Intelligent diagnosis method and system for multi-dimensional parameters of terminal, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a terminal multi-dimensional parameter intelligent diagnosis method and system, electronic equipment and a storage medium, and belongs to the technical field of Internet of Things. According to the scheme, triple information of an Internet of Things terminal is obtained, wherein the triple information comprises a terminal identifier, a current time interval and a current wireless signal level; matching a configuration list of the Internet of Things terminal according to the terminal identifier; based on the configuration list, adding three fields of a data state, a dynamic maximum value and a dynamic minimum value to obtain eight-tuple diagnosis data, and persistently storing the eight-tuple diagnosis data in a module; acquiring current service data of the Internet of Things terminal, and updating the eight-tuple diagnosis data according to the current service data based on the current time interval and the current wireless signal level; and performing fault state diagnosis on the updated eight-tuple diagnosis data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result. The diagnosis timeliness and accuracy can be improved, and the overall power consumption of the terminal is reduced.

Description

Technical Field

[0001] This application relates to the field of Internet of Things technology, and in particular, to a method, system, electronic device, and storage medium for intelligent diagnosis of multi-dimensional parameters of a terminal. Background Art

[0002] When an Internet of Things terminal processes services in a wireless communication network, it often fails due to various reasons. At this time, it is necessary to quickly diagnose and troubleshoot various influencing factors. At present, most Internet of Things terminals rely on single or a small amount of sensor parameter data, which is difficult to comprehensively reflect the device status and mutual logical relationships. At the same time, the diagnostic functions in related technologies are simple and fixed, resulting in low accuracy and efficiency of terminal diagnosis. Moreover, complex instruction set architecture general-purpose processors are mostly used, with insufficient energy efficiency ratio and increased operation and maintenance costs. Summary of the Invention

[0003] The main purpose of the embodiments of this application is to propose a method, system, electronic device, and storage medium for intelligent diagnosis of multi-dimensional parameters of a terminal, aiming to improve the timeliness and accuracy of diagnosis and reduce the overall power consumption of the terminal.

[0004] To achieve the above object, on the one hand, an embodiment of this application proposes a method for intelligent diagnosis of multi-dimensional parameters of a terminal, and the method includes: Obtain the triple information of the Internet of Things terminal, where the triple information includes a terminal identifier, a current time interval, and a current wireless signal level; Match the configuration list of the Internet of Things terminal according to the terminal identifier, where the configuration list includes multi-dimensional parameters and threshold boundaries, the multi-dimensional parameters include parameter names of a reference time interval, a reference wireless signal level, and reference service data, and the threshold boundaries include a highest boundary threshold and a lowest boundary threshold of the reference service data; Based on the configuration list, add three fields of data status, dynamic maximum value, and dynamic minimum value to obtain eight-tuple diagnostic data and persistently store it in the module; Collect the current service data of the Internet of Things terminal, and based on the current time interval and the current wireless signal level, update and process the eight-tuple diagnostic data according to the current service data; Perform a fault status diagnosis on the updated eight-tuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnostic result, where the multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on a reduced instruction set architecture and is pre-set in the module.

[0005] In some embodiments, the step of adding three fields of data status, dynamic maximum value, and dynamic minimum value based on the configuration list to obtain eight-tuple diagnostic data and persistently store it in the module includes the following steps: Parse the configuration list to obtain the five - tuple diagnostic data corresponding to each of the reference service data. The five - tuple diagnostic data includes the reference time interval, the reference radio signal level, the parameter name, the highest boundary threshold, and the lowest boundary threshold; Initialize the fields of the data status, dynamic maximum value, and dynamic minimum value; According to the initialized data status, dynamic maximum value, and dynamic minimum value, splice the five - tuple diagnostic data to obtain eight - tuple diagnostic data and persistently store it in the module.

[0006] In some embodiments, the updating process of the eight - tuple diagnostic data according to the current service data based on the current time interval and the current radio signal level includes the following steps: Match the eight - tuple diagnostic data according to the current time interval, the current radio signal level, and the parameter name of the current service data; Determine the update strategy according to the data status in the eight - tuple diagnostic data; Based on the update strategy, update the dynamic maximum value and the dynamic minimum value according to the current service data.

[0007] In some embodiments, the process of diagnosing the fault status of the updated eight - tuple diagnostic data through a multi - dimensional parameter trend fusion algorithm to obtain a diagnostic result includes the following steps: Perform threshold fault diagnosis on the updated dynamic maximum value and dynamic minimum value according to the highest boundary threshold and the lowest boundary threshold to determine whether there is an over - threshold fault, and obtain a threshold result; When the threshold result indicates the existence of an over - threshold fault, determine that the diagnostic result is an over - threshold fault warning state; Or, when the threshold result indicates the non - existence of an over - threshold fault, perform trend prediction according to the current service data and the updated dynamic maximum value and dynamic minimum value to obtain a prediction result. The prediction result includes a trend balance value, a trend maximum value, and a trend minimum value; Perform risk diagnosis on the prediction result according to the highest boundary threshold and the lowest boundary threshold to determine whether there is a potential risk, and obtain a risk result; When the risk result indicates the existence of a potential risk, determine that the diagnostic result is a potential risk trend state; otherwise, determine that the diagnostic result is a stable fault - free trend state.

[0008] In some embodiments, the process of performing trend prediction according to the current service data and the updated dynamic maximum value and dynamic minimum value to obtain a prediction result includes the following steps: Based on the pre-designed calculation weights, perform weighted calculation according to the current service data and the updated dynamic maximum value and the dynamic minimum value to obtain a trend balance value; Perform fluctuation quantization processing according to the trend balance value and the updated dynamic minimum value to obtain a trend maximum value and a trend minimum value.

[0009] In some embodiments, the risk diagnosis of the prediction result according to the highest boundary threshold and the lowest boundary threshold to determine whether there is a potential risk and obtain a risk result includes the following steps: Perform upper limit safety diagnosis on the trend maximum value according to the highest boundary threshold to obtain an upper limit result; Perform lower limit safety diagnosis on the trend minimum value according to the lowest boundary threshold to obtain a lower limit result; Perform risk diagnosis according to the upper limit result and the lower limit result to determine whether there is a potential risk and obtain a risk result; Among them, the risk diagnosis according to the upper limit result and the lower limit result to determine whether there is a potential risk and obtain a risk result includes: When the upper limit result is that the trend maximum value is greater than the highest boundary threshold or the lower limit result is that the trend minimum value is less than the lowest boundary threshold, it is determined that the risk result is that there is a potential risk; Or, when the upper limit result is that the trend maximum value is less than the highest boundary threshold and the lower limit result is that the trend minimum value is greater than the lowest boundary threshold, it is determined that the risk result is that there is no potential risk.

[0010] In some embodiments, the terminal multi-dimensional parameter intelligent diagnosis method further includes the following steps: When the diagnosis result is the over-threshold fault warning state or the potential risk trend state, send an alarm message; Execute a fault handling operation according to the alarm message; After completing the fault handling operation, send an update instruction to the module to update the data state in the eight-tuple diagnosis data to the initial state.

