Method, device and equipment for customizing device monitoring diagnosis model, and storage medium
By using a customized approach to equipment monitoring and diagnostic models, the problems of low model development efficiency and high operation and maintenance costs have been solved, achieving efficient and low-cost equipment monitoring and diagnostics.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 武汉钢铁有限公司
- Filing Date
- 2023-08-22
- Publication Date
- 2026-07-14
AI Technical Summary
The problem of low development efficiency and high operation and maintenance costs of equipment monitoring and diagnostic models.
Customization of device monitoring and diagnostic models can be achieved through metadata configuration, model service configuration, device configuration, and model deployment configuration, including setting model running parameters, parameter mapping, data processing configuration, and rule binding, and dynamically processing the collected data from access devices.
It improves the development efficiency of equipment monitoring and diagnostic models, reduces operation and maintenance costs, and enables accurate and efficient monitoring and diagnosis of equipment.
Smart Images
Figure CN117234768B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of computer technology, and in particular relates to a method, apparatus, device and storage medium for customizing equipment monitoring and diagnostic models. Background Technology
[0002] Traditional equipment maintenance methods mostly involve periodic inspections. While short intervals between inspections can ensure equipment safety to some extent, they are inefficient, especially for large equipment or units. Unnecessary maintenance wastes production time and reduces economic benefits, a phenomenon known as "over-maintenance." Conversely, excessively long intervals between inspections prevent timely assessments of equipment safety, increasing the risk of safety issues, a phenomenon known as "under-maintenance." Furthermore, periodic inspections require checking each component individually, lacking focus and wasting production time.
[0003] With the development of digital and intelligent technologies, equipment monitoring and diagnostic models have become an effective means of preventing equipment accidents and unplanned downtime. By extracting useful information from a large amount of equipment monitoring data, they enable real-time monitoring of equipment operating status during operation or with minimal disassembly, and predict and forecast the future status of the equipment. At the same time, the equipment monitoring and diagnostic models determine the location and cause of equipment failures, narrowing the scope of investigation, identifying the main repair components, saving maintenance time, and achieving precision and efficiency. This overcomes the over-maintenance and under-maintenance caused by traditional maintenance methods, ensuring the safe operation of equipment.
[0004] Currently, the development of equipment monitoring and diagnostic models faces challenges such as low model development efficiency and high operation and maintenance costs. Summary of the Invention
[0005] The embodiments of this application provide a method, apparatus, device, and storage medium for customizing equipment monitoring and diagnostic models, which can at least to some extent improve the development efficiency of equipment monitoring and diagnostic models and reduce the operation and maintenance costs of equipment monitoring and diagnostic models.
[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.
[0007] According to a first aspect of the embodiments of this application, a method for customizing a device monitoring and diagnostic model is provided, the method comprising:
[0008] Based on the data collected by the access devices, metadata is configured to obtain a metadata list;
[0009] Configure model services based on the model information of equipment early warning models and equipment diagnostic models in the model library to obtain a list of model services;
[0010] Based on the metadata list, the access device is configured to obtain device information;
[0011] The access device is bound to the target model in the model service list according to the device information, and the target model is configured for deployment according to the collected data, the fault feature extraction tool model in the model library and the metadata list. The model deployment configuration includes setting model running parameters, mapping model running parameters, configuring model running parameter data processing and binding rules.
[0012] Run the configured target model to monitor and diagnose the access device.
[0013] In some embodiments of this application, based on the foregoing scheme, the steps of setting the model running parameters and mapping the model running parameters include:
[0014] Obtain the basic data items of the collected data;
[0015] The model running parameters for the target model are determined based on the basic data items.
[0016] The model running parameters are mapped to the collected data so that the configured target model can obtain the collected data according to the model running parameters.
[0017] In some embodiments of this application, based on the foregoing scheme, the step of configuring the model running parameter data includes:
[0018] The feature extraction strategy for the collected data is determined based on the fault feature extraction tool model, so that the configured target model can extract features from the collected data according to the feature extraction strategy.
[0019] In some embodiments of this application, based on the foregoing scheme, the rule binding step includes:
[0020] A list of rules for device early warning diagnosis is generated based on the metadata list;
[0021] The rule list is bound to the target model so that the configured target model can perform early warning diagnosis on the access device according to the rule list.
