Intelligent terminal and data processing method thereof

Abstract

The invention relates to an intelligent terminal and a data processing method thereof. The method comprises the steps that data integrity determination is conducted on monitoring data stored in a storage module, and when it is determined that abnormal data exists in the monitoring data according to a data integrity determination result, whether multiple pieces of abnormal data exist in a preset time range or not is judged; if not, the first processor core reconstructs the abnormal data by adopting a three-time Hemite interpolation method, and backfills the reconstructed abnormal data to a corresponding position in the storage module, and if yes, the first processor core outputs a data recollection instruction to the second processor core; the second processor core is instructed to reacquire and store the monitoring data through the external signal acquisition interface; by adopting the method, the data can be conveniently and quickly responded and processed on the end side, the cloud computing pressure is relieved, meanwhile, the cloud does not need to clean the monitoring data after subsequently receiving the monitoring data output by the intelligent terminal side, and the data transmission efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution and utilization management, and in particular to an intelligent terminal and a data processing method thereof. Background Art

[0002] With the development of power Internet of Things technology, massive numbers of smart terminals are connected to the power grid, and data is facing explosive growth. To ensure the stable operation of the power grid, it is necessary to collect and store data on the distribution network side for subsequent monitoring, risk prediction and other applications.

[0003] At present, data on the distribution network side is usually collected by smart terminals and then aggregated to the cloud for processing. As a result, the cloud needs to clean and integrate massive amounts of data before applying the collected data, which greatly increases the cloud's computing time. If invalid data appears in the process of data sorting, the cloud needs to communicate and interact with the terminal side in order to obtain valid data again, which greatly increases the data throughput and the data transmission delay, resulting in low data transmission efficiency. Summary of the Invention

[0004] Based on this, it is necessary to provide a data processing method for smart terminals that can reduce cloud computing pressure and improve data transmission efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a data processing method for an intelligent terminal, wherein the intelligent terminal includes a control module and a storage module connected to each other, the control module includes a first processor core and a second processor core connected to each other, the first processor core and the second processor core are both connected to the storage module, and the second processor core is used to connect to an external signal acquisition interface; the method is applied to the first processor core, and the method includes:

[0006] Performing data integrity determination on the monitoring data stored in the storage module, and when it is determined based on the data integrity determination result that abnormal data exists in the monitoring data, determining whether there are multiple abnormal data within a preset time range;

[0007] If not, the abnormal data is reconstructed using the cubic Hemite interpolation method, and the reconstructed abnormal data is backfilled to the corresponding position in the storage module;

[0008] If so, a data re-collection instruction is output to the second processor core, where the data re-collection instruction is used to instruct the second processor core to re-collect the monitoring data through the external signal collection interface, and send the re-collected monitoring data to the storage module for storage.

[0009] In one embodiment, determining the data integrity of monitoring data stored in a storage module includes:

[0010] In the case where blank data exists in the monitoring data, the blank data is determined as abnormal data; and

[0011] Obtaining the numerical values ​​corresponding to the remaining data, and determining the remaining data corresponding to the numerical values ​​outside the preset numerical range as abnormal data, wherein the remaining data is the data in the monitoring data excluding blank data; and / or,

[0012] According to the 3 sigma principle, identify abnormal data contained in the remaining data; and / or,

[0013] The quartile method was used to identify abnormal data contained in the remaining data.

[0014] In one embodiment, the abnormal data is reconstructed using a cubic Hemite interpolation method, including:

[0015] The cubic Hermite interpolation polynomial is established using the preceding and following data corresponding to the abnormal data, and the abnormal data is reconstructed based on the cubic Hermite interpolation polynomial.

[0016] In a second aspect, the present application further provides a data processing method for an intelligent terminal, wherein the intelligent terminal includes a control module and a storage module connected to each other, the control module includes a first processor core and a second processor core connected to each other, the first processor core and the second processor core are both connected to the storage module, and the second processor core is used to connect to an external signal acquisition interface; the method is applied to the second processor core, and the method includes:

[0017] In response to the data recollection instruction output by the first processor core, the monitoring data is recollected through the external signal collection interface, and the recollected monitoring data is sent to the storage module for storage.

