An Automated Monitoring Method for a Scheduling System Based on Add-on Services

Abstract

This invention provides an automated monitoring method for a scheduling system based on an external service, belonging to the field of local device monitoring technology. It utilizes an external server-driven image recognition module to perform spatial correlation comparison between interface metadata templates and real-time running interface data to generate a real-time state vector. After comparing the real-time state vector with preset rules to output real-time monitoring alarms, it backtracks to locate related operation logs. The related operation logs are then used to match similar operation patterns in an error knowledge base to locate and invoke related handling solutions. The related handling solutions and real-time monitoring alarms are packaged and sent to the scheduling host to drive the scheduling system to execute adaptive operations. This solves the technical problem of existing technologies where scheduling system fault risk monitoring and alarm and risk repair are highly dependent on manual intervention, leading to high scheduling system operation and maintenance costs. It achieves a comprehensive improvement in the level of automated monitoring of the scheduling system, significantly reduces the need for manual intervention, and achieves highly reliable unmanned operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of local equipment monitoring technology, and in particular to an automated monitoring method for a scheduling system based on add-on services. Background Technology

[0002] In scheduling system operation and maintenance scenarios, the high dependence of existing technologies on manual monitoring and handling has become a chronic problem in the industry.

[0003] Maintenance personnel need to continuously poll the system interface to identify fault risks. This model has inherent flaws: fault detection is severely delayed compared to the actual occurrence time, and transient anomalies (such as millisecond-level link interruptions) are frequently missed due to insufficient granularity of manual observation; the alarm decision-making process is constrained by differences in personnel experience, threshold determination is highly subjective, false alarms and missed alarms are common, and secondary operational risks arise; the handling process lacks standardized guidance, relies on manual backtracking of historical cases to match handling solutions, and the response chain is often broken due to multi-system switching and coordination delays.

[0004] Even more challenging is the requirement for 24 / 7 on-call service, which forces a single dispatch center to employ multiple dedicated maintenance personnel, with labor costs accounting for the majority of the total maintenance budget. This heavily manual intervention model not only restricts the timeliness of fault self-healing but also severely hinders the evolution of the dispatch system towards intelligence and automation, making it particularly difficult to adapt to the stringent requirements of minute-level fault isolation in fields such as power and communications.

[0005] In summary, existing technologies suffer from the technical problem of high operational and maintenance costs due to the heavy reliance on manual intervention for fault risk monitoring, alarming, and risk repair in scheduling systems. Summary of the Invention

[0006] This invention provides an automated monitoring method for a scheduling system based on add-on services, which addresses the technical problem that existing technologies rely heavily on manual monitoring, alarming, and risk repair for scheduling system failures, resulting in high maintenance costs.

[0007] In view of the above problems, the present invention provides an automated monitoring method for a scheduling system based on add-on services.

[0008] This invention provides an automated monitoring method for a scheduling system based on an add-on service. The method includes: after the scheduling host starts, the add-on server sends a screenshot command to the scheduling host with a screenshot time window as a constraint to obtain real-time running interface data of the scheduling system; the add-on server drives an image recognition module to perform spatial correlation comparison between a pre-updated interface metadata template and the real-time running interface data to generate a real-time state vector; the real-time state vector is compared with preset rules to output a real-time monitoring alarm; historical operation backtracking is performed starting from the output time of the real-time state vector to locate the associated operation log; similar operation patterns are matched in an error knowledge base using the associated operation log to locate and invoke the associated handling solution; the associated handling solution and the real-time monitoring alarm are packaged and sent to the scheduling host to drive the scheduling system to perform adaptive operations.

[0009] In one implementation, the following processing is also performed:

[0010] When the scheduling host is initialized, the external server is activated; the external server captures the static layout information of the scheduling system from the scheduling host through the USB interface; after the external server updates the interface metadata template with the static layout information, it constructs a timed screenshot task, wherein the timed screenshot task has the screenshot time window.

[0011] In one implementation, the external server-driven image recognition module performs spatial correlation comparison between the pre-updated interface metadata template and the real-time running interface data to generate a real-time state vector, and also performs the following processing:

[0012] The image recognition module performs spatial registration and alignment of the interface metadata template and real-time running interface data based on preset salient feature points of the scheduling system interface elements, and outputs a registration-aligned dual interface; performs hierarchical state recognition on the registration-aligned dual interface, and outputs hierarchical state recognition results; performs state vector structured encapsulation on the hierarchical state recognition results, and outputs the real-time state vector.

