A method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things

Abstract

The present application discloses a method for automatically monitoring overheating of an intelligent module system based on the Internet of Things, comprising: determining a first overheating judgment result according to temperature monitoring data of a target intelligent module; determining a second overheating judgment result according to working monitoring data of the target intelligent module; determining a third overheating judgment result according to the temperature monitoring data and the working monitoring data, the third overheating judgment result being used to indicate the matching degree between the working temperature and the working state of the target intelligent module; determining an overheating treatment strategy based on the first overheating judgment result, the second overheating judgment result and the third overheating judgment result, and executing the overheating treatment strategy. It can realize effective overheating monitoring and processing, thereby ensuring the safety and stability of the intelligent module system.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things, and in particular to a method for automatically monitoring overheating treatment by an intelligent module system based on Internet of Things. Background Art

[0002] With the development of computer technology, the application of intelligent module systems has become more and more extensive. The intelligent module system based on the Internet of Things can be understood as an Internet of Things system composed of multiple intelligent modules. In this system, multiple intelligent modules are included. These multiple intelligent modules may be input and output devices, or other data processing devices, etc.; no matter what kind of intelligent module, it will execute the data processing process. In the process of data processing by these intelligent modules, the temperature may overheat. Therefore, it is necessary to monitor the temperature of the module for overheating.

[0003] In the related art, overheat monitoring and processing is achieved by monitoring the working temperature of the module; this overheat monitoring and processing method cannot achieve effective overheat monitoring and processing, and thus cannot ensure the safety and stability of the smart module system based on the Internet of Things. Summary of the invention

[0004] The purpose of this application is to provide a method for automatically monitoring overheating treatment of an intelligent module system based on the Internet of Things, which can realize effective overheating monitoring and processing, thereby ensuring the safety and stability of the intelligent module system based on the Internet of Things.

[0005] To achieve the above-mentioned purpose, an embodiment of the present application provides a method for automatically monitoring overheating treatment of an intelligent module system based on the Internet of Things, comprising: determining a first overheating judgment result according to temperature monitoring data of a target intelligent module; the temperature monitoring data comprises a plurality of temperature values ​​within a first preset time period, and the first overheating judgment result is used to indicate whether the operating temperature of the target intelligent module is abnormal; determining a second overheating judgment result according to the operating monitoring data of the target intelligent module; the operating monitoring data comprises a plurality of operating parameter values ​​within a second preset time period, and the second overheating judgment result is used to indicate whether the operating state of the target intelligent module is abnormal, and the second preset time period is determined based on the first overheating judgment result and the first preset time period; determining a third overheating judgment result according to the temperature monitoring data and the operating monitoring data, and the third overheating judgment result is used to indicate the matching degree between the operating temperature and the operating state of the target intelligent module; determining an overheating treatment strategy based on the first overheating judgment result, the second overheating judgment result and the third overheating judgment result, and executing the overheating treatment strategy.

[0006] In a possible implementation, determining a first overheating judgment result based on temperature monitoring data of the target intelligent module includes: determining whether a maximum temperature value and an average temperature value corresponding to the multiple temperature values ​​are both less than a preset temperature value; if so, determining multiple target time points based on the duration of the first preset time period and the historical working duration of the target intelligent module in the intelligent module system based on the Internet of Things; and determining whether the operating temperature of the target intelligent module is abnormal based on the temperature values ​​corresponding to the multiple target time points.

[0007] In a possible implementation, the method of determining a plurality of target time points based on the duration of the first preset time period and the historical working duration of the target intelligent module in the intelligent module system based on the Internet of Things includes: determining the ratio between the historical working duration and the duration of the first preset time period; if the ratio is greater than 1, based on the ratio, uniformly sampling within the first preset time period to determine the plurality of target time points; if the ratio is less than 1, based on the inverse of the ratio, randomly sampling within the first preset time point to determine the plurality of target time points.

[0008] In a possible implementation, the determining whether the operating temperature of the target intelligent module is abnormal based on the temperature values ​​corresponding to the multiple target time points respectively includes: if the multiple target time points are uniformly sampled time points, determining whether the operating temperature of the target intelligent module is abnormal according to whether the change pattern of the temperature values ​​corresponding to the multiple target time points conforms to a preset change pattern; if the multiple target time points are randomly sampled time points, determining whether the operating temperature of the target intelligent module is abnormal according to whether the temperature difference between the temperature values ​​corresponding to the multiple target time points conforms to a preset temperature difference condition.

[0009] In a possible implementation, the process of determining the second preset time period includes: if the first overheating judgment result indicates that the operating temperature of the target smart module is normal, any time point in the first preset time period is used as the starting time point of the second preset time period, and a time point that is a preset time later than the end time point of the first preset time period is used as the end time point of the second preset time period; if the first overheating judgment result indicates that the operating temperature of the target smart module is abnormal, multiple time points are selected in the first preset time period, and the time period composed of the multiple time points is determined as the second preset time period.

[0010] In a possible implementation, the working parameter value includes a working intensity value determined based on the real-time data processing volume, the real-time data processing speed, and the real-time data processing accuracy, and determining the second overheating judgment result according to the working monitoring data of the target intelligent module includes: determining the number of abnormal working parameter values ​​among multiple working parameter values; wherein the abnormal working parameter value is a working intensity value greater than a preset working intensity; determining the number of normal working parameter values ​​among multiple working parameter values; wherein the normal working parameter value is a working intensity value less than a preset working intensity; determining the distribution law of the abnormal working parameters and the normal working parameters; and determining the second overheating judgment result based on the distribution law, the number of abnormal working parameter values, and the number of normal working parameter values.

