Intelligent terminal data processing method and system based on edge computing

Abstract

The application discloses an intelligent terminal data processing method and system based on edge computing, and the method comprises the following steps: acquiring equipment monitoring data sent by monitoring equipment arranged in a photovoltaic production area; determining a processing performance parameter corresponding to the equipment monitoring data based on a prediction algorithm; determining an abnormality possibility parameter of the equipment monitoring data based on a data analysis algorithm; judging whether the equipment monitoring data is sent to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter; and the intelligent terminal is used for sending a processing result to a cloud terminal after processing the equipment monitoring data. It can be seen that the application can improve the efficiency and real-time performance of data processing, avoid non-key data from occupying cloud resources, reduce the computing burden of the cloud, and enhance the intelligent scheduling capability and data processing flexibility of the overall system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an intelligent terminal data processing method and system based on edge computing. BACKGROUND

[0002] In existing photovoltaic production monitoring technologies, device monitoring data is usually directly uploaded to the cloud for unified processing and analysis to realize monitoring and management of device running states. Although this method can centrally process a large amount of data, it has problems such as large data transmission pressure, slow response speed, and low utilization efficiency of cloud resources, especially in the case of limited processing performance or network fluctuations, which may cause abnormal data to be unable to be processed in time, affecting fault identification and system operation and maintenance efficiency. Some solutions attempt to introduce an edge computing architecture, but lack an intelligent judgment mechanism for data processing priority, and cannot dynamically select a processing path according to the importance or abnormal characteristics of the data itself, resulting in the inability of the edge computing capability to fully play a role, affecting the real-time performance and intelligent level of the overall system. It can be seen that the existing technology has defects and needs to be solved. SUMMARY

[0003] The technical problem to be solved by the present application is to provide an intelligent terminal data processing method and system based on edge computing, which can improve the efficiency and real-time performance of data processing, avoid non-critical data occupying cloud resources, reduce the computing burden of the cloud, and enhance the intelligent scheduling capability and data processing flexibility of the overall system.

[0004] To solve the above technical problems, the present application discloses an intelligent terminal data processing method based on edge computing in the first aspect, which comprises:

[0005] Obtaining device monitoring data sent by a monitoring device arranged in a photovoltaic production area;

[0006] Determining a processing performance parameter corresponding to the device monitoring data based on a prediction algorithm;

[0007] Determining an abnormality possibility parameter of the device monitoring data based on a data analysis algorithm;

[0008] According to the processing performance parameter and the abnormality possibility parameter, it is judged whether the device monitoring data is sent to a local intelligent terminal for edge computing processing; the intelligent terminal is used to send the processing result to a cloud terminal after processing the device monitoring data.

[0009] As an optional implementation, in the first aspect of the present application, the device monitoring data includes at least one of device temperature, device humidity, device image, device sound, device vibration, device working data, and device communication data.

[0010] As an optional implementation, in the first aspect of the present application, the determining of the processing performance parameter corresponding to the device monitoring data based on the prediction algorithm comprises:

[0011] inputting the device monitoring data into the trained processing task prediction neural network to obtain an output processing task set; the processing task prediction neural network is trained by a training data set comprising a plurality of training monitoring data and corresponding processing task labels;

[0012] determining the processing performance parameter corresponding to the device monitoring data according to the processing task set and a preset correspondence between processing tasks and performance parameters.

[0013] As an optional implementation, in the first aspect of the present application, the determining of the processing performance parameter corresponding to the device monitoring data according to the processing task set and a preset correspondence between processing tasks and performance parameters comprises:

[0014] for each predicted processing task in the processing task set, determining a performance parameter set corresponding to each predicted processing task according to the preset correspondence between processing tasks and performance parameters; the performance parameter set comprises a plurality of required performance parameters of multiple parameter types;

[0015] calculating the intersection of all parameter types corresponding to the performance parameter sets of all predicted processing tasks to obtain a parameter type set corresponding to the device monitoring data;

[0016] for each parameter type in the parameter type set, calculating the average of the parameter values of the parameter type in all performance parameter sets to obtain a performance parameter value corresponding to the parameter type;

[0017] determining the performance parameter values corresponding to all parameter types in the parameter type set as the processing performance parameter corresponding to the device monitoring data.