[0013] In one implementation, hierarchical state recognition is performed on the registered and aligned dual interfaces, and the hierarchical state recognition result is output. The following processing is also performed:

[0014] Based on the scheduling system interface elements, the target control area, target character display area, and alarm element display area are located. These areas are then projected onto the registration and alignment dual interfaces to decompose the control status dual interface, character display dual interface, and alarm element dual interface. The control status dual interface, character display dual interface, and alarm element dual interface are then mapped to the control status recognition channel, numerical recognition channel, and alarm recognition channel of the image recognition module, respectively. Multi-channel parallel status recognition is performed, and the hierarchical status recognition results are output. These hierarchical status recognition results include control status recognition results, numerical character recognition results, and alarm identifier recognition results.

[0015] In one implementation, the following processing is also performed:

[0016] In the control state recognition channel, the visual state of the dual interfaces of the control state is analyzed based on RGB values, and the control state recognition result is output. In the numerical recognition channel, after extracting numerical information from the dual interfaces of the character screen display, a numerical information consistency judgment is performed, and the numerical character recognition result is output. In the alarm recognition channel, after recognizing the alarm level dual information of the dual interfaces of the alarm element, the alarm level deviation is calculated, and the alarm identifier recognition result is output.

[0017] In one implementation, the real-time state vector is compared with a preset rule, a real-time monitoring alarm is output, and the following processing is performed:

[0018] Extract the vector features of the real-time state vector and perform hierarchical rule matching to obtain multi-dimensional state alarm rules; use the multi-dimensional state alarm rules as preset rules to compare with the real-time state vector to identify real-time state deviation and output the real-time monitoring alarm.

[0019] In one implementation, the associated operation log is used to perform similar operation pattern matching in the error knowledge base to locate and invoke the associated handling solution. Afterwards, the following processing is also performed:

[0020] The execution of the associated handling plan is tracked, and the variable handling plan is output; the variable handling plan and the associated operation log are associated and stored in the error knowledge base, and the handling plan data of the error knowledge base is updated.

[0021] In one implementation, the scheduling host establishes a point-to-point physical connection with the external server via the USB protocol, and the external server is deployed on a local edge computing unit.

[0022] In one implementation, the following processing is also performed:

[0023] The external server performs local alarm record backtracking to obtain short-term alarm records; after calculating and outputting the periodic alarm average based on the short-term alarm records, 1 / P of the periodic alarm average is used as the screenshot time window, and the timed screenshot task is constructed based on the screenshot time window.

[0024] Compared with the prior art, the technical solution provided in this invention has at least the following technical effects or advantages:

[0025] An external server-driven image recognition module spatially correlates and compares interface metadata templates and real-time running interface data to generate real-time status vectors. After comparing the real-time status vectors with preset rules and outputting real-time monitoring alarms, the system traces back to the associated operation logs. The associated operation logs are then used to match similar operation patterns in an error knowledge base to locate and invoke associated handling solutions. The associated handling solutions and real-time monitoring alarms are packaged and sent to the scheduling host to drive the scheduling system to execute adaptive operations. This achieves autonomous fault perception, accurate decision-making, and adaptive repair of the scheduling system, comprehensively improving the level of automated monitoring, significantly reducing the need for manual intervention, and achieving highly reliable unmanned operation and maintenance. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This invention provides a schematic diagram of an automated monitoring method for a scheduling system based on add-on services.

[0028] Figure 2 The diagram illustrates the process of generating real-time status vectors in an automated monitoring method for a scheduling system based on add-on services provided by this invention. Detailed Implementation

[0029] This invention provides an automated monitoring method for a scheduling system based on add-on services, which addresses the technical problem that existing technologies rely heavily on manual monitoring, alarming, and risk repair for scheduling system failures, resulting in high maintenance costs.

[0030] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0031] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0032] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0033] An embodiment of the present invention provides a flowchart of an automated monitoring method for a scheduling system based on add-on services. (See also...) Figure 1 The method includes:

[0034] Step A100: After the scheduling host starts, the external server sends a screenshot command to the scheduling host with the screenshot time window as a constraint, so as to obtain the real-time running interface data of the scheduling system.

[0035] In one implementation, the scheduling host establishes a point-to-point physical connection with the external server via the USB protocol, and the external server is deployed on a local edge computing unit.