[0011] In a possible implementation, the second overheating judgment result is determined based on the distribution law, the number of abnormal working parameter values ​​and the number of normal working parameter values, including: if the number of abnormal working parameter values ​​is greater than the number of normal working parameter values, judging whether the distribution law conforms to a first preset distribution law, and if so, determining that the working state of the target intelligent module is normal; wherein, in the first preset distribution law, the working parameter values ​​corresponding to adjacent time points of the continuous distribution time points of the abnormal working parameter values ​​include normal working parameter values; if the number of abnormal working parameter values ​​is less than the number of normal working parameter values, judging whether the distribution law conforms to a second preset distribution law, and if so, determining that the working state of the target intelligent module is normal; wherein, in the second preset distribution law, the distribution time points of the abnormal working parameter values ​​are interspersed between the distribution time points of the normal working parameter values.

[0012] In a possible implementation, determining a third overheating judgment result according to the temperature monitoring data and the working monitoring data includes: determining the temperature variation law of the multiple temperature values; determining the working parameter variation law of the multiple working parameter values; if the temperature variation law and the working parameter variation law are in a directly proportional variation relationship, determining the matching degree to be a first matching degree; if the temperature variation law and the working parameter variation law are in an inversely proportional variation relationship, determining the matching degree to be a second matching degree; if the temperature variation law and the working parameter variation law have no associated variation relationship, determining the matching degree to be a third matching degree; wherein, the first matching degree is greater than the third matching degree, and the third matching degree is greater than the second matching degree.

[0013] In a possible implementation, the overheating processing strategy is determined based on the first overheating judgment result, the second overheating judgment result and the third overheating judgment result, including: if the operating temperature of the target intelligent module is abnormal and the operating state of the target intelligent module is abnormal, and the matching degree is less than a preset matching degree, determining the overheating processing strategy includes: determining a replacement module from an intelligent module system based on the Internet of Things, and transferring the working data of the target intelligent module to the replacement module so that the replacement module processes the working data of the target intelligent module; if the operating temperature of the target intelligent module is abnormal and the working state of the target intelligent module is abnormal, and the matching degree is greater than the preset matching degree, determining the overheating processing strategy includes: determining target working data from the remaining working data of the target intelligent module, and determining the target working data as working data to be allocated; wherein, the allocation time of the working data to be allocated is later than the current time.

[0014] In a possible implementation, the method for automatically monitoring overheating treatment by the intelligent module system based on the Internet of Things also includes: if the operating temperature of the target intelligent module is abnormal, the working state of the target intelligent module is normal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing accuracy of the target intelligent module to be less than a preset accuracy; if the operating temperature of the target intelligent module is normal, the working state of the target intelligent module is abnormal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing amount of the target intelligent module to be less than a preset data processing amount, and / or controlling the real-time data processing speed of the target intelligent module to be less than a preset data processing speed.

[0015] Compared with the prior art, the method for automatically monitoring overheating treatment of an intelligent module system based on the Internet of Things provided in the embodiment of the present application determines a first overheating judgment result based on temperature monitoring data, and determines a second overheating judgment result based on working monitoring data, and also determines a third overheating judgment result in combination with temperature monitoring data and working monitoring data; finally, the three overheating judgment results are integrated to determine the overheating treatment strategy. Therefore, in the scheme for automatically monitoring overheating treatment, overheating judgment is achieved by combining data from both the temperature level and the working status level, and the obtained overheating judgment result is more accurate; furthermore, the corresponding overheating treatment strategy is determined in combination with the overheating judgment result, and the effectiveness of overheating treatment is higher, which can ensure the safety and stability of the target intelligent module. Therefore, it is possible to achieve effective overheating monitoring and processing, thereby ensuring the safety and stability of the intelligent module system based on the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a structural diagram of an intelligent module system based on the Internet of Things according to an embodiment of the present application;

[0017] Figure 2 is a flow chart of a method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to an embodiment of the present application;

[0018] Figure 3 It is a structural schematic diagram of a device for automatically monitoring overheating treatment based on an intelligent module system of the Internet of Things according to an embodiment of the present application;

[0019] Figure 4 It is a schematic diagram of the structure of a terminal device according to one embodiment of the present application. DETAILED DESCRIPTION

[0020] The specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present application is not limited by the specific implementation methods.

[0021] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0022] The technical solution provided in the embodiment of the present application can be applied to an intelligent module system based on the Internet of Things, such as Figure 1 As shown, it is an example diagram of an intelligent module system based on the Internet of Things; it can be seen that the intelligent module system based on the Internet of Things includes multiple intelligent modules, and these multiple intelligent modules can perform the same or different data processing functions. In addition, the intelligent module system based on the Internet of Things also includes a monitoring unit, which is used to implement the method for automatically monitoring overheating provided in the embodiment of the present application.

[0023] The intelligent module system is an Internet of Things-based system, which is configured with relevant Internet of Things links from data collection, data processing, data monitoring, etc.; the communication method between various devices is constructed and implemented based on the Internet of Things.