[0011] To achieve the above object, another aspect of the embodiments of the present application proposes a terminal multi-dimensional parameter intelligent diagnosis system, and the system includes: A first module, configured to obtain triple information of an Internet of Things terminal, where the triple information includes a terminal identifier, a current time interval, and a current wireless signal level; A second module, configured to match a configuration list of the Internet of Things (IoT) terminal according to the terminal identifier, where the configuration list includes multi-dimensional parameters and threshold boundaries, the multi-dimensional parameters include parameter names of a reference time interval, a reference radio signal level, and reference service data, and the threshold boundaries include a highest boundary threshold and a lowest boundary threshold of the reference service data; A third module, configured to add three fields of a data status, a dynamic maximum value, and a dynamic minimum value based on the configuration list to obtain octuple diagnostic data and persistently store the octuple diagnostic data in the module; A fourth module, configured to collect current service data of the IoT terminal, and update and process the octuple diagnostic data according to the current service data based on the current time interval and the current radio signal level; A fifth module, configured to perform a fault status diagnosis on the updated octuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result, where the multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on a reduced instruction set architecture and is preset in the module.

[0012] To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, where the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the above method is implemented.

[0013] To achieve the above object, another aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0014] The embodiments of the present application at least include the following beneficial effects: The present application provides a method, a system, an electronic device, and a storage medium for intelligent diagnosis of multi-dimensional parameters of a terminal. The solution obtains triple information of an IoT terminal, where the triple information includes a terminal identifier, a current time interval, and a current radio signal level; matches a configuration list of the IoT terminal according to the terminal identifier; adds three fields of a data status, a dynamic maximum value, and a dynamic minimum value based on the configuration list to obtain octuple diagnostic data and persistently store the octuple diagnostic data in the module; collects current service data of the IoT terminal, and updates and processes the octuple diagnostic data according to the current service data based on the current time interval and the current radio signal level; performs a fault status diagnosis on the updated octuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result. The present application can improve the timeliness and accuracy of diagnosis and reduce the overall power consumption of the terminal. Description of the Drawings

[0015] Figure 1 is a flowchart of the method for intelligent diagnosis of multi-dimensional parameters of a terminal provided by the embodiments of the present application; Figure 2 is the specific method step flowchart of step S103 provided by an embodiment of the present application; Figure 1 Figure 3 is the specific method step flowchart of step S104 provided by an embodiment of the present application; Figure 1 Figure 4 is the specific method step flowchart of step S105 provided by an embodiment of the present application; Figure 1 Figure 5 is the specific method step flowchart of step S403 provided by an embodiment of the present application; Figure 4 Figure 6 is the specific method step flowchart of step S404 provided by an embodiment of the present application; Figure 4 Figure 7 is the flowchart of steps S701 to S703 in the intelligent diagnosis method for terminal multi-dimensional parameters provided by an embodiment of the present application; Figure 8 is the flowchart of the intelligent diagnosis method for terminal multi-dimensional parameters provided by another embodiment of the present application; Figure 9 is the structural schematic diagram of the intelligent diagnosis system for terminal multi-dimensional parameters provided by an embodiment of the present application; Figure 10 is the hardware structural schematic diagram of the electronic device provided by an embodiment of the present application. Detailed implementation manners

[0016] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application described in detail in the appended claims.

[0017] ​​​​​It will be appreciated that the terms "first", "second", etc. used in the present application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, as used herein, the words "if", "when" may be interpreted as "when...", "when...", or "in response to determining".

[0018] The terms "at least one", "a plurality of", "each", "any one", etc. used in the present application, at least one includes one, two or more than two, a plurality includes two or more than two, each refers to each of the corresponding plurality, and any one refers to any one of the plurality.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0020] Before elaborating on the embodiments of the present application in detail, some related technologies involved in the embodiments of the present application will be described first.

[0021] Currently, the mainstream Internet of Things terminal fault diagnosis solutions only monitor a single parameter through sensors, ignoring the correlation between multi-dimensional parameters, and it is difficult to comprehensively reflect the device status and mutual logical relationships. At the same time, the diagnosis of the solution usually directly compares the data collected by the sensors with a preset fixed threshold, and the diagnosis function is simply solidified, without the multi-dimensional parameter self-adaptive ability and the fault trend prediction function. Moreover, most of the related technical solutions use the complex instruction set (CISC) architecture. The increasingly complex instruction system is not only difficult to implement, but also reduces the terminal performance. The complex instruction system will inevitably bring about the complexity of the structure, which not only increases the design time and cost, but also easily causes design mistakes and affects the accuracy of fault diagnosis. Therefore, a terminal intelligent diagnosis method that can integrate self-adaptive multi-dimensional parameters and has low power consumption is needed to improve the accuracy and efficiency of terminal diagnosis and reduce the operation and maintenance costs.

[0022] In view of this, an intelligent diagnosis method, system, electronic device and storage medium for terminal multi-dimensional parameters are provided in an embodiment of the present application. This solution obtains the triple information of the Internet of Things terminal, where the triple information includes the terminal identifier, the current time interval, and the current wireless signal level; matches the configuration list of the Internet of Things terminal according to the terminal identifier; based on the configuration list, adds three fields of data status, dynamic maximum value, and dynamic minimum value to obtain octuple diagnostic data and persistently store it in the module; collects the current service data of the Internet of Things terminal, and based on the current time interval and the current wireless signal level, updates and processes the octuple diagnostic data according to the current service data; diagnoses the fault status of the updated octuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result. The present application can improve the timeliness and accuracy of diagnosis and reduce the overall power consumption of the terminal.

[0023] The intelligent diagnosis method for terminal multi-dimensional parameters provided by the embodiment of the present application relates to the field of Internet of Things technology. The intelligent diagnosis method for terminal multi-dimensional parameters provided by the embodiment of the present application can be applied to a terminal, or to a server, or can also be software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, or can be configured as a server cluster or distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing the intelligent diagnosis method for terminal multi-dimensional parameters, etc., but is not limited to the above forms.

[0024] The present application can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0025] Figure 1 is an optional flowchart of the intelligent diagnosis method for terminal multi-dimensional parameters provided by an embodiment of the present application. Figure 1 The method in

[0026] Step S101: Obtain the triple information of the IoT terminal, where the triple information includes the terminal identifier, the current time interval, and the current wireless signal level.

[0027] Step S102: Match the configuration list of the IoT terminal according to the terminal identifier. The configuration list includes multi-dimensional parameters and threshold boundaries. The multi-dimensional parameters include the parameter names of the reference time interval, the reference wireless signal level, and the reference service data. The threshold boundaries include the highest boundary threshold and the lowest boundary threshold of the reference service data.

[0028] Step S103: Based on the configuration list, add three fields: data status, dynamic maximum value, and dynamic minimum value, to obtain the eight-tuple diagnostic data and persistently store it in the module.

[0029] Step S104: Collect the current service data of the IoT terminal, and based on the current time interval and the current wireless signal level, update and process the eight-tuple diagnostic data according to the current service data.

