generation: The work order generation unit creates maintenance work orders based on the diagnostic report. The work order content includes the faulty equipment (electricity meter in room 402), fault type (voltage exceeding the upper limit), fault location (electricity meter internal voltage module), repair suggestions and urgency level (urgent, processing time limit of 4 hours), and automatically links to the electricity meter's historical maintenance record (last maintenance time: 2024-02-18). Optimal Operations and Maintenance Personnel Matching: The operations and maintenance personnel matching unit selects personnel based on the KNN algorithm. The information of the three operations and maintenance personnel in the system is as follows: Maintenance Personnel A: Skill tag "Meter Repair" match score 0.95, geographical distance from Room 402 1.2km (after normalization) =0.12), historical processing success rate 0.92; Maintenance Personnel B: Skill tag "Meter Repair" match score 0.8, geographical distance from Room 402 3.5km (after normalization) =0.35), historical processing success rate 0.88; Maintenance Personnel C: Skill tag "Meter Repair" match score 0.75, geographical distance from Room 402 2.8km (after normalization) =0.28), historical processing success rate 0.9; Substitute into the formula ( =0.5、 =0.3、 =0.2), the calculation yields: Maintenance personnel A: 0.5x(1-0.95)+0.3x0.12+0.2x(1-0.92)=0.025+0.036+0.016=0.077; Operator B: 0.5x(1-0.8)+0.3x0.35+0.2x(1-0.88)=0.1+0.105+0.024=0.229; Operator C: 0.5x(1-0.75)+0.3x0.28+0.2x(1-0.9)=0.125+0.084+0.02=0.229; Select The operator A with the smallest score as the optimal assignment object; Work order tracking and upgrading: the work order assignment and tracking unit pushes the work order to the mobile terminal of the operator A, and starts a 4-hour processing countdown at the same time; if the operator A has not confirmed the processing at 3 hours and 50 minutes, the system automatically sends a "work order will be overdue" reminder; if it is overdue (not processed for 4 hours), the work order is automatically upgraded to the operator supervisor, and the supervisor reassigns personnel or coordinates resources; Implementation effect: from alarm triggering to work order assignment, the whole process takes <2 minutes, the operator A arrives at the scene 15 minutes after receiving the work order, and completes the fault repair in 30 minutes, with a work order processing accuracy rate of 100%, forming an "alarm-diagnosis-work order-repair" operation closed loop.

[0035] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An industrial internet platform system, characterized in that, include: The data acquisition task configuration and device access module is used to configure and execute data acquisition tasks, and synchronously acquire device energy consumption data and device operating status data from the target device through a multi-protocol adapter. The energy consumption data analysis module is used to store the collected equipment energy consumption data into the database according to time sequence, perform aggregation calculations using a preset sliding window, and then display the calculation results to the front-end interface. The device management module is used to identify the device status based on the collected device operating status data and synchronize it to the front-end interface, as well as to manage device firmware upgrades; The alarm management module is used to configure alarm rules and generate alarm events when the device operating status data meets the triggering conditions in the alarm rules, and to issue alarms according to the alarm methods set in the corresponding alarm rules. The operation and maintenance service module is used to perform root cause analysis of faults by combining the preset relationships between devices and using an anomaly propagation algorithm when the alarm rules triggered by the alarm event enable operation and maintenance linkage, generate operation and maintenance work orders, and distribute the operation and maintenance work orders in a preset manner.

2. The industrial internet platform system according to claim 1, characterized in that, The data acquisition task configuration and device access module includes: The task configuration unit configures the task source, execution type, interval time, and managed IoT points for the data collection tasks; the data collection tasks include energy consumption data collection tasks and operating status collection tasks. The Modbus transparent transmission parsing unit converts the RS485 signal uploaded by the device into JSON format device energy consumption data and device operating status data, which are then provided to the device management module and data analysis module. The protocol conversion unit is used to interact with the communication modules of heterogeneous devices through the AT command interface, automatically select the optimal communication protocol from the protocol library based on the handshake response time with the corresponding device, and provide the interaction data to the device management module and the data analysis module; the protocol library is configured with MQTT protocol, TCP protocol and HTTP protocol.