[0036] Specifically, in this embodiment, the external server establishes a point-to-point physical direct connection (non-network transmission) with the scheduling host via the USB protocol, forming a dedicated data channel without relays. Simultaneously, the external server is deployed on a local edge computing unit (such as a field industrial control computer), enabling the screenshot command sending, data return, and subsequent image recognition (step A200) in step A100 to be completed locally. This reduces transmission latency, accelerates response speed, comprehensively improves the automation monitoring level of the scheduling system, and meets the needs of the communication field.

[0037] Based on this physical architecture, in this embodiment, after the scheduling host completes system startup, the external server sends a screenshot command to the scheduling host via USB connection according to the preset screenshot time window (the period is dynamically adjusted by the alarm frequency). The screenshot command triggers the scheduling host to capture the real-time interface data (including control status, values ​​and alarm elements) of the scheduling system it is running, and transmits the data back via USB interface.

[0038] Step A200: The external server driver image recognition module performs spatial correlation comparison between the pre-updated interface metadata template and the real-time running interface data to generate a real-time state vector.

[0039] In one implementation, see Figure 2 The external server-driven image recognition module performs spatial correlation comparison between the pre-updated interface metadata template and the real-time running interface data to generate a real-time state vector. Method step A200 further includes:

[0040] Step A210: The image recognition module performs spatial registration and alignment of the interface metadata template and the real-time running interface data based on the preset significant feature points of the scheduling system interface elements, and outputs a registration-aligned dual interface.

[0041] Step A220: Perform hierarchical state recognition on the registered and aligned dual interfaces and output the hierarchical state recognition results.

[0042] Step A230: Perform state vector structured encapsulation on the hierarchical state recognition result and output the real-time state vector.

[0043] In one implementation, hierarchical state recognition is performed on the registered and aligned dual interfaces, and the hierarchical state recognition result is output. Method step A220 further includes:

[0044] Step A221: Locate the target control area, target character display area, and alarm graphic element display area based on the scheduling system interface elements.

[0045] Step A222: By projecting the target control area, target character display area, and alarm element display area onto the registration and alignment dual interface respectively, the control status dual interface, character screen display dual interface, and alarm element dual interface are decomposed.

[0046] Step A223: Map the dual interfaces of control status, dual interfaces of character display, and dual interfaces of alarm graphic elements to the control status recognition channel, numerical recognition channel, and alarm recognition channel of the image recognition module, respectively, perform multi-channel parallel status recognition, and output the hierarchical status recognition result, wherein the hierarchical status recognition result includes control status recognition result, numerical character recognition result, and alarm identifier recognition result.

[0047] In one implementation, the method further includes:

[0048] Step A223-a: In the control state recognition channel, perform visual state analysis of the dual interface of the control state based on RGB values, and output the control state recognition result.

[0049] Step A223-b: In the numerical recognition channel, after extracting numerical information from the dual character display interfaces, a numerical information consistency judgment is performed, and the numerical character recognition result is output.

[0050] Step A223-c: In the alarm identification channel, after identifying the alarm level dual information of the dual interface of the alarm element, the alarm level deviation is calculated, and the alarm identifier identification result is output.

[0051] Specifically, the image recognition module described in this embodiment has both image alignment and multi-channel collaborative comparison functions.

[0052] First, the image recognition module spatially registers and aligns the dynamically updated interface metadata template (the baseline layout that adapts to interface changes) with the real-time running interface data, based on the significant feature points of the scheduling system interface defined in the initialization phase (such as key coordinates of button corners, dashboard center, etc.).

[0053] Specifically, by calculating the affine transformation matrix (translation / rotation / scaling parameters), geometric differences caused by window displacement or scaling are eliminated, outputting a pixel-aligned registered dual interface (the coordinates of the template interface and the real-time interface are completely mapped). Step A210 is the foundation for subsequent layered recognition, ensuring spatial consistency of state extraction, and fundamentally solving the positioning drift problem caused by dynamic changes in the interface in non-intrusive monitoring.

[0054] Based on this, the image recognition module performs hierarchical state recognition on the registered and aligned dual interfaces and outputs the hierarchical state recognition results.

[0055] The specific technical implementation of the image recognition module performing hierarchical state recognition and outputting hierarchical state recognition results is as follows:

[0056] Based on the inherent layout characteristics of the scheduling system interface elements, the target control area (including operable elements such as buttons and switches), target character display area (carrying dynamically refreshed numerical information), and alarm graphic element display area (an icon area dedicated to status alerts) are located in the registration and alignment dual interface. The location is based on the spatial coordinate mapping of the interface metadata template stored in the initialization stage, which directly serves the physical decomposition of the subsequent hierarchical identification channel, ensuring that the status monitoring accurately focuses on the core interaction and status elements of the scheduling system, and supporting the reliability of non-intrusive monitoring.