[0018] As an optional implementation, in the first aspect of the present application, the parameter type is a processor clock frequency parameter, a cache capacity parameter, a core number parameter, a thread number parameter, a power consumption parameter, a memory bandwidth parameter, a hard disk storage capacity parameter, a floating point operation frequency parameter or an integer operation frequency parameter.

[0019] As an optional implementation, in the first aspect of the present application, the determining of the abnormality possibility parameter of the device monitoring data based on the data analysis algorithm comprises:

[0020] for each monitoring data in the device monitoring data, determining the data type and the monitoring object device corresponding to the monitoring data;

[0021] determining a plurality of historical normal data corresponding to the data type and the monitoring object device in a historical database;

[0022] calculating an average value of all the historical normal data to obtain a reference data value;

[0023] calculating a difference between the monitoring data and the reference data value;

[0024] calculating a weighted sum average value of all the differences corresponding to the monitoring data to obtain an abnormality likelihood parameter of the device monitoring data.

[0025] As an optional implementation, in the first aspect of the present application, when calculating the weighted sum average value of all the differences corresponding to the monitoring data, the weight corresponding to each difference includes a first weight and a second weight; the first weight is proportional to the data amount of the corresponding historical normal data; and the second weight is proportional to the historical abnormal frequency corresponding to the monitoring object device.

[0026] As an optional implementation, in the first aspect of the present application, the judging whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality likelihood parameter includes:

[0027] judging whether the processing performance parameter is less than a first parameter threshold to obtain a first judgment result;

[0028] judging whether the abnormality likelihood parameter is less than a second parameter threshold to obtain a second judgment result;

[0029] when the first judgment result and the second judgment result are both yes, sending the device monitoring data to the local intelligent terminal for edge computing processing.

[0030] The second aspect of the embodiment of the present application discloses an intelligent terminal data processing system based on edge computing, and the system includes:

[0031] an acquisition module configured to acquire device monitoring data sent by a monitoring device arranged in a photovoltaic production area;

[0032] a prediction module configured to determine a processing performance parameter corresponding to the device monitoring data based on a prediction algorithm;

[0033] an analysis module configured to determine an abnormality likelihood parameter of the device monitoring data based on a data analysis algorithm;

[0034] A judgment module is configured to judge whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter; and the intelligent terminal is configured to send a processing result to a cloud terminal after processing the device monitoring data.

[0035] As an optional implementation, in the second aspect of the present application, the device monitoring data includes at least one of device temperature, device humidity, device image, device sound, device vibration, device working data and device communication data.

[0036] As an optional implementation, in the second aspect of the present application, the specific manner in which the prediction module determines the processing performance parameter corresponding to the device monitoring data based on the prediction algorithm includes:

[0037] inputting the device monitoring data into the trained processing task prediction neural network to obtain an output processing task set; the processing task prediction neural network is trained by a training data set including a plurality of training monitoring data and corresponding processing task labels;

[0038] determining the processing performance parameter corresponding to the device monitoring data according to the processing task set and a preset correspondence between processing tasks and performance parameters.

[0039] As an optional implementation, in the second aspect of the present application, the specific manner in which the prediction module determines the processing performance parameter corresponding to the device monitoring data according to the processing task set and the preset correspondence between processing tasks and performance parameters includes:

[0040] for each predicted processing task in the processing task set, determining a performance parameter set corresponding to each predicted processing task according to the preset correspondence between processing tasks and performance parameters; the performance parameter set includes a plurality of required performance parameters of multiple parameter types;

[0041] calculating the intersection of all parameter types corresponding to the performance parameter sets of all predicted processing tasks to obtain a parameter type set corresponding to the device monitoring data;

[0042] for each parameter type in the parameter type set, calculating the average value of the parameter value of the parameter type in all performance parameter sets to obtain a performance parameter value corresponding to the parameter type;

[0043] determining the performance parameter values corresponding to all parameter types in the parameter type set as the processing performance parameter corresponding to the device monitoring data.

[0044] As an optional implementation, in the second aspect of the present application, the parameter type is a processor clock frequency parameter, a cache capacity parameter, a core number parameter, a thread number parameter, a power consumption parameter, a memory bandwidth parameter, a hard disk storage capacity parameter, a floating point operation frequency parameter, or an integer operation frequency parameter.