3. The industrial internet platform system according to claim 2, characterized in that, The data acquisition task configuration and device access module also includes: The device manual addition unit responds to the user's addition request and adds the corresponding device based on the user's input of basic device information and device fingerprint information; the basic device information includes the device MAC address and a preset key; the fingerprint information includes the manufacturer ID and hardware version number; The QR code binding unit is used to extract basic device information by scanning the QR code configured on the device and to verify the basic device information. The access service unit is used to match the device fingerprint information entered by the user or extracted from the device's QR code with a preset protocol template, and then use the matched protocol template to establish a shadow channel for the device and store the corresponding device data.

4. The industrial internet platform system according to claim 1, characterized in that, The energy consumption data analysis module includes: The data storage unit is used to build a time-series database to store real-time received device energy consumption data; The data aggregation unit is used to perform aggregation calculations on the stored device energy consumption data in a streaming manner, using a preset window size for sliding processing. The report management unit is used to construct data display templates using the XML Schema description language, receive user configurations for the data display templates, and generate data display visualization interface instances. The data export service unit is used to convert the aggregated calculation results into Excel / CSV format using stream processing, and then display them in the corresponding data display and visualization interface instance.

5. The industrial internet platform system according to claim 3, characterized in that, The device management module includes: The device shadow service unit is used to store device operating status data in key-value pairs and synchronize the device operating status data with the front-end interface in real time using the WebSocket channel via copy-on-write. The device upgrade management unit is used to generate a firmware upgrade package by using a differential compression algorithm to compare the differences between the old firmware and the new firmware of the device, and then distribute it to the corresponding device to realize online firmware upgrade of the device. The device access control unit is used to control device access based on user roles and according to preset access permissions, wherein the granularity of the access permissions is at the device point level. The device map service unit is used to extract location data from the device operation status data uploaded by the device, determine the geographical location, and then render the device on the map using preset icons through the integrated map service API. The equipment health assessment unit is used to calculate the equipment health score based on the equipment's online time, alarm frequency, and maintenance records, and to push maintenance suggestions.

6. The industrial internet platform system according to claim 1, characterized in that, The alarm management module includes: The alarm rule configuration unit is used to configure the triggering conditions, alarm level, notification channel, and operation and maintenance linkage switch of alarm rules; the triggering conditions are set based on the IoT point values ​​in the device operation status data; The alarm triggering and judgment unit is used to receive the device status identified from the device operating status data in real time, compare it with the alarm rules, and generate an alarm event when the triggering conditions are met. The alarm notification unit is used to send alarm information to designated users or user groups according to the alarm level corresponding to the alarm event and the configured notification channel; The operation and maintenance linkage unit is used to send the alarm event and related equipment status to the operation and maintenance service module when the operation and maintenance linkage switch is enabled in the alarm rule corresponding to the triggered alarm event, so as to trigger the generation of the operation and maintenance work order.

7. The industrial internet platform system according to claim 6, characterized in that, The operation and maintenance service module includes: The intelligent diagnostic unit is used to receive alarm events and related device status data, combine them with a device association topology diagram constructed by preset device association relationships, calculate the fault impact weight of each device node, and generate a fault diagnosis report when the weight exceeds a dynamic threshold; the dynamic threshold is adjusted according to the historical work order accuracy at a set frequency; The work order generation unit is used to generate maintenance work orders containing fault location and repair suggestions based on the fault diagnosis report; The operations and maintenance personnel matching unit is used to match the best operations and maintenance personnel for operations and maintenance work orders based on the personnel's skill tags, geographical location and historical processing success rate using the KNN algorithm; The work order dispatch and tracking unit is used to dispatch the maintenance work order to the matching maintenance personnel, start a processing countdown, and automatically escalate the work order to the next higher level of responsible person if it is not processed within the time limit.

8. A method of using the industrial internet platform system according to any one of claims 1-7, characterized in that, Includes the following steps: S1. Configure the data acquisition task, define the target device, the set of acquisition points, and the execution rules; S2. Execute the data acquisition task and synchronously collect device energy consumption data and device operating status data from the target device through the multi-protocol adapter; S3. Store the collected device energy consumption data into the database according to time sequence, perform aggregation calculations using a preset sliding window, and then display the calculation results to the front-end interface; S4. Identify the device status based on the collected device operation status data, synchronize the device status with the front-end interface in real time, and manage device firmware upgrades based on the device status; S5. Configure alarm rules, and when the device operating status data meets the triggering conditions in the alarm rules, generate an alarm event and send an alarm notification according to the alarm method set in the corresponding alarm rules; S6. When the alarm rule triggered by the alarm event enables operation and maintenance linkage, the root cause analysis of the fault is performed using the anomaly propagation algorithm in combination with the preset inter-device relationship, and an operation and maintenance work order is generated and dispatched to the designated operation and maintenance personnel.