[0022] In some embodiments of this application, based on the foregoing scheme, the metadata configuration based on the collected data from the access device includes:
[0023] Obtain the basic data items of the collected data from the access device;
[0024] Modify the names of the basic data items according to the preset specifications.
[0025] In some embodiments of this application, based on the foregoing scheme, configuring the access device according to the metadata list includes:
[0026] Based on the metadata list, the device type, device acquisition gateway, and data access policy of the access device are configured. The data access policy configuration includes the configuration of the accessed data items and the configuration of the data access method.
[0027] In some embodiments of this application, based on the foregoing scheme, the step of configuring the accessed data items includes:
[0028] Determine the accessed data items from the metadata list;
[0029] Configure the accessed data items.
[0030] According to a second aspect of the embodiments of this application, a device for customizing an equipment monitoring and diagnostic model is provided, the device comprising:
[0031] The metadata configuration unit is used to configure metadata based on the data collected by the access device and obtain a metadata list.
[0032] The model service configuration unit is used to configure model services based on the model information of the equipment early warning model and the equipment diagnostic model in the model library, and obtain a list of model services.
[0033] The device configuration unit is used to configure the access device according to the metadata list to obtain device information;
[0034] The model deployment configuration unit is used to bind the access device to the target model in the model service list according to the device information, and to configure the model deployment of the target model according to the collected data, the fault feature extraction tool model in the model library and the metadata list. The model deployment configuration includes model running parameter setting, model running parameter mapping, model running parameter data processing configuration and rule binding.
[0035] The model execution unit is used to run the configured target model to monitor and diagnose the access device.
[0036] According to a third aspect of the embodiments of this application, a customized device for a device monitoring and diagnostic model is provided, including a processor and a memory, wherein the memory stores computer program instructions that can be executed by the processor, and when the processor executes the computer program instructions, it implements the instructions of the method as described in any of the first aspects above.
[0037] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein computer program instructions are stored therein, the computer program instructions being loaded and executed by a processor to perform the operations performed by the method described in any of the first aspects above.
[0038] In this application, a metadata list is obtained by configuring metadata based on the collected data from the access device; a model service list is obtained by configuring model services based on the model information of the device early warning model and device diagnostic model in the model library; device information is obtained by configuring the access device based on the metadata list; the access device is bound to the target model in the model service list based on the device information; and the target model is deployed and configured based on the collected data, the fault feature extraction tool model in the model library, and the metadata list. The model deployment and configuration includes setting model running parameters, mapping model running parameters, configuring model running parameter data processing, and binding rules; the configured target model is then run to monitor and diagnose the access device. This solution achieves customization of the device monitoring and diagnostic model with minimal or no code, improving the development efficiency of the device monitoring and diagnostic model and reducing operation and maintenance costs.
[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:
[0041] Figure 1 A schematic diagram of the structure of a customized system for a device monitoring and diagnostic model in one embodiment is shown;
[0042] Figure 2 A flowchart illustrating a method for customizing a device monitoring and diagnostic model in one embodiment is shown.
[0043] Figure 3 A data flow diagram of a method for customizing a device monitoring and diagnostic model in one embodiment is shown;
[0044] Figure 4 A block diagram of a customized device for a device monitoring and diagnostic model is shown in one embodiment;
[0045] Figure 5A schematic diagram of a customized structure for a device monitoring and diagnostic model in one embodiment is shown. Detailed Implementation
[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0047] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.
[0048] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0049] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0050] To enable those skilled in the art to better understand this application, firstly, in conjunction with Figure 1 A brief description of the structure of the customized system for the equipment monitoring and diagnostic model involved in this application is provided.
[0051] See Figure 1 The diagram shows a structural schematic of a customized system for which the device monitoring and diagnostic model of the present application can be applied.
[0052] The customized system for equipment monitoring and diagnostic models includes a model library, a rule library, and a configuration center.
[0053] The model library mainly includes an equipment early warning model library, an equipment diagnostic model library, and a fault feature extraction tool model library. The equipment early warning model library contains multiple equipment early warning models, and the equipment diagnostic model library contains multiple equipment diagnostic models. Based on their application, the equipment early warning and diagnostic models can be categorized into general-purpose models, specialized models, and hybrid models. General-purpose models are developed specifically for a particular type of equipment, specialized models are developed for specific equipment, and hybrid models combine general-purpose and specialized models. The model parameters of both the equipment early warning and diagnostic models can be dynamically expanded, and during model operation, the collected data from the connected devices can be dynamically processed according to customized processing strategies.