[0018] In one embodiment, the second processor core is further configured to connect to the cloud; the method further comprises:

[0019] Periodically sample monitoring data through an external signal acquisition interface;

[0020] Output the acquired monitoring data to the storage module for storage;

[0021] The monitoring data is exported to the cloud at preset time intervals.

[0022] In a third aspect, the present application further provides an intelligent terminal, comprising a control module and a storage module connected to each other, the control module comprising a first processor core and a second processor core connected to each other, the first processor core and the second processor core both being connected to the storage module, and the second processor core being configured to connect to an external signal acquisition interface;

[0023] The first processor core is used to execute the steps of the above method; the second processor core is used to execute the steps of the above method.

[0024] In one embodiment, the memory module is a shared DDR.

[0025] In one embodiment, the control module further includes a first bus, and the first processor core and the second processor core are both connected to the storage module via the first bus.

[0026] In one embodiment, the control module further includes a second bus, and the second processor core is connected to the cloud via the second bus.

[0027] In one embodiment, the external signal acquisition interface includes an ADC sampling interface, an RS232 communication interface, an RS485 communication interface, and a telemetry signal interface.

[0028] The above-mentioned smart terminal and its data processing method, the first processor core in the smart terminal determines the data integrity of the monitoring data stored in the storage module, and when it is determined that there is abnormal data in the monitoring data based on the data integrity determination result, it determines whether there are multiple abnormal data within a preset time range; if not, the first processor core uses the cubic Hemite interpolation method to reconstruct the abnormal data, and backfills the reconstructed abnormal data to the corresponding position in the storage module. If so, the first processor core outputs a data re-collection instruction to the second processor core to instruct the second processor core to re-collect and store the monitoring data through the external signal acquisition interface; this application provides different processor cores in the control module of the smart terminal, so that the smart terminal can timely discover and process abnormal data when collecting monitoring data, facilitates rapid response and processing of data on the terminal side, reduces the computing pressure on the cloud, and at the same time, facilitates the cloud to subsequently receive the monitoring data output by the smart terminal side without having to clean the monitoring data, and directly use the data according to application requirements, thereby improving data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0030] FIG1 is a block diagram of a smart terminal according to an embodiment;

[0031] FIG2 is a schematic flow chart of a data processing method of an intelligent terminal in one embodiment;

[0032] FIG3 is a schematic flow chart of a data processing method of an intelligent terminal in another embodiment;

[0033] FIG4 is a schematic diagram of the structure of an intelligent terminal in one embodiment. DETAILED DESCRIPTION

[0034] To facilitate understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The accompanying drawings provide embodiments of the present application. However, the present application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. It will be understood that the terms "first," "second," etc., used herein may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element.

[0036] As used herein, the singular forms "a," "an," and "the" may also include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include," "comprising," "having," and the like specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof. Furthermore, the term "and / or" as used in this specification includes any and all combinations of the relevant listed items.

[0037] Currently, new power systems are showing a trend of developing with a high proportion of renewable energy. To better adapt to the development trend of massive new energy access, the Power Internet of Things is one of the current directions of power development. The Power Internet of Things is one of the specific manifestations of the new power system. By fully utilizing modern information technology and advanced communications technologies, it connects various intelligent devices and services in the power system, realizing the interconnection of all things and human-computer interaction. This will enhance the level of informationization of the power system, improve the utilization efficiency of the existing power system infrastructure, and provide important technical support for the power grid's generation, transmission, transformation, distribution, and utilization.