9. The method of use according to claim 8, characterized in that, In step S1, the task source, execution type, interval time, and IoT points to be collected for the data collection task are configured. The specific steps of step S2 are as follows: S21. Construct a multi-protocol adapter; S22. Respond to the user's request to add the device, add the corresponding access device based on the user's input of basic device information and device fingerprint information, or extract the basic device information by scanning the QR code configured on the access device and verify the basic device information; S23. Determine whether the access device is an RS485 signal transmission device through the multi-protocol adapter; If so, proceed to step S24; If not, proceed to step S25; S24. Parse the data collected by the access device using the Modbus protocol and convert it into JSON format device energy consumption data and device operating status data, then proceed to step S26; S25. Interact with the communication module of the access device through the AT command interface, and automatically select the optimal communication protocol from the protocol library to obtain device energy consumption data and device operating status data based on the handshake response time with the access device; S26. Match the device fingerprint information entered by the user or extracted from the QR code of the access device with the preset protocol template, and then use the matched protocol template to establish a shadow channel for the access device and store the corresponding access device data. The specific steps of step S3 are as follows: S31. Monitor the device data frequency and the current processor load in real time, calculate the window size based on the device data frequency, and reduce the number of parallel processing threads in the sliding window in a preset manner when the current processor load exceeds a set threshold. S32. Construct a time-series database to store the real-time received device energy consumption data; S33. The stored device energy consumption data is processed in a streaming manner, using a calculated window size for sliding, and within each window, a calculated parallel processing thread is used to perform data aggregation calculations; S34. Use XML Schema description language to construct data display templates, receive user configurations for data display templates, and generate data display visualization interface instances; S35. Convert the aggregation calculation results into Excel / CSV format using stream processing, and display them in the corresponding data display visualization interface example.

10. The method of use according to claim 9, characterized in that, The specific steps of step S4 are as follows: S41. Store device operating status data in key-value pairs and use the WebSocket channel to synchronize the device operating status data with the front-end interface in real time via copy-on-write. S42. Determine whether a new firmware version exists on the connected device. If it does, use a differential compression algorithm to generate a firmware upgrade package by comparing the differences between the old firmware and the new firmware of the device, and send it to the corresponding device to perform an online firmware upgrade. S43. Access control is performed on access devices based on user roles and preset access permissions, wherein the granularity of the access permissions is at the device point level; The specific steps of step S5 are as follows: S51. Configure the alarm rule's trigger conditions, alarm level, notification channel, and operation and maintenance linkage switch; the trigger conditions are set based on the IoT point values ​​in the device's operating status data; S52. Monitor the device status in real time, compare the device status with the configured alarm rules, and generate an alarm event when the device status meets the triggering condition of any alarm rule; S53. Based on the alarm level corresponding to the alarm event and the pre-configured notification channel, send alarm information to the specified user or user group; S54. Determine whether the operation and maintenance linkage switch is enabled in the alarm rule corresponding to the triggered alarm event; If enabled, proceed to step S6; If not enabled, then the process ends; The specific steps of step S6 are as follows: S61. Receive alarm events and device status data related to alarm events, combine them with the device association topology diagram constructed by the preset device association relationship, calculate the fault impact weight of each device node, and generate a fault diagnosis report when the weight exceeds the dynamic threshold. The dynamic threshold is adjusted at a set frequency based on the accuracy of historical work orders; S62. Based on the fault diagnosis report, generate an operation and maintenance work order that includes fault location and repair suggestions; S63. Based on the skill tags, geographical location, and historical processing success rate of maintenance personnel, use the KNN algorithm to match the optimal maintenance personnel for maintenance work orders; S64. Dispatch the maintenance work order to the matching maintenance personnel, start the processing countdown, and automatically escalate the work order to the next higher level of responsible person if it is not processed within the time limit.

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