[0057] By mapping and projecting the coordinate boundaries of the target control area, target character display area, and alarm element display area onto the registration and alignment dual interface, physically isolated control state dual interface (containing only the visual state of operating elements such as buttons / switches), character screen display dual interface (pure numerical area stripped of non-character interference), and alarm element dual interface (focusing on the independent recognition area of ​​alarm icons) are generated. This realizes the transformation from a hybrid interface to a function-focused interface, laying the input foundation for subsequent multi-channel parallel recognition and ensuring the accuracy and timeliness of the analysis of each state dimension.

[0058] The image recognition module's hierarchical state recognition relies on a parallel architecture of control state recognition channels, numerical recognition channels, and alarm recognition channels. It maps the decomposed control state dual interface, character screen display dual interface, and alarm graphic element dual interface to the corresponding channels, thereby achieving synchronous and independent processing of the three types of interfaces.

[0059] The hierarchical state recognition technology of the image recognition module, relying on the hardware parallel capabilities of the edge computing unit, reduces the state recognition time to 1 / 3 of that of serial processing, directly supporting the millisecond-level response requirements of the scheduling system.

[0060] The dual interfaces for control status, dual interfaces for character display, and dual interfaces for alarm elements are respectively mapped to the control status recognition channel, numerical recognition channel, and alarm recognition channel of the image recognition module to perform multi-channel parallel status recognition.

[0061] Specifically, in the control state recognition channel, the visual representation of the dual interface of the control state is analyzed based on the threshold of the RGB three-channel components (e.g., the red channel value exceeding the threshold is judged as an alarm state), the interface pixel features are converted into predefined machine status codes, and the control state recognition result is output.

[0062] In the numerical recognition channel, optical character recognition and logical verification are performed on the dual-interface character display: after extracting the numbers and unit symbols, the compatibility between the numerical values ​​and physical dimensions is verified (e.g., port traffic is not negative, CPU load is ≤100%), and structured numerical values ​​(value / unit / verification flag) with validity labels are output, and the numerical character recognition results are output.

[0063] In the alarm identification channel, by comparing the geometric differences between the dual interface of alarm elements and the metadata template (such as the rate of change of icon size and color space offset), the quantitative deviation value between the real-time alarm level and the preset level is calculated (deviation rate = |real-time level - template level| / template level × 100%), and the output result is supplemented with a deviation rating (such as deviation > 15% → low confidence alarm), which is used as the alarm identification result output.

[0064] The hierarchical status recognition results (control status, numerical characters, alarm identifiers) are encapsulated in a multi-dimensional structure and the real-time status vector is output.

[0065] This embodiment achieves millisecond-level non-intrusive scheduling system with accurate full-state perception, providing zero-disturbance, highly timely machine-readable input for adaptive decision-making.

[0066] Step A300: Compare the real-time state vector with the preset rules and output a real-time monitoring alarm.

[0067] In one implementation, the real-time state vector is compared with a preset rule, and a real-time monitoring alarm is output. The method step A300 further includes:

[0068] Step A310: Extract the vector features of the real-time state vector and perform hierarchical rule matching to obtain multi-dimensional state alarm rules.

[0069] Step A320: Use the multi-dimensional state alarm rule as a preset rule to compare with the real-time state vector to perform real-time state deviation hit identification, and output the real-time monitoring alarm.

[0070] Specifically, in this embodiment, the vector features of the real-time status vector are extracted to obtain the device node status, numerical attributes and alarm identifiers. These are then matched hierarchically with device-level threshold rules (such as port traffic limits), system-level association rules (such as primary and backup link mutual exclusion) and derived composite rules (historical fault extraction) in the rule base to output multi-dimensional status alarm rules bound to specific devices.

[0071] Based on a multi-dimensional alarm rule set, the system performs joint analysis on the control status codes, numerical attributes (including validity labels), and alarm identifiers (including deviation values) in the real-time status vector. The system dynamically adjusts the rule trigger threshold based on the alarm deviation value and outputs real-time monitoring alarms in a tiered manner, including but not limited to basic alarms, composite alarms, or manual verification warnings, in combination with the numerical validity labels and rule hit results. This achieves closed-loop alarm decision-making that is strictly aligned with the status vector structure.