[0024] For example, the smart module system based on the Internet of Things can be a power system, and each smart module can be a power dispatching device in the power system. The smart module system based on the Internet of Things can also be a communication system, and each smart module can be a communication device in the communication system, such as a relay communication device.

[0025] In some embodiments, one or more intelligent modules correspond to one monitoring unit, for example, multiple intelligent modules with the same function correspond to one monitoring unit.

[0026] In some embodiments, each smart module is provided with a temperature monitoring unit, such as a temperature sensor, which can realize temperature monitoring of each smart module.

[0027] In some embodiments, each smart module is further provided with a working parameter monitoring unit, which can obtain working parameter data of the smart module.

[0028] In the related art, the operating temperature of these smart modules is monitored and processed according to the monitoring results. For example, when the operating temperature is detected to be greater than the preset temperature, it is considered to be overheated. At this time, the data processing amount of the smart module may be reduced to achieve overheating processing.

[0029] This overheating monitoring and processing method cannot achieve effective overheating monitoring and processing, and thus cannot ensure the safety and stability of the smart module system based on the Internet of Things.

[0030] Based on this, the embodiment of the present application provides a solution for automatically monitoring overheating treatment of an intelligent module system based on the Internet of Things, combining data from both the temperature level and the working status level to realize overheating judgment, and the obtained overheating judgment result is more accurate; then, the corresponding overheating treatment strategy is determined in combination with the overheating judgment result, and the effectiveness of overheating treatment is higher, which can ensure the safety and stability of the target intelligent module. Therefore, it can realize effective overheating monitoring and processing, thereby ensuring the safety and stability of the intelligent module system based on the Internet of Things.

[0031] Please refer to the following Figure 2 , is a flow chart of a method for automatically monitoring overheating provided in an embodiment of the present application, the method comprising:

[0032] Step 201, determining a first overheating judgment result according to temperature monitoring data of a target smart module, wherein the temperature monitoring data includes a plurality of temperature values ​​within a first preset time period, and the first overheating judgment result is used to indicate whether the operating temperature of the target smart module is abnormal.

[0033] In some embodiments, the start time point of the first preset time period can be a period of time before the current time, and the end time point can be the current time; wherein, the duration between the start time point and the end time point can be set in combination with different application scenarios, for example: 10 minutes, 20 minutes, etc.

[0034] In some embodiments, the multiple temperature values ​​within the first preset time period include temperature values ​​corresponding to multiple time points, wherein the multiple time points may cover all time points within the first preset time period, or may only include some time points within the first preset time period, which is not limited here.

[0035] In some embodiments, the target intelligent module can be understood as an intelligent module that currently needs to perform overheating monitoring, which can be a core module in the system, etc.

[0036] In some embodiments, a first overheating judgment result is determined based on the temperature monitoring data of the target smart module, including: determining whether the maximum temperature value and the average temperature value corresponding to the multiple temperature values ​​are both less than the preset temperature value; if so, determining multiple target time points based on the length of the first preset time period and the historical working time of the target smart module in the smart module system based on the Internet of Things; based on the temperature values ​​corresponding to the multiple target time points, determining whether the operating temperature of the target smart module is abnormal.

[0037] In this implementation, it is first determined whether the maximum temperature value and the average temperature value are both less than the preset temperature value, and if so, further determinations can be made. If not, it can be directly determined that the operating temperature of the target smart module is abnormal.

[0038] In some embodiments, the preset temperature value can be set in combination with different application scenarios, which represents the upper limit of the overheating process, usually the temperature specified in the system that is considered to have reached overheating.

[0039] In some embodiments, if the judgment result is yes, multiple target time points are first determined according to the duration of the first preset time period and the historical working duration of the target smart module in the smart module system based on the Internet of Things.

[0040] The historical working hours can be recorded in the intelligent module system based on the Internet of Things. For example, if module A has been working in the system for 1 year, the historical working hours is 1 year.

[0041] In some embodiments, multiple target time points are determined based on the length of a first preset time period and the historical working time of the target smart module in the smart module system based on the Internet of Things, including: determining the ratio between the historical working time and the length of the first preset time period; if the ratio is greater than 1, based on the ratio, uniformly sampling within the first preset time period to determine multiple target time points; if the ratio is less than 1, based on the inverse of the ratio, randomly sampling within the first preset time point to determine multiple target time points.

[0042] In some embodiments, the historical working hours are used as the numerator and the duration of the first preset time period is used as the denominator to calculate the ratio.

[0043] In some embodiments, if the ratio is greater than 1, the ratio may be rounded first, and then the relationship between the ratio and the number of multiple time points is determined. If the ratio is less than the number of multiple time points, multiple time points of the ratio are uniformly sampled within the first preset time period. If the ratio is greater than the number of multiple time points, uniform sampling is performed directly from the start time point until the last time point is sampled.

[0044] In some embodiments, uniform sampling refers to: performing time point sampling at fixed time point intervals, for example: the interval between each sampled time point is 1 minute.

[0045] In some embodiments, if the ratio is less than 1, the reciprocal of the ratio is first determined, and then the reciprocal of the ratio is rounded.