[0045] As an optional implementation, in the second aspect of the present application, the specific manner in which the analysis module determines the abnormality likelihood parameter of the device monitoring data based on a data analysis algorithm includes:

[0046] For each monitoring data in the device monitoring data, the data type and the monitored object device corresponding to the monitoring data are determined;

[0047] A plurality of historical normal data corresponding to the data type and the monitored object device are determined in a historical database;

[0048] The average value of all the historical normal data is calculated to obtain a reference data value;

[0049] The difference between the monitoring data and the reference data value is calculated;

[0050] The weighted sum average value of the difference corresponding to all the monitoring data is calculated to obtain the abnormality likelihood parameter of the device monitoring data.

[0051] As an optional implementation, in the second aspect of the present application, when the weighted sum average value of the difference corresponding to all the monitoring data is calculated, the weight corresponding to each difference includes a first weight and a second weight; the first weight is proportional to the data amount of the corresponding historical normal data; and the second weight is proportional to the historical abnormal frequency corresponding to the corresponding monitored object device.

[0052] As an optional implementation, in the second aspect of the present application, the specific manner in which the judgment module judges whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality likelihood parameter includes:

[0053] It is judged whether the processing performance parameter is less than a first parameter threshold to obtain a first judgment result;

[0054] It is judged whether the abnormality likelihood parameter is less than a second parameter threshold to obtain a second judgment result;

[0055] When the first judgment result and the second judgment result are both yes, the device monitoring data is sent to a local intelligent terminal for edge computing processing.

[0056] The third aspect of the present application discloses another intelligent terminal data processing system based on edge computing, which comprises:

[0057] a memory storing executable program codes;

[0058] a processor coupled with the memory;

[0059] The processor invokes the executable program codes stored in the memory to execute part or all steps of the intelligent terminal data processing method based on edge computing disclosed in the first aspect of the present application.

[0060] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, which are invoked to execute part or all steps of the intelligent terminal data processing method based on edge computing disclosed in the first aspect of the present application.

[0061] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0062] The present application can improve the efficiency and real-time performance of data processing by performing performance analysis and abnormality possibility evaluation on the equipment monitoring data of the photovoltaic production area, and combining the two types of parameters to determine whether to send the equipment monitoring data to the local intelligent terminal for edge computing processing, thereby avoiding non-critical data occupying cloud resources, reducing the computing burden of the cloud, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0064] Figure 1 is a flow diagram of an intelligent terminal data processing method based on edge computing disclosed by the embodiments of the present application.

[0065] Figure 2 is a structural diagram of an intelligent terminal data processing system based on edge computing disclosed by the embodiments of the present application.

[0066] Figure 3 is a structural diagram of another intelligent terminal data processing system based on edge computing disclosed by the embodiments of the present application. DETAILED DESCRIPTION

[0067] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0068] The terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, rather than to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or equipment including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or equipment.

[0069] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood that the embodiments described herein can be combined with other embodiments.

[0070] The present application discloses an intelligent terminal data processing method and system based on edge computing. The method performs performance analysis and abnormality possibility evaluation on equipment monitoring data of a photovoltaic production area, and determines whether to send the equipment monitoring data to a local intelligent terminal for edge computing processing in combination with two types of parameters, so as to improve the efficiency and real-time performance of data processing, avoid non-key data from occupying cloud resources, reduce the computing burden of the cloud, and enhance the intelligent scheduling capability and data processing flexibility of the overall system. The following will be described in detail.

[0071] Embodiment one

[0072] Please refer to Figure 1 , Figure 1 is a flowchart of an intelligent terminal data processing method based on edge computing disclosed by the embodiments of the present application. Among them, Figure 1 The described intelligent terminal data processing method based on edge computing can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 1 shown, the intelligent terminal data processing method based on edge computing can include the following operations:

[0073] 101. Obtain device monitoring data sent by a monitoring device arranged in a photovoltaic production area.

[0074] 102. Determine a processing performance parameter corresponding to the device monitoring data based on a prediction algorithm.

[0075] 103. Determine an abnormality possibility parameter of the device monitoring data based on a data analysis algorithm.

[0076] 104. Determine whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter.

[0077] Optionally, the intelligent terminal is configured to send a processing result to a cloud terminal after processing the device monitoring data.

[0078] As can be seen, the above embodiments can improve the efficiency and real-time performance of data processing, avoid non-critical data occupying cloud resources, reduce the computing burden of the cloud, and enhance the intelligent scheduling capability and data processing flexibility of the overall system by analyzing the processing performance and evaluating the abnormality possibility of the device monitoring data of the photovoltaic production area and determining whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the two parameters.