[0030] Step S105: Perform a fault status diagnosis on the updated eight-tuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result. The multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on the reduced instruction set architecture and is pre-set in the module.

[0031] In this embodiment, the IoT terminal collects triple information, which includes the terminal identifier ID (including the terminal product model, manufacturer identifier information, etc.), the current wireless signal level, and the current time interval, and reports it to the platform through the module for networking.

[0032] Specifically, IoT terminals produced by different product models or manufacturers have differences in working environments and business requirements. For example, the working temperature threshold of terminal model A is 10°C higher than that of terminal model B. To improve the accuracy of diagnosis, the pre-stored configuration of the terminal is matched through the terminal identifier ID. At the same time, considering that the working environment and business requirements of the terminal also change dynamically with time (such as day / night / seasons / dates) and network status (such as wireless signal level), the present application further forms triple information by obtaining the current time interval and the current wireless signal level, so as to accurately screen the threshold rules with the smallest granularity later and avoid sending redundant information. In addition, the triple information is lightweight data, which can reduce redundant data transmission and power consumption compared with uploading the original service data (such as continuously transmitting the temperature curve). Communicating with triple information can effectively improve the energy efficiency ratio.

[0033] After the platform receives the triple information reported by the terminal, it matches the multi-dimensional parameters and threshold configuration list of the corresponding IoT terminal according to the terminal identifier ID.

[0034] It can be understood that the platform stores configuration lists corresponding to different terminal types. The configuration list of each terminal type contains a reference time interval representing different time intervals, a reference radio signal level representing different radio signal levels, parameter names of reference service data fields defined by the user (such as temperature, power, etc.), and the highest boundary threshold and the lowest boundary threshold of faults set by the user for the reference service data field values (such as the highest temperature degree, the lowest remaining power, etc.). The configuration list corresponding to the current terminal identifier ID is matched from multiple configuration lists through the terminal identifier ID and sent to the terminal.

[0035] After the terminal receives the configuration list, it adds three fields: data status, dynamic maximum value, and dynamic minimum value to the configuration list, and merges them into an eight-tuple diagnostic data (reference time interval, reference radio signal level, parameter name, highest boundary threshold, lowest boundary threshold, data status, dynamic maximum value, and dynamic minimum value), and then persists and stores it in the module.

[0036] It should be noted that the data status is used to identify the stage of the diagnostic process. For example, 0 is the initial state, indicating that the terminal has just received the configuration list and has not started fault diagnosis, and needs to wait for the first data collection. 1 is the diagnostic monitoring state, indicating that the fault diagnosis has started and the terminal has recorded real-time data. The dynamic maximum value is used to record the peak value of the parameter during the fault monitoring period in real time. The dynamic minimum value is used to record the valley value of the parameter during the fault monitoring period in real time.

[0037] Next, the IoT terminal starts to collect the current service data, associates the current service data with the current time interval and the current radio signal level, adaptively matches the eight-tuple diagnostic data in the stored eight-tuple diagnostic data set that has the same current time interval, the same current radio signal level, and the same parameter name, updates the dynamic maximum value and / or the dynamic minimum value in the matched eight-tuple diagnostic data with the current service data, and overwrites the old eight-tuple diagnostic data with the updated eight-tuple diagnostic data, and persists and stores it in the module.

[0038] Finally, the module performs fault status diagnosis through a multi-dimensional parameter trend fusion algorithm. The multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on the Reduced Instruction Set Computing (RISC) architecture. The reduced instruction set only contains a small number of instructions with high usage frequencies and provides some necessary instructions to support the operating system and high-level languages. Among them, the fifth-generation reduced instruction set (RISC-V) can freely view, use, and modify the design of the instruction set, and developers can freely match and extend the most suitable instruction set for the project according to actual needs. In this embodiment, the multi-dimensional parameter trend fusion algorithm is implemented by extending and defining several source data groups and operation calculation logics on the basis of the RISC-V architecture, which is completed by a software and hardware integrated execution unit and is pre-set in the chip for the module integrating this chip to use.

[0039] Specifically, after completing the update of the octuple diagnostic data, the module first compares the currently collected service data with the highest boundary threshold and the lowest boundary threshold stored in the octuple diagnostic data. If the currently collected service data is within the threshold range, the trend analysis stage is further carried out to determine whether there is a potential risk according to the trend. If the analysis shows that although the parameter does not immediately exceed the threshold but shows a change trend that may cause a fault, there is a potential risk in the terminal. Finally, according to the results of the threshold comparison and trend analysis, the status of the terminal is classified to obtain a diagnostic result representing the terminal status, so that the platform can process the terminal according to the diagnostic result.

[0040] In the embodiment of the present application, the method of using a single or simple parameter to diagnose faults on a high-power consumption chip is improved to a low-power RISC-V proprietary instruction of a software and hardware integrated execution unit, which can calculate the fault trend data value in time, achieving the effects of greatly improving the diagnostic timeliness and reducing the overall power consumption of the terminal. At the same time, by persistently storing the octuple diagnostic data in the terminal module and adaptively matching the data collected by the terminal with the octuple diagnostic data, and using the intelligent diagnostic trend calculation algorithm to predict the potential risk status of the terminal fault, it is also possible to predict the possible faults in advance, greatly improving the accuracy of terminal fault diagnosis and the efficiency of the terminal operation in handling faults.

[0041] Refer to Figure 2 In some embodiments, step S103 may include but is not limited to steps S201 to S203.

[0042] Step S201: Parse and process the configuration list to obtain the five-tuple diagnostic data corresponding to each reference service data. The five-tuple diagnostic data includes a reference time interval, a reference radio signal level, a parameter name, a highest boundary threshold, and a lowest boundary threshold.

[0043] Step S202: Initialize the fields of the data status, dynamic maximum value, and dynamic minimum value.

[0044] Step S203: According to the initialized data status, dynamic maximum value, and dynamic minimum value, splice the five-tuple diagnostic data to obtain eight-tuple diagnostic data and persistently store it in the module.

[0045] In this embodiment, the terminal module first parses the configuration list sent by the platform and generates independent five-tuple diagnostic data for the parameters of each reference service data, including the reference time interval, reference radio signal level, parameter name, maximum boundary threshold, and minimum boundary threshold.

[0046] It should be noted that a five-tuple diagnostic data is the structured data of the terminal for a certain reference service data in a specific scenario, representing the monitoring specification of the terminal in the specific operating environment. Among them, the reference time interval represents the specific time period division to which the threshold of this group of parameters applies; the reference radio signal level can be quantified by levels. For example, levels 1-3 respectively represent good, average, and poor; the parameter name is used to identify the data type of the reference service data; the maximum boundary threshold and the minimum boundary threshold jointly define the safe operating range of this reference service data in a specific scenario.

[0047] Exemplarily, the terminal module reads each record in the configuration list, establishes a data index according to the parameter name field in the record, and then extracts the reference time interval, reference radio signal level, maximum boundary threshold, and minimum boundary threshold fields in the record to form complete five-tuple diagnostic data.