[0038] With the gradual development of power IoT technology, a vast number of smart terminals are being connected, leading to explosive data growth. Smart terminal perception is the underlying interface of the power IoT. Leveraging the data collection capabilities of smart terminals, we ensure comprehensive perception and interaction of power grid terminal status, ensuring safe grid operation and improving both grid operation quality and overall societal energy efficiency. However, during data collection, smart terminals also face data anomalies, such as data acquisition failure and data value deviations. These issues can lead to ineffective perception and waste of resources. Consequently, higher demands are placed on data accuracy and availability. Data cleaning, which effectively removes anomalies from data by identifying outliers and reconstructing missing values, is key to ensuring data application.

[0039] Currently, data cleansing for distribution network monitoring data primarily involves smart terminals collecting monitoring data and then uploading it to the cloud for centralized processing, enabling subsequent applications such as system monitoring and risk prediction. Existing technical solutions primarily utilize gateway communication, point-to-point device transfer, and manual transfer of storage media to upload data to the cloud. Gateway communication primarily targets terminal devices (smart terminals) accessible by the communication network, while point-to-point device transfer and manual transfer of storage media primarily target terminal devices beyond network coverage. However, these approaches can waste data transmission resources and inefficiency when encountering data collection errors. Cloud-based unified processing, on the other hand, involves the cloud integrating and cleaning all collected monitoring data before applying it to select valid, usable data. This approach offers advantages such as centralized processing and high computing power, but it also increases computing pressure on the cloud, often resulting in insufficient computing power when executing high-computing applications.

[0040] It should be noted that smart terminals can be equipped with distribution network monitoring and control chips to collect and transmit monitoring data. These chips can be deployed in a distributed manner using edge gateways and edge processors, enabling local data collection, processing, protocol conversion, and data analysis, significantly alleviating pressure on network transmission and data centers. Furthermore, edge processing effectively ensures low latency for smart terminal business processing, reducing processing speeds from seconds to milliseconds, with strong responsiveness and real-time performance. However, in conventional monitoring, only a portion of the computing power of the distribution network monitoring and control chips is utilized, leaving the computing power of each smart terminal's distribution network monitoring and control chips idle. This does not fully tap the computing power of the chips underlying the Power Internet of Things (POI), leading to insufficient terminal-side computing power utilization.

[0041] The data processing method of the smart terminal provided in the embodiment of the present application can be applied to the smart terminal as shown in Figure 1; wherein the smart terminal includes a control module 10 and a storage module 20 connected to each other, the control module 10 includes a first processor core 102 and a second processor core 104 connected to each other, the first processor core 102 and the second processor core 104 are both connected to the storage module 20, and the second processor core 104 is used to connect to the external signal acquisition interface 30; in the process of collecting and processing edge data (monitoring data), the first processor core 102 has the functions of integrity monitoring, data processing and data backfilling of the monitoring data; data integrity determination is mainly used to monitor the missing and noise points of the monitoring data, data processing is mainly to reconstruct the problem data (abnormal data), and data backfilling is used to store the repaired data (reconstructed abnormal data) It is placed in a designated location in the storage module; the second processor core 104 has the functions of data collection, data storage and data re-collection. Data collection mainly realizes the collection of power grid monitoring data, data storage mainly performs temporary storage of monitoring data, and data re-collection is re-sampling of data that cannot be processed. Finally, the second processor core 104 outputs the processed monitoring data to the cloud at preset time intervals, which facilitates rapid response and processing of data on the terminal side, reduces the computing pressure on the cloud, and facilitates the cloud to subsequently receive data output by the smart terminal side without having to clean the data, and directly use the data according to application requirements, thereby improving data transmission efficiency; at the same time, effective data processing and data resampling avoid additional data transmission between the cloud and the terminal, improve the efficiency of data transmission, and better utilize the computing power resources of each processor core.

[0042] For example, the control module can be a multi-core network distribution monitoring chip. Smart terminals can include, but are not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices can include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like.

[0043] In an exemplary embodiment, as shown in FIG2 , a data processing method for an intelligent terminal is provided. The method is described by taking the application of the method to the first processor core in FIG1 as an example. The method includes:

[0044] S202 , performing data integrity determination on the monitoring data stored in the storage module, and when it is determined based on the data integrity determination result that abnormal data exists in the monitoring data, determining whether there are multiple abnormal data within a preset time range.