[0079] As an optional embodiment, in the above steps, the device monitoring data includes at least one of device temperature, device humidity, device image, device sound, device vibration, device working data, and device communication data.

[0080] As can be seen, the above optional embodiments limit the content of the device monitoring data to comprehensively represent the running-related features of the monitored device, so as to facilitate the subsequent accurate calculation of the path and assist in improving the efficiency and real-time performance of data processing, avoiding non-critical data occupying cloud resources, reducing the computing burden of the cloud, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0081] As an optional embodiment, in the above steps, the processing performance parameter corresponding to the device monitoring data is determined based on a prediction algorithm, including:

[0082] inputting the device monitoring data into a trained processing task prediction neural network to obtain an output processing task set; optionally, the processing task prediction neural network is trained by a training data set including a plurality of training monitoring data and corresponding processing task labels;

[0083] determining the processing performance parameter corresponding to the device monitoring data according to the processing task set and a preset correspondence between processing tasks and performance parameters.

[0084] It can be seen that, through the above optional embodiments, by inputting the device monitoring data into the trained processing task prediction neural network, the output contains the processing task set required to be executed by the data, and the corresponding relationship between the preset task and the performance parameter is combined to determine the processing performance parameter corresponding to the device monitoring data, so as to intelligently evaluate the processing complexity and resource occupation based on the specific processing requirements of the data, and improve the accuracy of subsequent computing resource scheduling and system running efficiency.

[0085] As an optional embodiment, in the above step, according to the processing task set and the preset corresponding relationship between the processing task and the performance parameter, the processing performance parameter corresponding to the device monitoring data is determined, including:

[0086] For each predicted processing task in the processing task set, according to the preset corresponding relationship between the processing task and the performance parameter, a performance parameter set corresponding to each predicted processing task is determined; optionally, the performance parameter set includes multiple demand performance parameters of multiple parameter types;

[0087] Calculate the intersection of all parameter types corresponding to the performance parameter set of all predicted processing tasks to obtain a parameter type set corresponding to the device monitoring data;

[0088] For each parameter type in the parameter type set, calculate the average value of the parameter value of the parameter type in all performance parameter sets to obtain the performance parameter value corresponding to the parameter type;

[0089] The performance parameter values corresponding to all parameter types in the parameter type set are determined as the processing performance parameter corresponding to the device monitoring data.

[0090] It can be seen that, through the above optional embodiments, by performing parameter type merging and aggregation processing on the multiple performance parameter sets corresponding to the predicted processing tasks, the common parameter types in the performance parameter sets corresponding to all predicted processing tasks are first extracted, and then the parameter values of each parameter type are averaged, and finally the processing performance parameter that comprehensively and accurately reflects the processing requirements of the device monitoring data is determined, so as to realize fine modeling of data processing complexity and quantitative evaluation of resource consumption, and improve the rationality and stability of edge computing resource scheduling and task distribution.

[0091] As an optional embodiment, in the above step, the parameter type is a processor clock frequency parameter, a cache capacity parameter, a core number parameter, a thread number parameter, a power consumption parameter, a memory bandwidth parameter, a hard disk storage capacity parameter, a floating point operation frequency parameter, or an integer operation frequency parameter.

[0092] It can be seen that through the above optional embodiments, the type of processing performance parameter is limited to determine the processing performance parameter that comprehensively and accurately reflects the device monitoring data processing demand, thereby assisting in improving the efficiency and real-time performance of data processing, avoiding non-critical data occupying cloud resources, reducing the cloud computing burden, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0093] As an optional embodiment, in the above step, the abnormal possibility parameter of the device monitoring data is determined based on a data analysis algorithm, including:

[0094] For each monitoring data in the device monitoring data, the data type and the monitoring object device corresponding to the monitoring data are determined;

[0095] A plurality of historical normal data corresponding to the data type and the monitoring object device are determined in the historical database;

[0096] The average value of all historical normal data is calculated to obtain a reference data value;

[0097] The difference between the monitoring data and the reference data value is calculated;

[0098] The weighted sum average value of the difference corresponding to all monitoring data is calculated to obtain the abnormal possibility parameter of the device monitoring data.