[0048] It can be understood that the terminal can maintain multiple five-tuple diagnostic data at the same time. The five-tuple diagnostic data corresponding to the service data in different time intervals, different wireless reference signal levels, or different parameter names are different. For example, for temperature, there can be: [1,1,Temperature,60,10] representing the temperature threshold range on the first day of each month when the wireless signal is good, and [1,3,Temperature,50,10] representing the temperature threshold range on the first day of each month when the wireless signal is poor.

[0049] Through the multi-dimensional threshold management mechanism, it can dynamically adapt to the changes in the terminal operating environment and avoid misdiagnosis caused by fixed threshold judgment. At the same time, it provides differentiated monitoring standards for different service scenarios. Through this structured design, flexible configuration and high-precision matching of diagnosis are realized, providing an accurate judgment basis for subsequent real-time diagnostic analysis.

[0050] Furthermore, considering that the five-tuple diagnostic data only contains static threshold rules and cannot distinguish whether the monitoring and diagnosis have started, and to achieve potential risk prediction, three dynamic fields of data status, dynamic maximum value, and dynamic minimum value are added and each field is initialized.

[0051] The data status field is used to identify the diagnostic phase. In the initialization phase, it is identified as the initial data status by 0, that is, the data collection and diagnosis of this monitoring parameter have not started yet. When the terminal first collects the data of this parameter, the data status is updated to 1, that is, it enters the diagnostic monitoring state.

[0052] The dynamic maximum value and dynamic minimum value fields are used to record historical data to support trend analysis. By continuously updating the dynamic maximum value and dynamic minimum value fields, the changes of the parameter during the monitoring process are recorded, providing input for trend calculation and realizing potential risk prediction. In the initialization phase, the dynamic maximum value and dynamic value can be initialized with 0 or invalid values. Until the first valid data is collected, these two fields are updated, and the historical extreme values of this parameter are continuously recorded during the subsequent monitoring cycles.

[0053] The terminal module splices and combines the three initialized dynamic fields with the five - tuple diagnostic data to form complete eight - tuple diagnostic data.

[0054] Exemplarily, after splicing the five - tuple diagnostic data [1, 1, Temperature, 60, 10] with the initialized fields, the eight - tuple diagnostic data [1, 1, Temperature, 60, 10, 0, 0, 0] is obtained.

[0055] Write the generated eight - tuple diagnostic data into the terminal module to ensure that the historical monitoring status can still be retained after the terminal device has been restarted or the network has been disconnected.

[0056] Refer to Figure 3 , in some embodiments, step S104 may include but is not limited to steps S301 to S303.

[0057] Step S301, match the eight - tuple diagnostic data according to the current time interval, the current wireless signal level, and the parameter name of the current service data.

[0058] Step S302, determine the update strategy according to the data status in the eight - tuple diagnostic data.

[0059] Step S303, based on the update strategy, update the dynamic maximum value and dynamic minimum value according to the current service data.

[0060] In this embodiment, the terminal performs a matching operation based on the collected current service data, and constructs a retrieval condition for data matching by obtaining key parameters of the current service environment, such as the current time interval, the current wireless signal level, and the parameter name of the current service data. The target data is found by searching in the octuple diagnostic data set stored in the module according to the retrieval condition, and an accurate matching mechanism is used to ensure that the diagnostic rule is adapted to the current service environment. The target data is a reference time interval that matches the current time interval, a reference wireless signal level that is consistent with the current wireless signal level, and octuple diagnostic data with the same parameter name.

[0061] Parse according to the data status field in the matched octuple diagnostic data, check the value of the data status field. When the data status field shows 0, that is, the initial state, then change the status to the diagnostic monitoring state, and determine the update strategy as simultaneously storing the data value of the current service data into the dynamic maximum value and the dynamic minimum value. Based on the update strategy, update the dynamic maximum value and the dynamic minimum value according to the current service data.

[0062] When the data status field shows 1, that is, the diagnostic monitoring state, it indicates that the service parameter has entered the normal monitoring process, and the dynamic maximum value and the dynamic minimum value have stored the collected data. A judgment mechanism is used to distinguish the fields to be updated, and based on the update strategy, update the dynamic maximum value or the dynamic minimum value according to the current service data.

[0063] Specifically, if the data value of the current service data is greater than the dynamic maximum value stored in the octuple diagnostic data, then determine the update strategy as changing the dynamic maximum value to the data value of the current service data. If the data value of the current service data is less than the dynamic minimum value stored in the octuple diagnostic data, then determine the update strategy as changing the dynamic minimum value to the data value of the current service data.

[0064] Refer to Figure 4 In some embodiments, step S105 may include, but is not limited to, steps S401 to S405.

[0065] Step S401, perform threshold fault diagnosis on the updated dynamic maximum value and dynamic minimum value according to the highest boundary threshold and the lowest boundary threshold, and determine whether there is an over-threshold fault to obtain a threshold result.

[0066] Step S402, when the threshold result is that there is an over-threshold fault, then determine the diagnostic result as the over-threshold fault alarm state.

[0067] Step S403, when the threshold result is that there is no over-threshold fault, then perform trend prediction according to the current service data and the updated dynamic maximum value and dynamic minimum value to obtain a prediction result, and the prediction result includes a trend balance value, a trend maximum value, and a trend minimum value.

[0068] Step S404: Perform risk diagnosis on the prediction result according to the highest boundary threshold and the lowest boundary threshold, determine whether there is a potential risk, and obtain a risk result.

[0069] Step S405: When the risk result indicates the existence of a potential risk, determine the diagnosis result as the potential risk trend state; otherwise, determine the diagnosis result as the stable and fault-free trend state.

[0070] In this embodiment, threshold fault diagnosis respectively compares the updated dynamic maximum value and dynamic minimum value with the highest boundary threshold and the lowest boundary threshold through parallel detection, determines whether the dynamic maximum value is greater than the highest boundary threshold and whether the dynamic minimum value is lower than the lowest boundary threshold, and obtains a threshold result.

[0071] When the threshold result of step S401 is a threshold-exceeding fault, that is, it means that at least one extreme value exceeds the corresponding boundary threshold range. At this time, jump to step S402, determine the diagnosis result as the threshold-exceeding fault warning state, and form the first line of defense for fault diagnosis through direct threshold comparison to quickly identify obvious parameter anomalies.

[0072] When the threshold result diagnosed in step S401 is that there is no threshold-exceeding fault, then jump to step S403. Considering that only using the static diagnosis method can only diagnose faults after they occur, and if the data is gradually approaching the threshold, potential risks cannot be detected in advance. Therefore, it is necessary to further perform trend prediction based on the current business data and the updated dynamic maximum value and dynamic minimum value, and achieve more intelligent fault diagnosis by quantifying the trend of data fluctuations.

[0073] Specifically, call the dedicated multi-dimensional parameter trend fusion algorithm preset in the RISC-V chip, calculate the trend balance value representing the overall distribution trend of the parameters through weighted average, and then, based on the trend balance value, expand upward and downward respectively according to the quantified fluctuation range to further derive the predictive trend maximum value and trend minimum value. The trend minimum value and trend maximum value can reflect the possible fluctuation range of the current business data in the future for a period of time, providing a basis for forward-looking risk judgment.