[0045] Among them, the preset time range can be set according to actual conditions and is not limited in the embodiments of this application.

[0046] Specifically, the first processor core monitors the monitoring data stored in the storage module by the second processor core, and detects the integrity of the monitoring data, such as whether there is data noise, data missing, etc.; wherein, the monitoring data may carry a corresponding sampling time. When the first processor core determines that there is abnormal data in the monitoring data based on the data integrity determination result, it obtains the sampling time corresponding to each abnormal data, and determines whether there are multiple abnormal data within a preset time range. It can be understood that the first processor core determines whether there are other abnormal data within a certain time range before and after the abnormal data.

[0047] S204: If not, use the cubic Hemite interpolation method to reconstruct the abnormal data, and backfill the reconstructed abnormal data to the corresponding position in the storage module.

[0048] Specifically, when it is determined that there are no multiple abnormal data within the preset time range, the first processor core uses the cubic Hemite interpolation method to reconstruct the abnormal data. The first processor core can obtain the corresponding position information of the abnormal data stored in the storage module, and backfill the reconstructed abnormal data to the corresponding position in the storage module according to the position information.

[0049] Exemplarily, the first processor core may also use monitoring data adjacent to the abnormal data for reconstruction, that is, the abnormal data may be filled with monitoring data (normal data) whose sampling time is close to the sampling time corresponding to the abnormal data, which will not be elaborated in the embodiments of the present application.

[0050] S206: If yes, output a data re-collection instruction to the second processor core, where the data re-collection instruction is used to instruct the second processor core to re-collect the monitoring data through the external signal collection interface and send the re-collected monitoring data to the storage module for storage.

[0051] Among them, the communication method between the first processor core and the second processor core can be set according to actual conditions. In the embodiment of the present application, the communication between the first processor core and the second processor core using mail mailboxes is used as an example for explanation.

[0052] Specifically, when it is determined that there are multiple abnormal data within a preset time range, this can be indicated as the existence of abnormal data that cannot be corrected, and data collection needs to be performed again. The first processor core outputs a data re-collection instruction to the second processor core. The second processor core responds to the received data re-collection instruction, re-collects the monitoring data through the external signal acquisition interface, and sends the re-collected monitoring data to the storage module for storage, thereby ensuring the validity of the monitoring data collected during each sampling period; wherein the monitoring data may include meteorological information such as temperature, humidity, wind speed, electrical signals such as voltage and current, and open and close signals (control signals for power devices in the distribution network).

[0053] In the data processing method of the above-mentioned smart terminal, the first processor core in the smart terminal determines the data integrity of the monitoring data stored in the storage module, and when it is determined that there is abnormal data in the monitoring data based on the data integrity determination result, it determines whether there are multiple abnormal data within the preset time range; if not, the first processor core uses the cubic Hemite interpolation method to reconstruct the abnormal data, and backfills the reconstructed abnormal data to the corresponding position in the storage module. If so, the first processor core outputs a data re-collection instruction to the second processor core to instruct the second processor core to re-collect and store the monitoring data through the external signal acquisition interface, so that the smart terminal can timely discover and process the abnormal data when collecting monitoring data, facilitate the rapid response and processing of data on the terminal side, reduce the computing pressure on the cloud, and at the same time, facilitate the subsequent cloud to receive the monitoring data output by the smart terminal side without having to clean the monitoring data, and directly use the data according to application requirements, thereby improving data transmission efficiency; in addition, different processor cores each have different functions, which better utilize the computing power resources of each processor core.

[0054] In one embodiment, determining the data integrity of monitoring data stored in a storage module includes:

[0055] In the case where blank data exists in the monitoring data, the blank data is determined as abnormal data; and

[0056] Obtaining the numerical values ​​corresponding to the remaining data, and determining the remaining data corresponding to the numerical values ​​outside the preset numerical range as abnormal data, wherein the remaining data is the data in the monitoring data excluding blank data; and / or,

[0057] According to the 3 sigma principle, identify abnormal data contained in the remaining data; and / or,

[0058] The quartile method was used to identify abnormal data contained in the remaining data.