[0046] Next, the relationship between the reciprocal of the ratio and the number of multiple time points is determined. If the reciprocal of the ratio is less than the number of multiple time points, multiple time points of the reciprocal of the ratio are randomly sampled within the first preset time period. If the reciprocal of the ratio is greater than the number of multiple time points, random sampling is performed directly from the starting time point until the last time point is sampled.

[0047] In some embodiments, random sampling can be understood as randomly selecting a time point according to a certain random principle.

[0048] Further, after determining the multiple target time points, based on the temperature values ​​corresponding to the multiple target time points, it is determined whether the operating temperature of the target smart module is abnormal. It can be understood that the number of the multiple target time points should be less than the number of all time points.

[0049] Furthermore, based on the temperature values ​​corresponding to multiple target time points, it is determined whether the operating temperature of the target intelligent module is abnormal, including: if the multiple target time points are uniformly sampled time points, it is determined whether the operating temperature of the target intelligent module is abnormal according to whether the change pattern of the temperature values ​​corresponding to the multiple target time points conforms to the preset change pattern; if the multiple target time points are randomly sampled time points, it is determined whether the operating temperature of the target intelligent module is abnormal according to whether the temperature difference between the temperature values ​​corresponding to the multiple target time points conforms to the preset temperature difference condition.

[0050] In some embodiments, the preset change rule can be understood as a preset temperature change rule, and when the temperature change rule is met, the operating temperature can be considered normal. For example, the preset temperature change rule is: the temperature changes within a preset range and gradually increases; or the temperature continues to increase, but the highest temperature does not exceed the preset temperature, etc.

[0051] Therefore, when the changing rule conforms to the preset changing rule, it is determined that the working temperature is normal; when the changing rule does not conform to the preset changing rule, it is determined that the working temperature is abnormal.

[0052] In some embodiments, the temperature difference between the temperature values ​​corresponding to the multiple target time points includes: the temperature difference between the temperature values ​​corresponding to two adjacent target time points. Further, the preset temperature difference condition includes: a temperature difference maximum value condition, a temperature difference minimum value condition and a temperature difference mean value condition. In different application scenarios, these conditions can be set in different ways, which are not limited here.

[0053] Furthermore, if the temperature difference meets the preset temperature difference condition, it is determined that the operating temperature of the target smart module is normal; otherwise, it is determined that the operating temperature of the target smart module is abnormal.

[0054] Step 202, determining a second overheating judgment result according to the working monitoring data of the target intelligent module, wherein the working monitoring data includes a plurality of working parameter values ​​within a second preset time period, the second overheating judgment result is used to indicate whether the working state of the target intelligent module is abnormal, and the second preset time period is determined based on the first overheating judgment result and the first preset time period.

[0055] In some embodiments, the process of determining the second preset time period includes: if the first overheating judgment result indicates that the operating temperature of the target smart module is normal, any time point within the first preset time period is used as the starting time point of the second preset time period, and a time point that is a preset time later than the end time point of the first preset time period is used as the end time point of the second preset time period; if the first overheating judgment result indicates that the operating temperature of the target smart module is abnormal, multiple time points are selected within the first preset time period, and the time period composed of the multiple time points is determined as the second preset time period.

[0056] In some embodiments, the preset duration can be set according to different application scenarios; for example, 5 minutes, 10 minutes, etc.

[0057] In some embodiments, if the end time point of the first preset time period is the current time, it means that the target smart module needs to continue to be monitored for a period of time to determine more overheating judgment results based on the working status monitoring data of the next period of time.

[0058] In some embodiments, if the first overheating judgment result indicates that the operating temperature of the target smart module is abnormal, multiple time points can be directly selected from the first preset time period. The multiple time points can be determined by uniform sampling, random sampling, etc.

[0059] Therefore, there is no fixed time relationship between the first preset time period and the second preset time period, but they need to be determined based on the first preset time period in combination with the first overheating judgment result.

[0060] In some embodiments, the working parameter value includes a working intensity value determined based on the real-time data processing volume, the real-time data processing speed, and the real-time data processing accuracy.

[0061] In some embodiments, the real-time data processing volume, real-time data processing speed and real-time data processing accuracy are respectively configured with corresponding weight values, and the final work intensity value is determined by weighted summing these three data volumes.

[0062] In other embodiments, other methods may be used to combine the three data amounts to determine the work intensity value. In short, the determined work intensity value may reflect the work intensity of the target intelligent module.

[0063] In other embodiments, the work intensity value may be determined in combination with more data amounts, which is not limited here.

[0064] Further, as an optional implementation, determining a second overheating judgment result based on the working monitoring data of the target intelligent module includes: determining the number of abnormal working parameter values ​​among multiple working parameter values; wherein the abnormal working parameter value is a working intensity value greater than a preset working intensity; determining the number of normal working parameter values ​​among multiple working parameter values; wherein the normal working parameter value is a working intensity value less than a preset working intensity; determining the distribution law of abnormal working parameters and normal working parameters; determining the second overheating judgment result based on the distribution law, the number of abnormal working parameter values ​​and the number of normal working parameter values.

[0065] In some embodiments, each working parameter value is compared with a preset working intensity, and a working parameter greater than the preset working intensity is determined as an abnormal working parameter value; and a working parameter less than the preset working intensity is determined as a normal working parameter value.

[0066] In some embodiments, the distribution law can be understood as: the law of time points at which abnormal working parameter values ​​and normal working parameter values ​​are distributed.