[0074] Perform risk diagnosis on the prediction result according to the highest boundary threshold and the lowest boundary threshold, and determine whether there is a potential risk.

[0075] If the fluctuation range of the data is too large, even if the current business data is still within the threshold range, but if the fluctuation exceeds the threshold limit, it will be determined that there is a potential risk. This design enables the terminal to detect abnormal changes in the parameter trend in advance and determine the risk result as the potential risk trend state.

[0076] If the fluctuation range of the data is within a small interval, it indicates that the terminal is in a normal operating state, the parameters are operating within a safe and stable range, there are no potential risks, and the risk result is determined to be a stable and fault-free trend state.

[0077] Referring to Figure 5 , in some embodiments, step S403 may include but is not limited to steps S501 to S502.

[0078] Step S501, based on a pre-designed calculation weight, perform a weighted calculation according to the current business data and the updated dynamic maximum value and dynamic minimum value to obtain a trend balance value.

[0079] Step S502, perform a fluctuation quantization process according to the trend balance value and the updated dynamic minimum value to obtain a trend maximum value and a trend minimum value.

[0080] Specifically, the dedicated multi-dimensional parameter trend fusion algorithm based on RISC-V customizes the following instruction functions within the chip: Tydiag SH, SHW, SL, SLW, SC, SCW, DB, DH, DL.

[0081] Among them, Tydiag is a custom opcode, SH, SHW, SL, SLW, SC, SCW are source data groups, SH represents the dynamic maximum value, SHW represents the calculation weight of SH, SL represents the dynamic minimum value, SLW represents the calculation weight of SL, SC represents the current business data, SCW represents the calculation weight of SC; DH, DL, DB are target trend data groups output after performing trend prediction operations, DB represents the trend balance value, DH represents the trend maximum value, and DL represents the trend minimum value.

[0082] Multiply the current business data and the updated dynamic maximum value and dynamic minimum value by their respective corresponding calculation weights, add the three weighted values and divide by the sum of the calculation weights to complete the weighted calculation. The weighted formula is as follows: DB=(SH*SHW+SL*SLW+SC*SCW) / (SHW+SLW+SCW) (1).

[0083] It can be understood that the calculation weight can be dynamically adjusted according to the actual application scenario and is defaulted to an equal weight of 1 standard deviation in this embodiment.

[0084] By weighted calculation, balance the influence degrees of the dynamic maximum value and dynamic minimum value recorded in historical data and the current business data on trend analysis, consider not only the long-term change trend of parameters but also the latest data fluctuations, and obtain the trend balance value.

[0085] Next, the obtained trend balance value in step S501 is used for quantization processing of the fluctuation range.

[0086] Exemplarily, by subtracting the updated dynamic minimum value from the trend average value, a reference difference value representing the data fluctuation amplitude is obtained. Subsequently, the reference difference value is added to and subtracted from the trend balance value respectively to obtain the predicted trend maximum value and trend minimum value. The calculation formulas are as follows: DH = DB + (DB - SL) (2); DL = DB - (DB - SL) (3); Among them, the trend maximum value DH represents the upper limit value that the current business data may reach under the condition of maintaining the current change trend, and the trend minimum value DL represents the lower limit value that the current business data may reach under the condition of maintaining the current change trend.

[0087] After completing the trend prediction, the obtained trend maximum value and trend minimum value are temporarily stored to provide data support for subsequent risk diagnosis.

[0088] Refer to Figure 6 , in some embodiments, step S404 may include but is not limited to steps S601 to S603.

[0089] Step S601: Perform upper limit safety diagnosis on the trend maximum value according to the highest boundary threshold to obtain an upper limit result.

[0090] Step S602: Perform lower limit safety diagnosis on the trend minimum value according to the lowest boundary threshold to obtain a lower limit result.

[0091] Step S603: Perform risk diagnosis according to the upper limit result and the lower limit result to determine whether there is a potential risk and obtain a risk result.

[0092] In this embodiment, first, the trend maximum value obtained in step S502 is compared with the highest boundary threshold in the octuple diagnosis data to determine whether the predicted fluctuation upper limit of the parameter in the future period may break through the safety boundary, and an upper limit result is obtained.

[0093] Meanwhile, in parallel, the trend minimum value obtained in step S502 is compared with the lowest boundary threshold in the octuple diagnosis data to determine whether the predicted fluctuation lower limit of the parameter in the future period may break through the safety boundary, and a lower limit result is obtained.

[0094] In step S603 of some embodiments, when the upper limit result is that the trend maximum value is greater than the highest boundary threshold or the lower limit result is that the trend minimum value is less than the lowest boundary threshold, this indicates that although the parameter of the current business data is currently still within the safe range, its change trend has shown risk characteristics that may exceed the safe boundary in the future.

[0095] It is understandable that for the diagnostic analysis of the current business data of the same monitoring, it is not required that both the trend maximum value and the trend minimum value exceed the safety boundary at the same time. As long as either condition is met, it can be determined that there is a potential risk. This is because the unilateral deviation of the parameter upwards or downwards may constitute an independent safety hazard. For example, the continuous increase in temperature may cause the terminal device to overheat, and the continuous decrease in voltage may lead to an undervoltage fault. Therefore, parallel judgment is adopted, and double detection is executed synchronously. As long as either condition is triggered, it can be determined that the risk result is the existence of a potential risk.

[0096] In step S603 of some other embodiments, when the upper limit result is that the trend maximum value is less than the highest boundary threshold and the lower limit result is that the trend minimum value is greater than the lowest boundary threshold, it indicates that even after fluctuations, the trend maximum value is still less than the highest boundary threshold and the trend minimum value is still greater than the lowest boundary threshold, and the parameter will operate stably in the future period. Then, it is determined that the risk result is the absence of a potential risk.

[0097] Referring to Figure 7 , in some embodiments, the intelligent diagnostic method for terminal multi-dimensional parameters may further include, but is not limited to, steps S701 to S703.

[0098] Step S701, when the diagnostic result is in the over-threshold fault warning state or the potential risk trend state, send an alarm message.

[0099] Step S702, perform a fault handling operation according to the alarm message.

[0100] Step S703, after completing the fault handling operation, send an update instruction to the module to update the data status in the octuple diagnostic data to the initial state.

[0101] In this embodiment, the terminal sends an alarm message according to the diagnostic result when it is in the over-threshold fault warning state or the potential risk trend state.

[0102] Specifically, when the diagnostic result is in the over-threshold fault warning state, the octuple diagnostic data corresponding to the diagnostic result is sent as an alarm message to the terminal or the platform.

[0103] When the diagnostic result is in the potential risk trend state, the octuple diagnostic data corresponding to the diagnostic result, the trend maximum value or the trend minimum value is sent as an alarm signal to the terminal or the platform.