[0054] The rule base is a collection of knowledge generated from human experience and data. It can include early warning rules such as equipment warning thresholds, diagnostic rules such as fault diagnosis logic, and can also set single-condition thresholds, multi-condition thresholds, and threshold duration / frequency. Users can customize and maintain the rules in the rule base through a visual rule editor.
[0055] The configuration center includes metadata configuration, model service configuration, device configuration, and model deployment configuration. Metadata configuration addresses the basic data items of the collected data from access devices, standardizing their names. Model service configuration generates and maintains all model service information in the model library, including model identification ID, model name, model address, and model description. Device configuration includes device type configuration, device acquisition gateway configuration, and data access policy configuration. Model deployment configuration binds access devices to models and allows customization of model running parameters, the mapping between running parameters and collected data, and feature extraction strategies.
[0056] In one embodiment, such as Figure 2 As shown, a method for customizing an equipment monitoring and diagnostic model is provided, which can be applied to... Figure 1 Taking the configuration center in the example, this method can include the following steps:
[0057] Step 201: Configure metadata based on the collected data from the access device to obtain a metadata list.
[0058] Metadata refers to the basic data items of the collected data, which may include vibration amplitude, vibration frequency, temperature, rotation speed, etc. of the access device. This application embodiment does not limit this.
[0059] Specifically, data can be collected from the access device to obtain the collected data, and then the basic data items of the collected data can be obtained. The names of the basic data items can be modified according to the preset specifications to unify and standardize the names of the basic data items, so as to facilitate the free customization of data.
[0060] Step 202: Configure model services based on the model information of the equipment early warning model and equipment diagnostic model in the model library to obtain a model service list.
[0061] The model information may include the model identification ID, model name, model runtime address, and model description. By configuring model services based on the model information, a model information set (i.e., a model service list) can be obtained, which contains information about the external services offered by the model library.
[0062] Step 203: Configure the access devices according to the metadata list to obtain device information.
[0063] In some embodiments, the device type configuration, device acquisition gateway configuration, and data access policy configuration of the access device can be performed based on the metadata list. The data access policy configuration includes the configuration of the accessed data items and the configuration of the data access method.
[0064] The steps for configuring the accessed data items may include: determining the accessed data items from the metadata list; and configuring the accessed data items.
[0065] Specifically, you can select the data items to be accessed from the metadata list, and then configure the data items. The data access method can be configured as real-time, timed, interval frequency, etc.
[0066] Step 204: Bind the access device to the target model in the model service list according to the device information, and configure the target model for deployment based on the collected data, the fault feature extraction tool model in the model library and the metadata list. The model deployment configuration includes setting model running parameters, mapping model running parameters, configuring model running parameter data processing and binding rules.
[0067] In some embodiments, model running parameters can be set and mapped based on the collected data, model running parameter data processing configuration can be performed based on the fault feature extraction tool model in the model library, and rules can be bound based on the metadata list.
[0068] Understandably, based on the device type of the access device in the device information or the functional requirements of the device model, the access device can be bound to the target model. After the device model is bound, the model operation parameters of the target model are set according to the collected data, and the model operation parameters are mapped to the collected data.
[0069] Specifically, the steps for setting and mapping model running parameters can be as follows: obtaining the basic data items of the collected data; determining the model running parameters of the target model based on the basic data items; and mapping the model running parameters to the collected data so that the configured target model can obtain the collected data according to the model running parameters.
[0070] It is understandable that the model running parameter data is also the data collected by the access device. The steps for processing and configuring the model running parameter data can be as follows: determine the feature extraction strategy of the collected data according to the fault feature extraction tool model, so that the configured target model can extract features from the collected data according to the feature extraction strategy.
[0071] In addition, the rule binding steps may include: generating a rule list for device early warning diagnosis based on the metadata list; binding the rule list to the target model so that the configured target model can perform early warning diagnosis on the access device based on the rule list.
[0072] During implementation, the rule base can be freely edited using a visual rule editor based on the metadata list to complete the rule setting for early warning diagnosis and obtain the rule list.
[0073] By using dynamic model running parameters, dynamically processing model running parameter data, and a dynamic rule editor, customization of low-code or no-code models is achieved.