[0067] As an optional implementation, based on the distribution law, the number of abnormal working parameter values ​​and the number of normal working parameter values, a second overheating judgment result is determined, including: if the number of abnormal working parameter values ​​is greater than the number of normal working parameter values, whether the distribution law conforms to the first preset distribution law, and if so, determining that the working state of the target intelligent module is normal; wherein, in the first preset distribution law, the working parameter values ​​corresponding to adjacent time points of the continuous distribution time points of the abnormal working parameter values ​​include the normal working parameter values; if the number of abnormal working parameter values ​​is less than the number of normal working parameter values, whether the distribution law conforms to the second preset distribution law, and if so, determining that the working state of the target intelligent module is normal; wherein, in the second preset distribution law, the distribution time points of the abnormal working parameter values ​​are interspersed between the distribution time points of the normal working parameter values.

[0068] In some embodiments, the continuously distributed time points can be understood as the time points at which abnormal working parameter values ​​appear continuously, for example, 1 minute 05 seconds, 1 minute 06 seconds and 1 minute 07 seconds all correspond to abnormal working parameter values, and these three time points are regarded as continuously distributed time points.

[0069] Furthermore, the adjacent time points of the continuous distribution time points are, for example, 1 minute 03 seconds and 1 minute 08 seconds. That is, in the case of continuous abnormality, if there is still a normal working situation before or after, it can be determined that the working state of the target intelligent module is normal.

[0070] In some embodiments, the distribution time points of the abnormal working parameter values ​​are interspersed between the distribution time points of the normal working parameter values, which means that the abnormal working parameter values ​​cannot be distributed continuously, but are distributed between two or more normal working parameters.

[0071] Furthermore, if the above judgment result is that it does not conform to the corresponding preset distribution rule, it is determined that the working state of the target intelligent module is abnormal.

[0072] Step 203: Determine a third overheating judgment result according to the temperature monitoring data and the working monitoring data, wherein the third overheating judgment result is used to indicate the matching degree between the working temperature and the working state of the target intelligent module.

[0073] As an optional implementation, step 203 includes: determining the temperature variation law of multiple temperature values; determining the working parameter variation law of multiple working parameter values; if the temperature variation law and the working parameter variation law are in direct proportion to each other, determining the matching degree as the first matching degree; if the temperature variation law and the working parameter variation law are in inverse proportion to each other, determining the matching degree as the second matching degree; if there is no correlation between the temperature variation law and the working parameter variation law, determining the matching degree as the third matching degree; wherein the first matching degree is greater than the third matching degree, and the third matching degree is greater than the second matching degree.

[0074] In some embodiments, the temperature variation law includes: a temperature variation trend and a temperature variation relationship, and the working parameter variation law includes: a working parameter variation trend and a working parameter variation relationship.

[0075] Examples of change trends include: from small to large, from large to small. Examples of change relationships include: exponential change, linear change.

[0076] Furthermore, in a positive proportional change relationship, the change trend is the same and the change relationship is of the same type. In an inverse proportional change relationship, the change trend is opposite and the change relationship is of the same type. If it is any other change relationship, it is considered to be an unrelated change relationship.

[0077] Furthermore, the first matching degree, the second matching degree and the third matching degree can be set in combination with different application scenarios. For example, the first matching degree is 90, the third matching degree is 50, the second matching degree is 10, and so on.

[0078] Step 204 : determining an overheating treatment strategy based on the first overheating judgment result, the second overheating judgment result, and the third overheating judgment result, and executing the overheating treatment strategy.

[0079] As an optional implementation, step 204 includes: if the operating temperature of the target intelligent module is abnormal and the operating state of the target intelligent module is abnormal, and the matching degree is less than a preset matching degree, determining the overheating treatment strategy includes: determining a replacement module from the intelligent module system based on the Internet of Things, and transferring the working data of the target intelligent module to the replacement module, so that the replacement module processes the working data of the target intelligent module; if the operating temperature of the target intelligent module is abnormal and the working state of the target intelligent module is abnormal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: determining the target working data from the remaining working data of the target intelligent module, and determining the target working data as the working data to be allocated; wherein, the allocation time of the working data to be allocated is later than the current time.

[0080] In some embodiments, the replacement module of the target smart module can be a module of the same type as the target smart module, and the operating temperature and working state of the replacement module are normal, and the matching degree is greater than the preset matching degree. At this time, it is equivalent to stopping the operation of the target smart module, and it can be repaired and restored to normal operation after confirming that it is normal.

[0081] In some embodiments, the target working data may be core working data among the remaining working data; or, data that can be processed by other modules, etc.

[0082] In some embodiments, the target work data is reallocated when the allocation time is reached.

[0083] In some embodiments, there is a certain time interval between the allocated time and the current time, such as 10 minutes, 20 minutes, etc.

[0084] In an embodiment, the preset matching degree may be set in combination with different application scenarios, for example, may be 40.

[0085] In some embodiments, the processing method also includes: if the operating temperature of the target intelligent module is abnormal, the working state of the target intelligent module is normal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing accuracy of the target intelligent module to be less than a preset accuracy; if the operating temperature of the target intelligent module is normal, the working state of the target intelligent module is abnormal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing amount of the target intelligent module to be less than a preset data processing amount, and / or controlling the real-time data processing speed of the target intelligent module to be less than a preset data processing speed.