[0099] It can be seen that through the above optional embodiments, the historical normal data is accurately filtered based on the data type and the monitoring object device, and the overall deviation degree of the current monitoring data is quantitatively evaluated by using the difference weighted sum average method, which can effectively generate the abnormal possibility parameter reflecting the abnormal degree of the current device state, thereby assisting in improving the efficiency and real-time performance of data processing, avoiding non-critical data occupying cloud resources, reducing the cloud computing burden, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0100] As an optional embodiment, in the above step, when calculating the weighted sum average value of the difference corresponding to all monitoring data, the weighted calculation weight corresponding to each difference includes a first weight and a second weight; the first weight is proportional to the data amount of the corresponding historical normal data; and the second weight is proportional to the historical abnormal frequency corresponding to the corresponding monitoring object device.

[0101] It can be seen that, through the above optional embodiments, by introducing the first weight proportional to the number of historical normal data and the second weight proportional to the historical abnormal frequency of the monitoring object device in the difference weighted sum average calculation, the credibility of the historical data and the device abnormal risk level can be fully considered when evaluating the abnormal possibility of the current monitoring data, thereby improving the accuracy and discrimination of the abnormal possibility parameter, assisting in improving the efficiency and real-time performance of data processing, avoiding non-key data occupying cloud resources, reducing the cloud computing burden, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0102] As an optional embodiment, in the above steps, according to the processing performance parameter and the abnormal possibility parameter, it is judged whether the device monitoring data is sent to the local intelligent terminal for edge computing processing, comprising:

[0103] It is judged whether the processing performance parameter is less than the first parameter threshold, and a first judgment result is obtained;

[0104] It is judged whether the abnormal possibility parameter is less than the second parameter threshold, and a second judgment result is obtained;

[0105] When the first judgment result and the second judgment result are both yes, the device monitoring data is sent to the local intelligent terminal for edge computing processing.

[0106] It can be seen that, through the above optional embodiments, by respectively judging whether the processing performance parameter and the abnormal possibility parameter corresponding to the device monitoring data are lower than the respective preset thresholds, and triggering the edge computing processing operation when both meet the conditions, the data is only processed by the local intelligent terminal in the case that the device is in good running state and has no abnormal risk, thereby improving the safety and reliability of edge computing, and avoiding causing waste of computing resources or processing deviation in high-risk or complex computing scenarios, and finally realizing the precision of edge computing task scheduling and the stability guarantee of photovoltaic monitoring system operation.

[0107] Embodiment two

[0108] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of an intelligent terminal data processing system based on edge computing disclosed by the embodiments of the present application. Among them, Figure 2 The edge computing based intelligent terminal data processing system described can be applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 2 shown, the edge computing based intelligent terminal data processing system can include:

[0109] The acquisition module 201 is configured to acquire device monitoring data sent by a monitoring device arranged in a photovoltaic production area.

[0110] a prediction module 202, configured to determine a processing performance parameter corresponding to the equipment monitoring data based on a prediction algorithm.

[0111] an analysis module 203, configured to determine an abnormality possibility parameter of the equipment monitoring data based on a data analysis algorithm.

[0112] a judgment module 204, configured to determine whether to send the equipment monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter.

[0113] Optionally, the intelligent terminal is configured to send a processing result to a cloud terminal after processing the equipment monitoring data.

[0114] It can be seen that the above embodiments of the application can improve the efficiency and real-time performance of data processing, avoid non-critical data occupying cloud resources, reduce the computing burden of the cloud, and enhance the intelligent scheduling capability and data processing flexibility of the overall system by analyzing the processing performance of the equipment monitoring data of the photovoltaic production area and evaluating the abnormality possibility, and combining the two types of parameters to determine whether to send the equipment monitoring data to a local intelligent terminal for edge computing processing.

[0115] As an optional embodiment, the equipment monitoring data includes at least one of device temperature, device humidity, device image, device sound, device vibration, device working data, and device communication data.

[0116] It can be seen that the above optional embodiments limit the content of the equipment monitoring data to comprehensively represent the running-related features of the monitored equipment, so as to facilitate the subsequent accurate determination of the calculation path, and assist in improving the efficiency and real-time performance of data processing, avoiding non-critical data occupying cloud resources, reducing the computing burden of the cloud, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0117] As an optional embodiment, the specific manner in which the prediction module determines the processing performance parameter corresponding to the equipment monitoring data based on the prediction algorithm includes:

[0118] inputting the equipment monitoring data into a trained processing task prediction neural network to obtain an output processing task set; optionally, the processing task prediction neural network is trained by a training data set including a plurality of training monitoring data and corresponding processing task labels;

[0119] determining the processing performance parameter corresponding to the equipment monitoring data according to the processing task set and a preset correspondence between processing tasks and performance parameters.