[0072] Step A400: Starting from the output time of the real-time state vector, perform historical operation backtracking and locate the associated operation log.

[0073] It should be understood that if the real-time monitoring alarm is a non-empty set, it means that a fault has been detected. Based on this, it is necessary to further determine the associated handling plan.

[0074] Specifically, this embodiment uses the millisecond-level timestamp of the real-time state vector as a benchmark, traces back a preset time window (such as 5 minutes before the fault), extracts all operation records (including manual instructions and automatic operations) for the alarm device within that time period from the log library, accurately locates the operation log sequence (the associated operation log) that is strongly correlated with the current fault in time and space, and constructs the input basis for the fault decision chain.

[0075] Step A500: Use the associated operation log to perform similar operation pattern matching in the error knowledge base to locate and invoke the associated handling solution.

[0076] The associated operation logs are matched with the error knowledge base for similarity. Priority is given to searching historical handling cases with the same device type and alarm code. If there is no sufficient match, the search is expanded to cases of the same device family and similar alarm patterns. A reinforcement learning model is used to integrate operation sequence similarity, historical success rate and solution timeliness for dynamic scoring. The optimal associated handling solution and execution confidence label are output to complete the intelligent positioning of the associated handling solution.

[0077] Step A600: Package the associated handling plan and real-time monitoring alarms, and send them to the scheduling host to drive the scheduling system to perform adaptive operations.

[0078] The associated handling plan and real-time monitoring alarms are packaged into a structured operation package and transmitted directly to the scheduling host via USB protocol. This drives the scheduling system to perform adaptive operations (such as automatic port switching and load redistribution), completing a millisecond-level autonomous response from fault detection to closed-loop handling.

[0079] In one implementation, the associated operation log is used to perform similar operation pattern matching in the error knowledge base to locate the invocation of the associated handling solution. Then, method step A500 further includes:

[0080] Step A510: Perform execution tracking on the aforementioned associated handling scheme and output the variable handling scheme.

[0081] Step A520: Associate the variable handling scheme and the associated operation log and store them in the error knowledge base, and update the handling scheme data in the error knowledge base.

[0082] This embodiment tracks the entire process of the associated handling plan executed by the scheduling system. When the plan parameters or operation sequence are adjusted manually based on the on-site situation, the modified content and execution effect are captured in real time, and variable handling plan marked with manual optimization items and execution performance report are generated.

[0083] The variable handling plan (including manual tuning records) is bound to the original associated operation log according to the fault mode. After knowledge distillation to remove redundant operation steps, it is updated to the corresponding case set in the error knowledge base to achieve continuous evolution of the handling strategy.

[0084] In one implementation, the method further includes:

[0085] Step A101: When the scheduling host is initialized, the external server is activated.

[0086] Step A102: The external server captures the static layout information of the scheduling system from the scheduling host via the USB interface.

[0087] Step A103: After the external server updates the interface metadata template using the static layout information, it constructs a timed screenshot task, wherein the timed screenshot task has the screenshot time window.

[0088] Specifically, in this embodiment, during the initialization phase of the scheduling host system, the external server service process is automatically activated, establishing a point-to-point USB communication link with the scheduling host to provide underlying connection assurance for subsequent non-intrusive monitoring. The external server captures static layout information of the scheduling system interface (such as control coordinates, character area positions, and alarm icon sizes) through the USB interface, stores it as baseline metadata, and constructs an initial interface recognition template to support subsequent dynamic interface registration. After updating the interface metadata template using the static layout information, the external server synchronously creates periodic screenshot tasks, laying the foundation for the time-series framework of real-time status monitoring.

[0089] In one implementation, the method further includes:

[0090] Step A103-a: The external server performs local alarm record backtracking to obtain short-term alarm records.

[0091] Step A103-b: After calculating the average value of the output periodic alarms based on the short-term alarm records, 1 / P of the average value of the periodic alarms is used as the screenshot time window, and the timed screenshot task is constructed based on the screenshot time window.

[0092] Specifically, the timed screenshot task in this embodiment is a non-standard task, and the method for setting the timed screenshot task is as follows:

[0093] By using the external server to trace back recent alarm trigger records (such as data from the previous 24 hours) in local edge storage, the time distribution and frequency characteristics of alarm events are extracted to form a short-term alarm record set that reflects the real-time load status of the system.