[0059] Among them, the preset numerical range can be set according to actual conditions and is not limited in the embodiments of this application.

[0060] Specifically, the first processor core can first perform data missing judgment on the monitoring data, where the data missing judgment criterion mainly determines whether the collected detection data is NULL (blank data). If so, it is considered that there is missing data, and the blank data is determined as abnormal data, and the remaining data is continued to be judged for data noise. The first processor core can use any one or any combination of the numerical size judgment method, the 3sigma principle and the quartile method to make the judgment.

[0061] It should be noted that the numerical value judgment method is a method of obtaining the numerical value corresponding to the remaining data and judging whether the remaining data is abnormal data based on the numerical value. The first processor core has a built-in maximum threshold and minimum threshold. When the numerical value corresponding to the remaining data is greater than or equal to the maximum threshold, or when the numerical value corresponding to the remaining data is less than or equal to the minimum threshold (the numerical value is outside the preset numerical range), the remaining data is determined to be abnormal data (data noise).

[0062] The 3sigma principle uses the remaining data to obtain the standard deviation σ and the average value μ. When the numerical value corresponding to the remaining data is greater than or equal to the first numerical value, or when the numerical value corresponding to the remaining data is less than or equal to the second numerical value, the remaining data is determined to be abnormal data (data noise); wherein, the standard deviation σ can be updated in real time by the cloud and sent to the first processor core, and the average value μ can be obtained by the first processor core based on the numerical size and quantity of each remaining data. The first value is the sum of the average value μ and three times the standard deviation σ, and the second value is the difference between the average value μ and three times the standard deviation σ.

[0063] The quartile method is to sort the remaining data from small to large according to the numerical value, and confirm the numbers in the upper 1 / 4 position, that is, the numbers in the 75% position as the upper quartile Q1, the numbers in the upper 1 / 2 position, that is, the numbers in the 50% position as the median Q2, and the numbers in the lower 1 / 4 position, that is, the numbers in the 25% position as the lower quartile Q3. Among them, the median Q2 is greater than the upper quartile Q1, and the median Q2 is less than the lower quartile Q3; the distance I_DIS between the upper quartile Q1 and the lower quartile Q3 is obtained according to the following formula (1):

[0064] I_DIS = Q1 - Q3 (1);

[0065] After obtaining the upper and lower quartile distances I_DIS, the maximum judgment threshold Max_value and the minimum judgment threshold Min_value are obtained according to equations (2) and (3):

[0066] Max_value= Q3 - 1.5 * I_DIS (2);

[0067] Min_value= Q1 + 1.5 * I_DIS (3);

[0068] When the value corresponding to the remaining data is greater than or equal to the maximum judgment threshold Max_value, or when the value corresponding to the remaining data is less than or equal to the minimum judgment threshold Min_value, the remaining data is determined to be abnormal data (data noise).

[0069] In the embodiment of the present application, multiple methods are adopted to determine the data integrity of the monitoring data, abnormal data is discovered in a timely manner, and the accuracy of the monitoring data is improved, so that the cloud can directly use the monitoring data according to application requirements in the subsequent process, thereby improving the application efficiency of the monitoring data on the distribution network side.

[0070] In one embodiment, the abnormal data is reconstructed using a cubic Hemite interpolation method, including:

[0071] The cubic Hermite interpolation polynomial is established using the preceding and following data corresponding to the abnormal data, and the abnormal data is reconstructed based on the cubic Hermite interpolation polynomial.

[0072] It should be noted that the number of front and back data can be set according to actual conditions and is not limited in the embodiments of this application.