[0120] It can be seen that, through the above optional embodiments, by inputting the device monitoring data into the trained processing task prediction neural network, the output contains the processing task set required to be executed by the data, and the corresponding relationship between the preset task and the performance parameter is combined to determine the processing performance parameter corresponding to the device monitoring data, so as to intelligently evaluate the processing complexity and resource occupation based on the specific processing requirements of the data, and improve the accuracy of subsequent computing resource scheduling and system running efficiency.

[0121] As an optional embodiment, the specific manner of the prediction module for determining the processing performance parameter corresponding to the device monitoring data according to the processing task set and the corresponding relationship between the preset processing task and the performance parameter includes:

[0122] For each predicted processing task in the processing task set, the corresponding performance parameter set of each predicted processing task is determined according to the corresponding relationship between the preset processing task and the performance parameter; optionally, the performance parameter set includes multiple demand performance parameters of multiple parameter types;

[0123] The intersection of all parameter types corresponding to the performance parameter set of all predicted processing tasks is calculated to obtain the parameter type set corresponding to the device monitoring data;

[0124] For each parameter type in the parameter type set, the average value of the parameter value of the parameter type in all performance parameter sets is calculated to obtain the performance parameter value corresponding to the parameter type;

[0125] The performance parameter values corresponding to all parameter types in the parameter type set are determined as the processing performance parameter corresponding to the device monitoring data.

[0126] It can be seen that, through the above optional embodiments, by performing parameter type merging and aggregation processing on the multiple performance parameter sets corresponding to the predicted processing tasks, the common parameter types in the performance parameter sets corresponding to all predicted processing tasks are first extracted, and then the parameter values of each parameter type are averaged, and finally the processing performance parameter that comprehensively and accurately reflects the processing requirements of the device monitoring data is determined, so as to realize fine modeling of data processing complexity and quantitative evaluation of resource consumption, and improve the rationality and stability of edge computing resource scheduling and task distribution.

[0127] As an optional embodiment, the parameter type is a processor clock frequency parameter, a cache capacity parameter, a core number parameter, a thread number parameter, a power consumption parameter, a memory bandwidth parameter, a hard disk storage capacity parameter, a floating point operation frequency parameter, or an integer operation frequency parameter.

[0128] It can be seen that through the above optional embodiments, the types of processing performance parameters are limited to determine the processing performance parameters that comprehensively and accurately reflect the device monitoring data processing requirements, thereby assisting in improving the efficiency and real-time performance of data processing, avoiding non-critical data occupying cloud resources, reducing the cloud computing burden, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0129] As an optional embodiment, the specific manner of determining the abnormality possibility parameter of the device monitoring data by the analysis module based on the data analysis algorithm includes:

[0130] For each monitoring data in the device monitoring data, the data type and the monitored object device corresponding to the monitoring data are determined.

[0131] A plurality of historical normal data corresponding to the data type and the monitored object device are determined in the historical database.

[0132] The average value of all historical normal data is calculated to obtain a reference data value.

[0133] The difference between the monitoring data and the reference data value is calculated.

[0134] The weighted sum average value of the differences corresponding to all monitoring data is calculated to obtain the abnormality possibility parameter of the device monitoring data.

[0135] It can be seen that through the above optional embodiments, the historical normal data is accurately filtered based on the data type and the monitored object device, and the overall deviation degree of the current monitoring data is quantitatively evaluated by using the difference weighted sum average method, which can effectively generate the abnormality possibility parameter reflecting the abnormality degree of the current device state, thereby assisting in improving the efficiency and real-time performance of data processing, avoiding non-critical data occupying cloud resources, reducing the cloud computing burden, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0136] As an optional embodiment, when calculating the weighted sum average value of the differences corresponding to all monitoring data, the weighted calculation weight corresponding to each difference includes a first weight and a second weight; the first weight is proportional to the data amount of the corresponding historical normal data; and the second weight is proportional to the historical abnormal frequency corresponding to the corresponding monitored object device.