[0074] Step 205: Run the configured target model to monitor and diagnose the access devices.
[0075] Once the model deployment and configuration are complete, the dynamic customization of the device monitoring and diagnostic model is finished. At this point, device monitoring and diagnostics can be achieved by loading and running the target model's runtime address.
[0076] Figure 3 A data flow diagram of a customized method for a device monitoring and diagnostic model in one embodiment is shown, such as... Figure 3 As shown, model services are configured based on the model information of the equipment early warning model and equipment diagnostic model in the model library to obtain a model service list, which is used for model binding.
[0077] Metadata is configured based on the data collected from the access devices to obtain a metadata list, which is used for device configuration and rule base.
[0078] The device configuration of the access device is performed based on the metadata list to obtain device information. This device information is used for model deployment configuration. In this embodiment, model binding is part of the model deployment configuration. Based on the device information, the access device is bound to the target model in the model service list, and the target model is configured with parameters (i.e., model running parameter setting), parameter mapping (i.e., model running parameter mapping), parameter data processing configuration (i.e., model running parameter data processing configuration), and rule binding.
[0079] A rule list is generated based on the metadata list, and rules are bound during the model deployment and configuration phase based on this rule list.
[0080] After the model is deployed and configured, run the configured target model to monitor and diagnose the access devices.
[0081] This application embodiment obtains a metadata list by configuring metadata based on the collected data from the access device; obtains a model service list by configuring model services based on the model information of device early warning models and device diagnostic models in the model library; obtains device information by configuring the access device based on the metadata list; binds the access device to the target model in the model service list based on the device information; and configures the target model for deployment based on the collected data, the fault feature extraction tool model in the model library, and the metadata list. The model deployment configuration includes setting model running parameters, mapping model running parameters, configuring model running parameter data processing, and binding rules. The configured target model is then run to monitor and diagnose the access device. This solution achieves customized device monitoring and diagnostic models with minimal or no code, improving the development efficiency of device monitoring and diagnostic models and reducing operation and maintenance costs.
[0082] The following describes an embodiment of the apparatus described in this application, which can be used to execute the method for customizing the equipment monitoring and diagnostic model described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method for customizing the equipment monitoring and diagnostic model described in the above embodiments of this application.
[0083] See Figure 4 The diagram shows a block diagram of a customized device for a device monitoring and diagnostic model in an embodiment of this application.
[0084] like Figure 4As shown in the embodiment of this application, the device for customizing a device monitoring and diagnostic model includes: a metadata configuration unit 401, a model service configuration unit 402, a device configuration unit 403, a model deployment configuration unit 404, and a model execution unit 405. The metadata configuration unit 401 is used to configure metadata based on the collected data from the access device to obtain a metadata list. The model service configuration unit 402 is used to configure model services based on model information of device early warning models and device diagnostic models in the model library to obtain a model service list. The device configuration unit 403 is used to configure the access device based on the metadata list to obtain device information. The model deployment configuration unit 404 is used to bind the access device to a target model in the model service list based on the device information, and to configure the target model for deployment based on the collected data, the fault feature extraction tool model in the model library, and the metadata list. The model deployment configuration includes setting model running parameters, mapping model running parameters, configuring model running parameter data processing, and binding rules. The model execution unit 405 is used to run the configured target model to monitor and diagnose the access device.
[0085] In some embodiments of this application, based on the foregoing scheme, the model deployment configuration unit 404 is further configured to obtain basic data items of the collected data; determine model running parameters of the target model based on the basic data items; and map the model running parameters to the collected data so that the configured target model obtains the collected data based on the model running parameters.
[0086] In some embodiments of this application, based on the foregoing scheme, the model deployment configuration unit 404 is further configured to determine the feature extraction strategy of the collected data according to the fault feature extraction tool model, so that the configured target model can extract features from the collected data according to the feature extraction strategy.
[0087] In some embodiments of this application, based on the foregoing scheme, the model deployment configuration unit 404 is further configured to generate a rule list for device early warning diagnosis based on the metadata list; and bind the rule list to the target model so that the configured target model performs early warning diagnosis on the access device based on the rule list.
[0088] In some embodiments of this application, based on the aforementioned scheme, the metadata configuration unit 401 is further configured to obtain basic data items of the collected data from the access device and modify the names of the basic data items according to a preset specification.