[0086] In some embodiments, the preset data processing volume, preset data processing speed, and preset accuracy can be set in combination with different application scenarios.

[0087] In some embodiments, if the judgment result is other, it is deemed that no processing is required and the target intelligent module can continue to work.

[0088] Of course, other overheating treatment strategies may also be adopted in combination with different application scenarios, which are not limited here.

[0089] Furthermore, after determining the overheating treatment strategy, the corresponding control unit may be controlled to execute the overheating treatment strategy.

[0090] Through the introduction of the embodiments of the present application, it can be seen that the method for automatically monitoring overheating treatment of the intelligent module system based on the Internet of Things provided by the embodiments of the present application determines the first overheating judgment result according to the temperature monitoring data, and determines the second overheating judgment result according to the working monitoring data, and also determines the third overheating judgment result in combination with the temperature monitoring data and the working monitoring data; finally, the three overheating judgment results are integrated to determine the overheating treatment strategy. Therefore, in the scheme of automatically monitoring overheating treatment, the overheating judgment is realized by combining the data of the temperature level and the working status level, and the obtained overheating judgment result is more accurate; then, the corresponding overheating treatment strategy is determined in combination with the overheating judgment result, and the effectiveness of the overheating treatment is higher, which can ensure the safety and stability of the target intelligent module. Therefore, it can realize effective overheating monitoring and processing, thereby ensuring the safety and stability of the intelligent module system based on the Internet of Things.

[0091] Please refer to the following Figure 3, is a schematic diagram of a structure of a device for automatically monitoring overheating treatment based on an intelligent module system of the Internet of Things provided in an embodiment of the present application, the device comprising:

[0092] A judgment unit 301 is used to determine a first overheating judgment result according to temperature monitoring data of a target intelligent module; the temperature monitoring data includes a plurality of temperature values ​​within a first preset time period, and the first overheating judgment result is used to indicate whether the operating temperature of the target intelligent module is abnormal; a second overheating judgment result is determined according to the operating monitoring data of the target intelligent module; the operating monitoring data includes a plurality of operating parameter values ​​within a second preset time period, and the second overheating judgment result is used to indicate whether the operating state of the target intelligent module is abnormal, and the second preset time period is determined based on the first overheating judgment result and the first preset time period; a third overheating judgment result is determined according to the temperature monitoring data and the operating monitoring data, and the third overheating judgment result is used to indicate a degree of matching between the operating temperature and the operating state of the target intelligent module; a processing unit 302 is used to determine an overheating treatment strategy based on the first overheating judgment result, the second overheating judgment result and the third overheating judgment result, and execute the overheating treatment strategy.

[0093] In some embodiments, the judgment unit 301 is further used to: judge whether the maximum temperature value and the average temperature value corresponding to the multiple temperature values ​​are both less than the preset temperature value; if so, determine multiple target time points according to the duration of the first preset time period and the historical working duration of the target smart module in the IoT-based smart module system; based on the temperature values ​​corresponding to the multiple target time points, determine whether the operating temperature of the target smart module is abnormal.

[0094] In some embodiments, the judgment unit 301 is further used to: determine the ratio between the historical working time and the length of the first preset time period; if the ratio is greater than 1, based on the ratio, uniformly sample within the first preset time period to determine the multiple target time points; if the ratio is less than 1, based on the inverse of the ratio, randomly sample within the first preset time point to determine the multiple target time points.

[0095] In some embodiments, the judgment unit 301 is further used to: if the multiple target time points are uniformly sampled time points, determine whether the operating temperature of the target intelligent module is abnormal according to whether the change pattern of the temperature values ​​corresponding to the multiple target time points conforms to a preset change pattern; if the multiple target time points are randomly sampled time points, determine whether the operating temperature of the target intelligent module is abnormal according to whether the temperature difference between the temperature values ​​corresponding to the multiple target time points conforms to a preset temperature difference condition.

[0096] In some embodiments, the process of determining the second preset time period includes: if the first overheating judgment result indicates that the operating temperature of the target smart module is normal, any time point within the first preset time period is used as the starting time point of the second preset time period, and a time point that is a preset time later than the end time point of the first preset time period is used as the end time point of the second preset time period; if the first overheating judgment result indicates that the operating temperature of the target smart module is abnormal, multiple time points are selected within the first preset time period, and the time period composed of the multiple time points is determined as the second preset time period.

[0097] In some embodiments, the judgment unit 301 is further used to: determine the number of abnormal working parameter values ​​among multiple working parameter values; wherein the abnormal working parameter values ​​are working intensity values ​​greater than a preset working intensity; determine the number of normal working parameter values ​​among multiple working parameter values; wherein the normal working parameter values ​​are working intensity values ​​less than a preset working intensity; determine the distribution law of the abnormal working parameters and the normal working parameters; based on the distribution law, the number of abnormal working parameter values ​​and the number of normal working parameter values, determine the second overheating judgment result.

[0098] In some embodiments, the judgment unit 301 is further used to: if the number of abnormal working parameter values ​​is greater than the number of normal working parameter values, judge whether the distribution law conforms to a first preset distribution law, and if so, determine that the working state of the target intelligent module is normal; wherein, in the first preset distribution law, the working parameter values ​​corresponding to adjacent time points of the continuous distribution time points of the abnormal working parameter values ​​include normal working parameter values; if the number of abnormal working parameter values ​​is less than the number of normal working parameter values, judge whether the distribution law conforms to a second preset distribution law, and if so, determine that the working state of the target intelligent module is normal; wherein, in the second preset distribution law, the distribution time points of the abnormal working parameter values ​​are interspersed between the distribution time points of the normal working parameter values.