[0137] It can be seen that, through the above optional embodiments, by introducing the first weight proportional to the number of historical normal data and the second weight proportional to the historical abnormal frequency of the monitored object device in the difference weighted sum average calculation, the credibility of the historical data and the device abnormal risk level can be fully considered when evaluating the abnormal possibility of the current monitoring data, thereby improving the accuracy and discrimination of the abnormal possibility parameter, assisting in improving the efficiency and real-time performance of data processing, avoiding non-key data occupying cloud resources, reducing the cloud computing burden, and enhancing the intelligent scheduling capability and data processing flexibility of the overall system.

[0138] As an optional embodiment, the specific manner in which the judgment module judges whether to send the device monitoring data to the local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormal possibility parameter includes:

[0139] judging whether the processing performance parameter is less than a first parameter threshold to obtain a first judgment result;

[0140] judging whether the abnormal possibility parameter is less than a second parameter threshold to obtain a second judgment result;

[0141] When the first judgment result and the second judgment result are both yes, the device monitoring data is sent to the local intelligent terminal for edge computing processing.

[0142] It can be seen that, through the above optional embodiments, by respectively judging whether the processing performance parameter and the abnormal possibility parameter corresponding to the device monitoring data are lower than the respective preset thresholds, and triggering the edge computing processing operation when both meet the conditions, the data is only processed by the local intelligent terminal in the case that the device is in good operating state and has no abnormal risk, thereby improving the safety and reliability of edge computing, while avoiding causing waste of computing resources or processing deviation in high-risk or complex computing scenarios, and finally realizing the precision of edge computing task scheduling and the stability guarantee of the photovoltaic monitoring system.

[0143] Embodiment Three

[0144] Please refer to Figure 3 , Figure 3 The application discloses another kind of intelligent terminal data processing system based on edge computing. Figure 3 The described intelligent terminal data processing system based on edge computing is applied to data processing system / data processing equipment / data processing server (wherein the server includes local processing server or cloud processing server). As Figure 3 The intelligent terminal data processing system based on edge computing can include:

[0145] a memory 301 storing executable program codes;

[0146] A processor 302 coupled to the memory 301;

[0147] The processor 302 invokes the executable program code stored in the memory 301 to perform the steps of the edge computing based intelligent terminal data processing method described in Embodiment One.

[0148] Embodiment Four

[0149] The embodiments of the present application disclose a computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform the steps of the edge computing based intelligent terminal data processing method described in Embodiment One.

[0150] Embodiment Five

[0151] The embodiments of the present application disclose a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the edge computing based intelligent terminal data processing method described in Embodiment One.

[0152] The above describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0153] The systems, apparatuses, modules or units illustrated by the above embodiments specifically can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0154] For the convenience of description, the above apparatuses are described in functions and divided into various units to be described respectively. Of course, the functions of each unit can be implemented in the same or more software and / or hardware when implementing the present specification.

[0155] Those skilled in the art will appreciate that embodiments of the present description can be readily used as a method, an apparatus (system) or a computer program product. Accordingly, embodiments of the present description can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer readable program code.

[0156] The present description is described in reference to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present description. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flow diagram and / or block diagram block or blocks.

[0157] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flow diagram and / or block diagram block or blocks.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flow diagram and / or block diagram block or blocks.

[0159] In one typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0160] The memory can include non-persistent memory and / or persistent memory, such as flash memory, read-only memory (ROM), and / or random access memory (RAM), among others. The memory is an example of computer-readable media.