[0094] Based on the average alarm value within a unit of time of short-term alarm records, the reciprocal of this average value (1 / P, where P is a configurable scaling factor) is dynamically calculated as the screenshot acquisition time window parameter. Based on this, a periodic screenshot task is constructed to achieve adaptive adjustment of the monitoring frequency according to the system load intensity.

[0095] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for automatically monitoring a dispatch system based on a plug-in service, characterized in that, Comprise: After the dispatch host starts, the external server sends a screenshot instruction to the dispatch host to obtain real-time running interface data of the dispatch system, with a screenshot time window as a constraint; The external server drives an image recognition module to perform spatial correlation comparison on the pre-updated interface metadata template and the real-time running interface data, to generate a real-time state vector; Compare the real-time state vector with a preset rule, and output a real-time monitoring alarm; Backtrack the history operation from the output time of the real-time state vector, and locate the associated operation log; Match the similar operation mode in the error knowledge base by using the associated operation log, to locate the call associated treatment scheme; Package the associated treatment scheme and the real-time monitoring alarm, and send them to the dispatch host to drive the dispatch system to perform adaptive operation; Wherein, the external server drives an image recognition module to perform spatial correlation comparison on the pre-updated interface metadata template and the real-time running interface data, to generate a real-time state vector, comprising: The image recognition module performs spatial registration alignment on the interface metadata template and the real-time running interface data based on the preset salient feature points of the dispatch system interface elements, and outputs a registration alignment double interface; Perform hierarchical state recognition on the registration alignment double interface, and output a hierarchical state recognition result; Perform state vector structure packaging on the hierarchical state recognition result, and output the real-time state vector; Perform hierarchical state recognition on the registration alignment double interface, and output a hierarchical state recognition result, comprising: Position the target control area, target character display area and alarm graphic display area based on the dispatch system interface elements; By projecting the target control area, target character display area and alarm graphic display area to the registration alignment double interface respectively, the control state double interface, character screen display double interface and alarm graphic double interface are obtained; Map the control state double interface, character screen display double interface and alarm graphic double interface to the control state recognition channel, numerical value recognition channel and alarm recognition channel of the image recognition module respectively, perform multi-channel parallel state recognition, and output the hierarchical state recognition result, wherein the hierarchical state recognition result includes control state recognition result, numerical value character recognition result and alarm identification recognition result.

2. The method of claim 1, wherein the method further comprises: Further comprise: Activate the external server when the dispatch host is initialized; The external server captures the static layout information of the dispatch system through the USB interface of the dispatch host; After the external server updates the interface metadata template with the static layout information, it constructs a timed screenshot task, wherein the timed screenshot task has the screenshot time window.

3. The method of claim 1, wherein the method further comprises: Further comprise: In the control state recognition channel, perform visual state analysis on the control state double interface based on RGB value, and output the control state recognition result; In the numerical value recognition channel, after performing numerical value information extraction on the character screen display double interface, perform numerical value information consistency judgment, and output the numerical value character recognition result; In the alarm identification channel, after identifying the alarm level double information of the alarm icon double interface, alarm level deviation calculation is performed, and the alarm identification result is output.

4. The method of claim 1, wherein the method further comprises: Comparing the real-time state vector with a preset rule, a real-time monitoring alarm is output, including: Extracting the vector features of the real-time state vector for hierarchical rule matching to obtain a multi-dimensional state alarm rule; The multi-dimensional state alarm rule is used as a preset rule to compare the real-time state vector for real-time state deviation hit identification, and the real-time monitoring alarm is output.

5. The method of claim 1, wherein the method further comprises: The correlation operation log is used for similar operation mode matching in the error knowledge base to locate a call correlation treatment scheme, and then, including: Performing execution tracking on the correlation treatment scheme to output a variable treatment scheme; The variable treatment scheme and the correlation operation log are associated and stored in the error knowledge base to update the treatment scheme data of the error knowledge base.

6. The method of claim 1, wherein the method further comprises: The scheduling host establishes a point-to-point physical connection with the external server through a USB protocol, and the external server is deployed in a local edge computing unit.

7. The method of claim 2, wherein the method further comprises: receiving a request for a service from the external device; and providing the requested service to the external device. Also including: The external server performs local alarm record backtracking to obtain short-term alarm records; After calculating and outputting a periodic alarm mean based on the short-term alarm records, 1 / P of the periodic alarm mean is used as the screenshot time window, and the timing screenshot task is constructed based on the screenshot time window.

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