[0089] In some embodiments of this application, based on the foregoing scheme, the device configuration unit 403 is further configured to configure the device type, device acquisition gateway, and data access policy of the access device according to the metadata list, wherein the data access policy configuration includes the configuration of accessed data items and the configuration of data access methods.
[0090] In some embodiments of this application, based on the foregoing scheme, the device configuration unit 403 is further configured to determine the accessed data items from the metadata list and configure the accessed data items.
[0091] Based on the same inventive concept, this application also provides a customized device for an equipment monitoring and diagnostic model, see reference. Figure 5 The diagram shows a structural schematic of a customized device for a device monitoring and diagnostic model in an embodiment of this application. The customized device for the device monitoring and diagnostic model includes one or more memories 504, one or more processors 502, and at least one computer program (computer program instructions) stored in the memory 504 and executable on the processor 502. When the processor 502 executes the computer program, it implements the method described above.
[0092] Among them, Figure 5 In this document, a bus architecture (represented by bus 500) is used. Bus 500 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 can be used to store data used by processor 502 during operation.
[0093] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations described above.
[0094] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.
[0095] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.
[0096] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.
[0097] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0098] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for customizing an equipment monitoring and diagnostic model, characterized in that, The method includes: Based on the data collected by the access devices, metadata is configured to obtain a metadata list; Configure model services based on the model information of equipment early warning models and equipment diagnostic models in the model library to obtain a list of model services; Based on the metadata list, the access device is configured to obtain device information; The access device is bound to the target model in the model service list according to the device information, and the target model is configured for deployment according to the collected data, the fault feature extraction tool model in the model library and the metadata list. The model deployment configuration includes setting model running parameters, mapping model running parameters, configuring model running parameter data processing and binding rules. Run the configured target model to monitor and diagnose the access device; The steps for binding the rules include: A list of rules for device early warning diagnosis is generated based on the metadata list; The rule list is bound to the target model so that the configured target model can perform early warning diagnosis on the access device according to the rule list.
2. The method according to claim 1, characterized in that, The steps of setting and mapping the model running parameters include: Obtain the basic data items of the collected data; The model running parameters for the target model are determined based on the basic data items. The model running parameters are mapped to the collected data so that the configured target model can obtain the collected data according to the model running parameters.
3. The method according to claim 2, characterized in that, The steps for configuring the model's runtime parameter data processing include: The feature extraction strategy for the collected data is determined based on the fault feature extraction tool model, so that the configured target model can extract features from the collected data according to the feature extraction strategy.
4. The method according to claim 1, characterized in that, The metadata configuration based on the data collected by the access device includes: Obtain the basic data items of the collected data from the access device; Modify the names of the basic data items according to the preset specifications.
5. The method according to claim 1, characterized in that, The step of configuring the access device based on the metadata list includes: Based on the metadata list, the device type, device acquisition gateway, and data access policy of the access device are configured. The data access policy configuration includes the configuration of the accessed data items and the configuration of the data access method.
6. The method according to claim 5, characterized in that, The steps for configuring the accessed data items include: Determine the accessed data items from the metadata list; Configure the accessed data items.
7. A device for customizing equipment monitoring and diagnostic models, characterized in that, The device includes: The metadata configuration unit is used to configure metadata based on the data collected by the access device and obtain a metadata list. The model service configuration unit is used to configure model services based on the model information of the equipment early warning model and the equipment diagnostic model in the model library, and obtain a list of model services. The device configuration unit is used to configure the access device according to the metadata list to obtain device information; The model deployment configuration unit is used to bind the access device to the target model in the model service list according to the device information, and to configure the model deployment of the target model according to the collected data, the fault feature extraction tool model in the model library and the metadata list. The model deployment configuration includes model running parameter setting, model running parameter mapping, model running parameter data processing configuration and rule binding. The model execution unit is used to run the configured target model to monitor and diagnose the access device; The model deployment configuration unit is further configured to generate a rule list for device early warning diagnosis based on the metadata list; and bind the rule list to the target model so that the configured target model can perform early warning diagnosis on the access device based on the rule list.
8. A customized device for a device monitoring and diagnostic model, comprising a processor and a memory, characterized in that, The memory stores computer program instructions that can be executed by the processor, and when the processor executes the computer program instructions, it implements the instructions of the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that are loaded and executed by a processor to perform the operations described in any one of claims 1 to 6.