[0099] In some embodiments, the judgment unit 301 is further used to: determine the temperature change law of the multiple temperature values; determine the working parameter change law of the multiple working parameter values; if the temperature change law and the working parameter change law are in a positive proportional change relationship, then determine the matching degree to be a first matching degree; if the temperature change law and the working parameter change law are in an inverse proportional change relationship, then determine the matching degree to be a second matching degree; if the temperature change law and the working parameter change law have no associated change relationship, then determine the matching degree to be a third matching degree; wherein, the first matching degree is greater than the third matching degree, and the third matching degree is greater than the second matching degree.

[0100] In some embodiments, the processing unit 302 is further used for: if the operating temperature of the target intelligent module is abnormal and the working state of the target intelligent module is abnormal, and the matching degree is less than the preset matching degree, determining the overheating treatment strategy includes: determining a replacement module from the intelligent module system based on the Internet of Things, and transferring the working data of the target intelligent module to the replacement module, so that the replacement module processes the working data of the target intelligent module; if the operating temperature of the target intelligent module is abnormal and the working state of the target intelligent module is abnormal, and the matching degree is greater than the preset matching degree, determining the overheating treatment strategy includes: determining the target working data from the remaining working data of the target intelligent module, and determining the target working data as the working data to be allocated; wherein, the allocation time of the working data to be allocated is later than the current time.

[0101] In some embodiments, the processing unit 302 is also used for: if the operating temperature of the target intelligent module is abnormal, the working state of the target intelligent module is normal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing accuracy of the target intelligent module to be less than a preset accuracy; if the operating temperature of the target intelligent module is normal, the working state of the target intelligent module is abnormal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing amount of the target intelligent module to be less than a preset data processing amount, and / or controlling the real-time data processing speed of the target intelligent module to be less than a preset data processing speed.

[0102] like Figure 4 As shown, an embodiment of the present application further provides a terminal device, including a processor 401 and a memory 402, wherein the processor 401 and the memory 402 are communicatively connected, and the terminal device can be used as an executor of the aforementioned method of automatically monitoring overheating treatment.

[0103] The processor 401 and the memory 402 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these elements can be electrically connected through one or more communication buses or signal buses. The aforementioned methods for automatically monitoring overheating treatment each include at least one software function module that can be stored in the memory 402 in the form of software or firmware.

[0104] Processor 401 may be an integrated circuit chip with signal processing capability. Processor 401 may be a general-purpose processor, including a CPU (Central Processing Unit), NP (Network Processor), etc.; it may also be a digital signal processor, a dedicated integrated circuit, a readily available programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0105] The memory 402 may store various software programs and modules, such as program instructions / modules corresponding to the image processing method and apparatus provided in the embodiments of the present invention. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402, that is, implementing the method in the embodiments of the present application.

[0106] The memory 402 may include, but is not limited to, RAM (Random Access Memory), ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory), etc.

[0107] Understandably, Figure 4 The structure shown is for illustration only. The terminal device may also include Figure 4 More or fewer components as shown, or with Figure 4 Different configurations shown.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] The foregoing description of the specific exemplary embodiments of the present application is for the purpose of illustration and illustration. These descriptions are not intended to limit the present application to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present application and its practical application, so that those skilled in the art can realize and utilize the various exemplary embodiments of the present application and various selections and changes. The scope of the present application is intended to be limited by the claims and their equivalents.

Claims

1. A method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things, characterized in that: include: Determine a first overheating judgment result according to the temperature monitoring data of the target intelligent module; The temperature monitoring data includes a plurality of temperature values ​​within a first preset time period, and the first overheating judgment result is used to indicate whether the operating temperature of the target smart module is abnormal; determining a second overheating judgment result according to working monitoring data of the target intelligent module; the working monitoring data includes a plurality of working parameter values ​​within a second preset time period, the second overheating judgment result is used to indicate whether the working state of the target intelligent module is abnormal, and the second preset time period is determined based on the first overheating judgment result and the first preset time period; Determine a third overheating judgment result according to the temperature monitoring data and the working monitoring data, wherein the third overheating judgment result is used to indicate a matching degree between the working temperature and the working state of the target smart module; determining an overheating treatment strategy based on the first overheating judgment result, the second overheating judgment result and the third overheating judgment result, and executing the overheating treatment strategy; Determining a third overheating judgment result according to the temperature monitoring data and the operation monitoring data includes: Determine the temperature variation law of the multiple temperature values; the temperature variation law includes: temperature variation trend and temperature variation relationship; Determine a working parameter variation rule of the plurality of working parameter values, wherein the working parameter variation rule includes: a working parameter variation trend and a working parameter variation relationship; If the temperature variation law and the operating parameter variation law are in a positive proportional relationship, determining the matching degree to be a first matching degree; If the temperature variation law and the operating parameter variation law are in an inversely proportional variation relationship, determining the matching degree to be a second matching degree; If there is no correlation between the temperature variation law and the operating parameter variation law, determining the matching degree to be a third matching degree; Wherein, the first matching degree is greater than the third matching degree, and the third matching degree is greater than the second matching degree; The determining of the overheating treatment strategy based on the first overheating judgment result, the second overheating judgment result and the third overheating judgment result includes: If the operating temperature of the target smart module is abnormal and the operating state of the target smart module is abnormal, and the matching degree is less than a preset matching degree, determining the overheating treatment strategy includes: determining a replacement module from the smart module system based on the Internet of Things, transferring the working data of the target smart module to the replacement module, so that the replacement module processes the working data of the target smart module; If the operating temperature of the target intelligent module is abnormal and the operating state of the target intelligent module is abnormal, and the matching degree is greater than the preset matching degree, determining the overheating treatment strategy includes: determining target working data from the remaining working data of the target intelligent module, and determining the target working data as the working data to be allocated; wherein the allocation time of the working data to be allocated is later than the current time; The method for automatically monitoring overheating treatment by the intelligent module system based on the Internet of Things also includes: If the operating temperature of the target intelligent module is abnormal, the operating state of the target intelligent module is normal, and the matching degree is greater than a preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing accuracy of the target intelligent module to be less than a preset accuracy; If the operating temperature of the target intelligent module is normal, the operating state of the target intelligent module is abnormal, and the matching degree is greater than the preset matching degree, determining the overheating treatment strategy includes: controlling the real-time data processing amount of the target intelligent module to be less than the preset data processing amount, and / or controlling the real-time data processing speed of the target intelligent module to be less than the preset data processing speed.