[0104] After receiving the alarm signal, for conventional faults that can be repaired by itself, the terminal calls the preset fault handling strategy and independently performs the fault handling operation according to the fault handling strategy. For complex faults that require manual intervention, the alarm signal is pushed to the operation and maintenance personnel through the platform so that the operation and maintenance personnel can quickly locate the fault according to the alarm message.

[0105] After completing the processing of the fault, send an update instruction to the module to update the data status in the octuple diagnostic data in the module to the initial state. At the same time, clear the dynamic maximum value and dynamic minimum value in the octuple diagnostic data. This state reset mechanism provides an initial environment for the new monitoring cycle to ensure that subsequent diagnoses are not interfered by historical fault data.

[0106] Next, in combination with specific application examples, the solutions of the embodiments of the present invention will be introduced and described in detail.

[0107] The intelligent diagnosis method for terminal multi-dimensional parameters provided by the embodiments of this application can be applied to the intelligent fault diagnosis of Internet of Things terminals. Based on this application, the terminal fault diagnosis strategy is optimized to overall improve the accuracy and timeliness of terminal fault diagnosis, and reduce the terminal power consumption and terminal maintenance cost.

[0108] At the same time, the network core network or platform is used to place the fault diagnosis trend calculation on the terminal module side. The terminal module side intelligently diagnoses the risk trend to improve the network and platform performance and operation efficiency, and reduce the diagnosis pressure and operation and maintenance cost of the cloud platform.

[0109] Refer to Figure 8 , Figure 8 which is the flowchart of the intelligent diagnosis method for terminal multi-dimensional parameters provided by another embodiment of this application.

[0110] In this embodiment, by proposing a customized RISC-V specific multi-dimensional parameter trend fusion instruction, a method for the terminal module to form octuple diagnostic data and adaptively match the octuple diagnostic data, a calculation algorithm for the terminal module to intelligently diagnose and calculate trend data, defining the terminal triple identification information and octuple diagnostic data, and diagnostic trend analysis data, the intelligent diagnosis method for terminal multi-dimensional parameters is realized. It can alarm threshold faults in a timely manner, provide potential risk trend analysis, not only overall reduce the terminal power consumption, improve the alarm response time, but also greatly improve the diagnosis accuracy and efficiency, and reduce the operation and maintenance cost.

[0111] This embodiment also provides a diagnostic system, including a specific multi-dimensional parameter trend fusion instruction module, a terminal information collection and reporting module, a platform data receiving module, a multi-dimensional parameter and threshold matching module, a sending module, a receiving configuration list module, a persistent octuple diagnostic data module, an intelligent diagnosis calculation module, a fault handling module, and an update data module.

[0112] Specifically, the dedicated multi-dimensional parameter trend fusion instruction module is used to customize dedicated multi-dimensional parameter trend fusion instructions based on RISC-V. Based on the RISC-V architecture, the instructions are extended through custom opcodes SH, SHW, SL, SLW, SC, SCW, DB, DH, DL, and the operation calculation logic is defined as: DB = (SH * SHW + SL * SLW + SC * SCW) / (SHW + SLW + SCW), DH = DB + (DB - SL), DL = DB - (DB - SL), and is pre-set in the RISC-V chip of the module.

[0113] The terminal information collection and reporting module is used to collect terminal triple information (terminal identification ID (including terminal product model / factory identification information), current wireless signal level, current time interval) and report it to the platform through the module.

[0114] The platform data receiving module is used to receive terminal identification information; the multi-dimensional parameter and threshold matching module is used to match the multi-dimensional parameter and threshold configuration list of the corresponding terminal according to the terminal identification ID; the sending module is used to control the platform to send the multi-dimensional parameter and threshold configuration list to the terminal.

[0115] The receiving configuration list module is used to control the terminal module to receive the multi-dimensional parameter and threshold configuration list; the persistent octuple diagnostic data module is used to persistently store the diagnostic data octuple in the module. The terminal module receives the configuration list sent by the platform and combines three fields (data status, dynamic maximum value, dynamic minimum value) to form a diagnostic data octuple (reference time interval, reference wireless signal level, parameter name, maximum boundary threshold, minimum boundary threshold, data status, dynamic maximum value, dynamic minimum value), and at the same time persists and stores it in the module for later diagnosis.

[0116] The intelligent diagnosis calculation module is used to control the terminal to collect data, adaptively match the octuple diagnostic data, call the intelligent diagnosis calculation of the module, and output the diagnosis status to the terminal and the platform. The terminal collects the current data value, calls the module instruction to execute the diagnosis, and the module adaptively matches the octuple diagnostic data according to the current time interval, current wireless signal level, and parameter name, calls the intelligent diagnosis calculation module, obtains the diagnosis result (over-threshold fault warning status, potential risk trend status, stable and fault-free trend status) and alarms it to the terminal, and at the same time reports it to the platform.

[0117] Among them, the process of diagnosis calculation and selection is as follows: Check the value of the data status field in the octuple diagnostic data. If the data status is the initial status, change the status to the diagnostic monitoring status, and at the same time store the current data value into the dynamic maximum value and the dynamic minimum value. If the status is the diagnostic monitoring status and the current data value is higher than the stored dynamic maximum value, then change the dynamic maximum value to the current data value. If the current data value is lower than the dynamic minimum value, then change the stored dynamic minimum value to the current data value; If the current data value is higher than the upper boundary threshold value or lower than the lower boundary threshold value, directly determine that the terminal is in the over-threshold fault warning status; If the current data value is between the upper boundary threshold value and the lower boundary threshold value, then use the dynamic maximum value, the dynamic minimum value, the current data value, and the weight (the user can customize the weight as needed, and the default is 1 standard deviation equal weight) as the SH, SHW, SL, SLW, SC, SCW parameter data, and call the function module that implements the customized special multi-dimensional parameter trend fusion instruction to obtain the business data trend balance value DB, the business data trend maximum value DH, and the business data trend minimum value DL. Finally, make the following judgment and compare with the threshold: When the business data trend maximum value DH is higher than the threshold maximum value or the business data trend minimum value DL is lower than the threshold minimum value, then determine that the terminal is in the potential risk trend status; When the foregoing conditions are not met, that is, the business data trend maximum value DH is lower than the threshold maximum value and the business data trend minimum value DL is higher than the threshold minimum value, then determine that the terminal is in the stable and fault-free trend status.

[0118] The fault handling module is used to control the platform or the terminal to handle the fault and update the diagnostic data status. After the terminal or the platform receives the fault warning status information, it executes the fault handling operation. After completing the fault handling, it sends an instruction to the module; the data update module is used to update the data status in the octuple diagnostic data in the module to the initial status.

[0119] Exemplarily, the Internet of Things terminal includes temperature (Temperature) and battery power (Battery), and it is necessary to simultaneously perform fault monitoring and diagnosis on the two groups of data of temperature and battery power. Use the module with the pre-set customized special multi-dimensional parameter trend fusion instruction based on RISC-V. The terminal reports the terminal identifier (assuming IMEI number, date is 1, wireless signal level), and the platform matches the parameter list according to the terminal identifier and issues the following parameters: 1, 1, Temperature, 60, 10; 1, 2, Temperature, 55, 10; 1, 3, Temperature, 50, 10; 1, 1, Battery, 100, 10; 1, 2, Battery, 100, 20; 1, 3, Battery, 100, 30.