[0161] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0162] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0163] The specification 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, and the like that perform specific tasks or implement specific abstract data types. The specification can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0164] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0165] It should be noted that the intelligent terminal data processing method and system based on edge computing disclosed in the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. The modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An edge-computing-based intelligent terminal data processing method, characterized in that, The method comprises: obtaining equipment monitoring data sent by monitoring equipment arranged in a photovoltaic production area; determining a processing performance parameter corresponding to the equipment monitoring data based on a prediction algorithm; determining an abnormality possibility parameter of the equipment monitoring data based on a data analysis algorithm, comprising: for each monitoring data in the equipment monitoring data, determining a data type and a monitoring object equipment corresponding to the monitoring data; determining a plurality of historical normal data corresponding to the data type and the monitoring object equipment in a historical database; calculating an average value of all the historical normal data to obtain a reference data value; calculating a difference value between the monitoring data and the reference data value; calculating a weighted sum average value of the difference values corresponding to all the monitoring data to obtain the abnormality possibility parameter of the equipment monitoring data; when calculating the weighted sum average value of the difference values corresponding to all the monitoring data, a weight corresponding to each difference value comprises a first weight and a second weight; the first weight is proportional to a data amount of the corresponding historical normal data; the second weight is proportional to a historical abnormal frequency corresponding to the corresponding monitoring object equipment; judging whether to send the equipment monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter; the intelligent terminal is used to send a processing result to a cloud terminal after processing the equipment monitoring data. 2.The edge computing based intelligent terminal data processing method according to claim 1, characterized in that, The equipment monitoring data comprises at least one of equipment temperature, equipment humidity, equipment image, equipment sound, equipment vibration, equipment working data and equipment communication data. 3.The edge computing based intelligent terminal data processing method of claim 1, wherein, The method comprises: inputting the equipment monitoring data into a trained processing task prediction neural network to obtain an output processing task set; the processing task prediction neural network is trained by a training data set comprising a plurality of training monitoring data and corresponding processing task labels; determining the processing performance parameter corresponding to the equipment monitoring data according to the processing task set and a preset correspondence between processing tasks and performance parameters. 4.The edge computing based intelligent terminal data processing method of claim 3, wherein, The method comprises: for each predicted processing task in the processing task set, determining a performance parameter set corresponding to each predicted processing task according to the preset correspondence between processing tasks and performance parameters; the performance parameter set comprises a plurality of required performance parameters of multiple parameter types; calculating an intersection of all parameter types corresponding to the performance parameter sets of all the predicted processing tasks to obtain a parameter type set corresponding to the equipment monitoring data; for each parameter type in the parameter type set, calculating an average value of parameter values of the parameter type in all the performance parameter sets to obtain a performance parameter value corresponding to the parameter type; determining the performance parameter values corresponding to all the parameter types in the parameter type set as the processing performance parameter corresponding to the equipment monitoring data. 5.The edge computing based intelligent terminal data processing method according to claim 4, characterized in that, The parameter type is a processor clock frequency parameter, a cache capacity parameter, a core number parameter, a thread number parameter, a power consumption parameter, a memory bandwidth parameter, a hard disk storage capacity parameter, a floating point operation frequency parameter, or an integer operation frequency parameter. 6.The edge computing based intelligent terminal data processing method according to claim 1, characterized in that, The judging whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter comprises: judging whether the processing performance parameter is less than a first parameter threshold to obtain a first judgment result; judging whether the abnormality possibility parameter is less than a second parameter threshold to obtain a second judgment result; when the first judgment result and the second judgment result are both yes, sending the device monitoring data to the local intelligent terminal for edge computing processing.

7. An edge computing based intelligent terminal data processing system, characterized in that, The system comprises: an acquisition module configured to acquire device monitoring data sent by a monitoring device arranged in a photovoltaic production area; a prediction module configured to determine a processing performance parameter corresponding to the device monitoring data based on a prediction algorithm; an analysis module configured to determine an abnormality possibility parameter of the device monitoring data based on a data analysis algorithm, comprising: for each monitoring data in the device monitoring data, determining a data type and a monitored object device corresponding to the monitoring data; determining a plurality of historical normal data corresponding to the data type and the monitored object device in a historical database; calculating an average value of all the historical normal data to obtain a reference data value; calculating a difference value between the monitoring data and the reference data value; calculating a weighted sum average value of the difference values corresponding to all the monitoring data to obtain the abnormality possibility parameter of the device monitoring data; when calculating the weighted sum average value of the difference values corresponding to all the monitoring data, a weighted calculation weight corresponding to each difference value comprises a first weight and a second weight; the first weight is proportional to a data amount of the corresponding historical normal data; and the second weight is proportional to a historical abnormal frequency corresponding to the corresponding monitored object device; a judgment module configured to judge whether to send the device monitoring data to a local intelligent terminal for edge computing processing according to the processing performance parameter and the abnormality possibility parameter; the intelligent terminal is configured to send a processing result to a cloud terminal after processing the device monitoring data.

8. An edge computing based intelligent terminal data processing system, characterized in that, The system comprises: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the edge computing-based intelligent terminal data processing method according to any one of claims 1-6.

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