[0073] Specifically, the first processor core uses the previous and subsequent data with similar sampling times corresponding to the abnormal data to establish a cubic Hermite interpolation polynomial. The cubic Hermite interpolation polynomial can be used to characterize the relationship between the sampling time and the numerical value corresponding to the data. The sampling time corresponding to the abnormal data and the cubic Hermite interpolation polynomial are used to obtain reconstructed data, and the abnormal data is replaced with the reconstructed data, and the reconstructed abnormal data (reconstructed data) is backfilled to the corresponding position in the storage module.

[0074] In an embodiment of the present application, the first processor core in the smart terminal can complete data cleaning and data reconstruction of the monitoring data, reducing the throughput in the subsequent data transmission process with the cloud, while reducing the delay caused by data transmission speed and bandwidth limitations, alleviating the data pressure on the cloud, and enabling the cloud to better serve distribution network data monitoring.

[0075] In an exemplary embodiment, a data processing method for an intelligent terminal is provided. The method is described by taking the application of the method to the second processor core in FIG. 1 as an example. The method includes:

[0076] In response to the data recollection instruction output by the first processor core, the monitoring data is recollected through the external signal collection interface, and the recollected monitoring data is sent to the storage module for storage.

[0077] Specifically, the second processor core has the functions of data re-collection and data storage. When receiving the data re-collection instruction output by the first processor core, the second processor core re-collects the monitoring data through the external signal acquisition interface. The data acquisition function is mainly realized by the interaction between the interface in the distribution network detection chip (control module) and the external signal acquisition interface. The external signal acquisition interface can be implemented according to actual sampling requirements using ADC sampling interface, RS232 communication interface, RS485 communication interface, telemetry signal interface and other interfaces, which can realize the collection of meteorological information such as temperature, humidity, wind speed, electrical signals such as voltage and current, and open and close signals; the second processor core stores the re-collected monitoring data in the storage module for subsequent data upload and for the first processor core to determine the data integrity of the monitoring data.

[0078] In the data processing method of the above-mentioned smart terminal, the second processor core responds to the data re-collection instruction output by the first processor core, re-collects the monitoring data through the external signal acquisition interface, and sends the re-collected monitoring data to the storage module for storage. That is, by directly cleaning the monitoring data on the smart terminal side, the validity of the monitoring data is guaranteed; the second processor core has built-in data collection, data storage, and data re-collection functions, which reduces the delay caused by the subsequent data transmission speed and bandwidth limitations on the cloud, alleviates the data pressure on the cloud, and can better serve the distribution network data monitoring. At the same time, when it comes to abnormal data that cannot be corrected, the second processor core uses the data re-collection function to execute the monitoring data collection again, ensuring the validity of the monitoring data during each sampling period and improving the data transmission efficiency.

[0079] In one embodiment, the second processor core is further configured to connect to the cloud; the method further comprises:

[0080] Periodically sample monitoring data through an external signal acquisition interface;

[0081] Output the acquired monitoring data to the storage module for storage;

[0082] The monitoring data is exported to the cloud at preset time intervals.

[0083] Among them, the preset time interval can be set according to actual conditions and is not limited in the embodiments of the present application; the time interval of periodic sampling can be set according to actual conditions and is not limited in the embodiments of the present application.

[0084] Specifically, the second processor core periodically samples the monitoring data through the external signal acquisition interface, and outputs the acquired monitoring data to the storage module for storage, so as to facilitate the subsequent data integrity determination of the monitoring data by the first processor core. At every preset time interval, the second processor core outputs the monitoring data with completed data integrity determination to the cloud.

[0085] In the embodiment of the present application, the first processor core has the functions of data integrity determination, data processing and data backfilling, and the second processor core has the functions of data collection, data storage and data recollection, thereby better utilizing the computing power resources of each processor core.

[0086] To facilitate understanding by those skilled in the art, the data processing method (edge ​​data processing flow) of a smart terminal is described below with reference to a specific example. As shown in Figure 3, through the parallel collaborative processing of the CPU_0 core and the CPU_1 core in the end-side chip, the collected data can be quickly processed on the end-side, which can fully tap the computing power of the end-side chip, reduce the computational load of data processing in the cloud, and promote the application of high-order algorithms in the cloud. Among them, the end-side chip is the control module, the shared DDR is the storage module, the CPU_1 core is the first processor core, and the CPU_0 core is the second processor core.