[0120] Among them, the first field represents the date (1 represents the first day of each month, 2 represents the second day of each month, and so on), the second field represents the wireless signal level (1 is good, 2 is average, 3 is poor), the third field represents the parameter name, the fourth field represents the highest boundary threshold, and the fifth field represents the lowest boundary threshold.

[0121] After the terminal receives the above parameters, it combines three fields in the module to form an eight-tuple diagnostic data and persists it for storage, as shown below: 1, 1, Temperature, 60, 10, 0, 0, 0; 1, 2, Temperature, 55, 10, 0, 0, 0; 1, 3, Temperature, 50, 10, 0, 0, 0; 1, 1, Battery, 100, 10, 0, 0, 0; 1, 2, Battery, 100, 20, 0, 0, 0; 1, 3, Battery, 100, 30, 0, 0, 0.

[0122] Among them, the sixth field represents the data status (0 is the initial state, 1 is the diagnostic monitoring state), the seventh field stores the real-time highest value, and the eighth field stores the real-time lowest value.

[0123] The terminal collects data and performs intelligent diagnosis. Assuming that the data obtained by the terminal for the first time on the 1st date is temperature 20, battery power 80, and the current wireless signal level is 1 (used to represent a good wireless signal level), then the corresponding eight-tuple diagnostic data in the module changes to: 1, 1, Temperature, 60, 10, 1, 20, 20; 1, 1, Battery, 100, 10, 1, 80, 80.

[0124] According to step S401, perform threshold fault diagnosis. If the threshold result is that there is no over-threshold fault, then jump to execute step S403 for calculation.

[0125] Temperature: DB = (20 * 1 + 20 * 1 + 20 * 2) / (1 + 1 + 2) = 20; DH = 20 + (20 - 20) = 20; DL = 20 - (20 - 20) = 20.

[0126] Power consumption: DB = (80 * 1 + 80 * 1 + 80 * 2) / (1 + 1 + 2) = 80; DH = 80 + (80 - 80) = 80; DL = 80 - (80 - 80) = 80.

[0127] Among them, the temperature DH = 20 is not higher than 60 and DL = 20 is not lower than 10, and the power consumption DH = 80 is not higher than 100 and DL = 80 is not lower than 10, then the terminal state is a stable non-fault trend state.

[0128] Suppose the data obtained for the second time is temperature 50, power consumption 60, and the current wireless signal level is still 1, then the data in the module changes to: 1,1, Temperature,60,10,1,50,20; 1,1, Battery,100,10,1,80,60.

[0129] According to step S401 for threshold fault diagnosis, and the threshold result is that there is no over-threshold fault, then jump to execute step S403 for calculation.

[0130] Temperature: DB = (50 * 1 + 20 * 1 + 50 * 2) / (1 + 1 + 2) = 42.5; DH = 42.5 + (42.5 - 20) = 65; DL = 42.5 - (42.5 - 20) = 20.

[0131] Power consumption: DB = (80 * 1 + 60 * 1 + 60 * 2) / (1 + 1 + 2) = 65; DH = 65 + (65 - 60) = 70; DL = 65 - (65 - 60) = 55.

[0132] Among them, the temperature DH = 65 is higher than 60, DL = 20 is not lower than 10, and the power consumption DH = 65 is not higher than 100 and DL = 55 is not lower than 10, then the terminal state is a potential risk trend state (because the temperature has a potential risk of being higher than the threshold).

[0133] Suppose the data obtained for the third time is temperature 70, power consumption 8, and the current wireless signal level is still 1, then the data in the module changes to: 1,1, Temperature,60,10,1,70,20; 1,1, Battery,100,10,1,80,8.

[0134] At this time, according to the threshold fault diagnosis in step S401, the current temperature 70 is higher than 60, and the current power 8 is lower than 10. The threshold result is that there is a super-threshold fault. Then, the multi-dimensional parameter intelligent diagnosis obtains that the terminal state is the super-threshold fault warning state.

[0135] In summary, the present application proposes a clear customized RISC-V proprietary multi-dimensional parameter trend fusion instruction, which improves the method of using a single or simple parameter to diagnose faults on high-power consumption chips to a low-power RISC-V proprietary instruction of a hardware-software integrated execution unit, can calculate the fault trend data value in time, and achieves the effects of greatly improving the diagnostic timeliness and reducing the overall power consumption of the terminal.

[0136] The terminal module persistently stores the octuple diagnostic data (reference time interval, reference wireless signal level, parameter name, highest boundary threshold, lowest boundary threshold, data status, dynamic highest value, dynamic lowest value), and the method of adaptively matching the data collected by the terminal with the octuple diagnostic data, the algorithm for the terminal module to intelligently diagnose and calculate the trend data, and the method flow for using the intelligent diagnosis trend calculation algorithm to predict the potential risk state of the terminal fault greatly improve the accuracy of terminal fault diagnosis and the efficiency of the terminal operation to handle faults.

[0137] Refer to Figure 9 , the embodiment of the present application further provides a terminal multi-dimensional parameter intelligent diagnosis system, which can implement the above terminal multi-dimensional parameter intelligent diagnosis method. The system includes: The first module is used to obtain the triple information of the IoT terminal, and the triple information includes the terminal identifier, the current time interval, and the current wireless signal level.

[0138] The second module is used to match the configuration list of the IoT terminal according to the terminal identifier. The configuration list includes multi-dimensional parameters and threshold boundaries. The multi-dimensional parameters include the reference time interval, the reference wireless signal level, and the parameter name of the reference service data. The threshold boundaries include the highest boundary threshold and the lowest boundary threshold of the reference service data.

[0139] The third module is used to add three fields of data status, dynamic highest value, and dynamic lowest value based on the configuration list to obtain the octuple diagnostic data and persistently store it in the module.

[0140] The fourth module is used to collect the current service data of the IoT terminal, and based on the current time interval and the current wireless signal level, update and process the octuple diagnostic data according to the current service data.

[0141] The fifth module diagnoses the fault state of the updated octuple diagnostic data through the multi-dimensional parameter trend fusion algorithm to obtain a diagnostic result. The multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on the reduced instruction set architecture and is preset in the module.

[0142] It can be understood that the content in the above method embodiments is applicable to the embodiments of this system. The functions specifically implemented in the embodiments of this system are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0143] The embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned intelligent diagnosis method for multi-dimensional parameters of the terminal is implemented. This electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0144] It can be understood that the content in the above method embodiments is applicable to the embodiments of this device. The functions specifically implemented in the embodiments of this device are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0145] Refer to Figure 10 , Figure 10 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes: A processor 901, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0146] A memory 902, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the intelligent diagnosis method for multi-dimensional parameters of the terminal in the embodiments of this application.

[0147] An input / output interface 903, which is used to implement information input and output.

[0148] A communication interface 904, which is used to implement communication interaction between this device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.).