2. The method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to claim 1 is characterized in that: The step of determining a first overheating judgment result according to the temperature monitoring data of the target intelligent module includes: Determine whether the maximum temperature value and the average temperature value corresponding to the multiple temperature values ​​are both less than a preset temperature value; If so, determining a plurality of target time points according to the duration of the first preset time period and the historical working duration of the target smart module in the smart module system based on the Internet of Things; Based on the temperature values ​​respectively corresponding to the multiple target time points, it is determined whether the operating temperature of the target smart module is abnormal.

3. The method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to claim 2 is characterized in that: The determining of a plurality of target time points according to the duration of the first preset time period and the historical working duration of the target smart module in the smart module system based on the Internet of Things includes: Determine the ratio between the historical working time and the time of the first preset time period; If the ratio is greater than 1, based on the ratio, uniform sampling is performed within the first preset time period to determine the multiple target time points; If the ratio is less than 1, based on the inverse of the ratio, random sampling is performed within the first preset time point to determine the multiple target time points.

4. The method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to claim 3 is characterized in that: The determining whether the operating temperature of the target smart module is abnormal based on the temperature values ​​respectively corresponding to the multiple target time points includes: If the multiple target time points are uniformly sampled time points, determining whether the operating temperature of the target smart module is abnormal according to whether the change rules of the temperature values ​​corresponding to the multiple target time points conform to a preset change rule; If the multiple target time points are randomly sampled time points, whether the operating temperature of the target smart module is abnormal is determined based on whether the temperature difference between the temperature values ​​corresponding to the multiple target time points meets a preset temperature difference condition.

5. The method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to claim 1 is characterized in that: The process of determining the second preset time period includes: If the first overheating judgment result indicates that the operating temperature of the target smart module is normal, any time point in the first preset time period is used as the starting time point of the second preset time period, and a time point that is a preset time later than the ending time point of the first preset time period is used as the ending time point of the second preset time period; If the first overheating judgment result indicates that the operating temperature of the target smart module is abnormal, multiple time points are selected within the first preset time period, and the time period consisting of the multiple time points is determined as the second preset time period.

6. The method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to claim 1, characterized in that: The working parameter value includes a working intensity value determined based on the real-time data processing amount, the real-time data processing speed and the real-time data processing accuracy. The second overheating judgment result is determined according to the working monitoring data of the target intelligent module, including: Determine the number of abnormal working parameter values ​​among the multiple working parameter values; wherein the abnormal working parameter value is a working intensity value greater than a preset working intensity; Determine the number of normal working parameter values ​​among the multiple working parameter values; wherein the normal working parameter value is a working intensity value less than a preset working intensity; Determining the distribution rules of the abnormal working parameters and the normal working parameters; The second overheating judgment result is determined based on the distribution law, the number of abnormal operating parameter values, and the number of normal operating parameter values.

7. The method for automatically monitoring overheating treatment by an intelligent module system based on the Internet of Things according to claim 6 is characterized in that: The determining the second overheating judgment result based on the distribution law, the number of abnormal operating parameter values ​​and the number of normal operating parameter values ​​includes: If the number of abnormal working parameter values ​​is greater than the number of normal working parameter values, determine whether the distribution law conforms to a first preset distribution law, and if so, determine that the working state of the target intelligent module is normal; wherein, in the first preset distribution law, the working parameter values ​​corresponding to adjacent time points of the continuous distribution time points of the abnormal working parameter values ​​include normal working parameter values; If the number of abnormal working parameter values ​​is less than the number of normal working parameter values, determine whether the distribution law conforms to the second preset distribution law. If so, determine that the working state of the target intelligent module is normal; wherein, in the second preset distribution law, the distribution time points of the abnormal working parameter values ​​are interspersed between the distribution time points of the normal working parameter values.

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