[0087] Step 1: The distribution network monitoring control chip determines whether the sampling cycle has been reached (set based on actual conditions) or whether a data resampling instruction has been received. According to the distribution network monitoring process, external data (monitoring data) on the distribution network side must be periodically sampled and uploaded to the upper-level unit (the cloud). It should be noted that the distribution network monitoring control chip is located within the control module and is connected to the CPU_0 and CPU_1 cores. When the CPU_0 core receives a data resampling instruction, it executes the data resampling function. When the sampling cycle is determined to have been reached, a periodic sampling instruction is output to the CPU_0 core, instructing it to execute the data collection function.

[0088] Step 2: This step is mainly used to determine whether the trigger signal is a periodic data sampling or data resampling instruction. When the instruction received by the CPU_0 core is a data resampling instruction, execute step 6; when the instruction received is a periodic sampling instruction, execute step 3.

[0089] Step 3: This step primarily involves data sampling performed by the CPU_0 core. This data sampling function is primarily implemented through the interaction between the interface within the control chip (control module) and the external signal acquisition interface. This external signal acquisition interface can be implemented using an ADC sampling interface, RS232 communication interface, RS485 communication interface, or telemetry signal interface, depending on actual sampling requirements. It can collect meteorological information such as temperature, humidity, and wind speed, electrical signals such as voltage and current, and signals such as opening and closing.

[0090] Step 4: In this step, the CPU_0 core stores the collected monitoring data in the shared DDR space for subsequent monitoring data upload and data integrity monitoring (data integrity determination) by the CPU_1 core.

[0091] Step 5: This step is mainly performed by CPU_0 to periodically send out the monitoring data that has completed the data integrity determination.

[0092] Step 6: After receiving the data recollection instruction sent by CPU_1 core, CPU_0 core executes the data recollection function and jumps to step 1 after execution.

[0093] Step 7: This step is mainly performed by the CPU_1 core monitoring the monitoring data stored by the CPU_0 core in the shared DDR space, and detecting the integrity of the data, such as data noise, data missing, etc.; wherein, the step of detecting the integrity of the data is the same as the above-mentioned step of determining the data integrity of the monitoring data stored in the storage module, and will not be repeated in the embodiment of this application.

[0094] Step 8: This step primarily processes outlier data. This step primarily reconstructs data from noise points and missing data points (outliers). There are two main methods for data reconstruction: reconstructing outlier data using adjacent missing values; and reconstructing using cubic Hemite interpolation. If outliers exist before and after a data point (multiple outliers within the preset time range), the data is considered unrecoverable within the sampling period and requires resampling.

[0095] Step 9: This step mainly determines whether there is data that cannot be repaired. If so, jump to step 10; if not, jump to step 11.

[0096] Step 10: When receiving data that cannot be repaired, CPU_1 sends a data resampling instruction to CPU_0. The communication between the cores is primarily through email.

[0097] Step 11: When the repaired data (reconstructed data) is received, the CPU_1 core performs a data backfill function to backfill the reconstructed data to the reconstructed data. The data exists in the shared DDR space.

[0098] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0099] In an exemplary embodiment, as shown in FIG1 , a smart terminal is provided. The smart terminal includes a control module 10 and a storage module 20 connected to each other. The control module 10 includes a first processor core 102 and a second processor core 104 connected to each other. The first processor core 102 and the second processor core 104 are both connected to the storage module 20. The second processor core 104 is used to connect to an external signal acquisition interface 30.

[0100] The first processor core 102 is used to execute the steps of the above method; the second processor core 104 is used to execute the steps of the above method.