[0149] The bus 905 transmits information among various components of the device, such as the processor 901, the memory 902, the input / output interface 903, and the communication interface 904.

[0150] Among them, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904 achieve communication connections with each other inside the device through the bus 905.

[0151] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned intelligent diagnosis method for terminal multi-dimensional parameters.

[0152] It can be understood that the content in the above method embodiments is applicable to the embodiments of this storage medium. The functions specifically implemented by the embodiments of this storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0153] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and their combinations.

[0154] The embodiments described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0155] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0156] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0157] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in systems and devices, can be implemented as software, firmware, hardware, or a suitable combination thereof.

[0158] The terms "first", "second", "third", "fourth", etc. (if any) in the description of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0159] The preferred embodiments of the embodiments of this application have been described above with reference to the drawings, and this does not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. An intelligent diagnosis method for multi-dimensional parameters of a terminal, characterized in that, The method includes the following steps: Obtain the triple information of the Internet of Things terminal, where the triple information includes the terminal identifier, the current time interval, and the current wireless signal level; Match the configuration list of the Internet of Things terminal according to the terminal identifier. Among them, the configuration list includes multi-dimensional parameters and threshold boundaries. The multi-dimensional parameters include the parameter names of the reference time interval, the reference wireless signal level, and the reference service data. The threshold boundaries include the highest boundary threshold and the lowest boundary threshold of the reference service data; Based on the configuration list, add three fields of data status, dynamic maximum value, and dynamic minimum value to obtain octuple diagnostic data and persistently store it in the module; Collect the current service data of the Internet of Things terminal, and based on the current time interval and the current wireless signal level, update and process the octuple diagnostic data according to the current service data; Perform a fault status diagnosis on the updated octuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result. Among them, the multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on a reduced instruction set architecture and is preset in the module.

2. The method according to claim 1, wherein The step of adding three fields of data status, dynamic maximum value, and dynamic minimum value based on the configuration list to obtain octuple diagnostic data and persistently store it in the module includes the following steps: Perform a parsing process on the configuration list to obtain the five-tuple diagnostic data corresponding to each reference service data. The five-tuple diagnostic data includes the reference time interval, the reference wireless signal level, the parameter name, the highest boundary threshold, and the lowest boundary threshold; Perform an initialization process on the fields of data status, dynamic maximum value, and dynamic minimum value; According to the initialized data status, dynamic maximum value, and dynamic minimum value, splice the five-tuple diagnostic data to obtain octuple diagnostic data and persistently store it in the module.

3. The method according to claim 1, characterized in that The step of updating and processing the octuple diagnostic data according to the current service data based on the current time interval and the current wireless signal level includes the following steps: Match the octuple diagnostic data according to the current time interval, the current wireless signal level, and the parameter name of the current service data; Determine the update strategy according to the data status in the octuple diagnostic data; Based on the update strategy, update the dynamic maximum value and the dynamic minimum value according to the current service data.

4. The method according to claim 3, wherein The step of performing a fault status diagnosis on the updated octuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result includes the following steps: Perform a threshold fault diagnosis on the updated dynamic maximum value and dynamic minimum value according to the highest boundary threshold and the lowest boundary threshold to determine whether there is a super-threshold fault and obtain a threshold result; When the threshold result is that there is a super-threshold fault, determine that the diagnosis result is a super-threshold fault warning state; Alternatively, when the threshold result indicates no over-threshold fault, trend prediction is performed based on the current service data and the updated dynamic maximum value and dynamic minimum value to obtain a prediction result, where the prediction result includes a trend balance value, a trend maximum value, and a trend minimum value; Risk diagnosis is performed on the prediction result according to the highest boundary threshold and the lowest boundary threshold to determine whether there is a potential risk, obtaining a risk result; When the risk result indicates a potential risk, the diagnosis result is determined to be in a potential risk trend state; otherwise, the diagnosis result is determined to be in a stable no-fault trend state.

5. The method according to claim 4, wherein The performing trend prediction based on the current service data and the updated dynamic maximum value and dynamic minimum value to obtain a prediction result includes the following steps: Based on a pre-designed calculation weight, weighted calculation is performed according to the current service data and the updated dynamic maximum value and dynamic minimum value to obtain a trend balance value; Fluctuation quantization processing is performed according to the trend balance value and the updated dynamic minimum value to obtain a trend maximum value and a trend minimum value.

6. The method according to claim 4, wherein The performing risk diagnosis on the prediction result according to the highest boundary threshold and the lowest boundary threshold to determine whether there is a potential risk, obtaining a risk result includes the following steps: Upper limit safety diagnosis is performed on the trend maximum value according to the highest boundary threshold to obtain an upper limit result; Lower limit safety diagnosis is performed on the trend minimum value according to the lowest boundary threshold to obtain a lower limit result; Risk diagnosis is performed according to the upper limit result and the lower limit result to determine whether there is a potential risk, obtaining a risk result; Among them, the performing risk diagnosis according to the upper limit result and the lower limit result to determine whether there is a potential risk, obtaining a risk result includes: When the upper limit result indicates that the trend maximum value is greater than the highest boundary threshold or the lower limit result indicates that the trend minimum value is less than the lowest boundary threshold, it is determined that the risk result indicates a potential risk; Alternatively, when the upper limit result indicates that the trend maximum value is less than the highest boundary threshold and the lower limit result indicates that the trend minimum value is greater than the lowest boundary threshold, it is determined that the risk result indicates no potential risk.

7. The method according to claim 4, wherein The terminal multi-dimensional parameter intelligent diagnosis method further includes the following steps: When the diagnosis result is the over-threshold fault warning state or the potential risk trend state, an alarm message is sent; A fault handling operation is performed according to the alarm message; After completing the fault handling operation, an update instruction is sent to the module to update the data status in the octuple diagnosis data to the initial state.

8. An intelligent diagnosis system for multi-dimensional parameters of a terminal, characterized in that, The system includes: A first module for obtaining triple information of an IoT terminal, where the triple information includes a terminal identifier, a current time interval, and a current wireless signal level; A second module, configured to match a configuration list of the Internet of Things terminal according to the terminal identifier, wherein the configuration list includes multi-dimensional parameters and threshold boundaries, the multi-dimensional parameters include parameter names of a reference time interval, a reference radio signal level, and reference service data, and the threshold boundaries include a highest boundary threshold and a lowest boundary threshold of the reference service data; A third module, configured to add three fields of data status, dynamic maximum value, and dynamic minimum value based on the configuration list, obtain octuple diagnostic data, and persistently store the octuple diagnostic data in the module; A fourth module, configured to collect current service data of the Internet of Things terminal, and update the octuple diagnostic data based on the current service data according to the current time interval and the current radio signal level; A fifth module, configured to perform a fault status diagnosis on the updated octuple diagnostic data through a multi-dimensional parameter trend fusion algorithm to obtain a diagnosis result, wherein the multi-dimensional parameter trend fusion algorithm is obtained by extending instructions based on a reduced instruction set architecture and is pre-set in the module.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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