[0101] Specifically, in the process of collecting and processing edge data (monitoring data), the first processor core 102 has the functions of monitoring the integrity of the monitoring data, data processing, and data backfilling. Data integrity determination is mainly used to monitor the missing and noise points of the monitoring data. Data processing is mainly used to reconstruct the problem data (abnormal data). Data backfilling is used to store the repaired data (reconstructed abnormal data) in a specified location in the storage module. The second processor core 104 has the functions of data collection, data storage, and data resampling. Data collection mainly realizes the collection of power grid monitoring data. Data storage is mainly for temporary storage of monitoring data. Data resampling is for resampling unprocessable data. Finally, the second processor core 104 outputs the processed monitoring data to the cloud at preset time intervals, facilitating rapid response and processing of data on the end side, reducing the computing pressure on the cloud. At the same time, it is convenient for the cloud to subsequently use the data directly according to application requirements after receiving the data output by the smart terminal side without having to clean the data, thereby improving data transmission efficiency. At the same time, effective data processing and data resampling avoid additional data transmission between the cloud and the end, improve data transmission efficiency, and better utilize the computing power resources of each processor core.

[0102] In one embodiment, as shown in FIG4 , the memory module is a shared DDR (Double Data Rate) synchronous dynamic random access memory.

[0103] It should be noted that, as shown in FIG4 , the CPU_1 core is the first processor core, and the CPU_0 core is the second processor core.

[0104] Specifically, as shown in FIG4 , both the CPU_1 core and the CPU_0 core are connected to the shared DDR, that is, both the CPU_1 core and the CPU_0 core can use the shared DDR space.

[0105] In one embodiment, as shown in FIG4 , the control module further includes a first bus, and the first processor core and the second processor core are both connected to the storage module via the first bus.

[0106] It should be noted that, as shown in FIG4 , the black vertical line represents the first bus.

[0107] In one embodiment, as shown in FIG4 , the control module further includes a second bus, and the second processor core is connected to the cloud via the second bus.

[0108] It should be noted that, as shown in FIG4 , the blue vertical line represents the second bus.

[0109] Specifically, the second processor core is connected to a PHY (Physical Layer) interface via a second bus, and the PHY interface is connected to the cloud.

[0110] Exemplarily, the first processor core is connected to the PHY interface via the second bus.

[0111] It should be noted that the distribution network detection chip (control module) is provided with multiple interfaces, each of which can be set according to actual conditions and is not limited in the embodiments of the present application.

[0112] In one embodiment, the external signal acquisition interface includes an ADC sampling interface, an RS232 communication interface, an RS485 communication interface, and a telemetry signal interface.

[0113] Specifically, the external signal acquisition interface can be implemented using ADC sampling interface, RS232 communication interface, RS485 communication interface, telemetry signal interface, etc. according to actual sampling requirements, and can realize the collection of meteorological information such as temperature, humidity, wind speed, electrical signals such as voltage and current, and open and close signals (control signals for power devices in the distribution network).

[0114] It should be noted that the data involved in this application (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0115] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0116] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0117] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A data processing method for an intelligent terminal, characterized in that: The intelligent terminal comprises a control module and a storage module connected to each other, the control module comprises a first processor core and a second processor core connected to each other, the first processor core and the second processor core are both connected to the storage module, and the second processor core is used to connect to an external signal acquisition interface; The method is applied to the first processor core, and the method comprises: Performing data integrity determination on the monitoring data stored in the storage module, and when it is determined according to the data integrity determination result that abnormal data exists in the monitoring data, determining whether there are multiple pieces of the abnormal data within a preset time range; If not, the abnormal data is reconstructed using the cubic Hemite interpolation method, and the reconstructed abnormal data is backfilled to the corresponding position in the storage module; If so, a data re-collection instruction is output to the second processor core, wherein the data re-collection instruction is used to instruct the second processor core to re-collect the monitoring data through the external signal acquisition interface, and send the re-collected monitoring data to the storage module for storage.

2. The method according to claim 1, characterized in that The step of determining the data integrity of the monitoring data stored in the storage module includes: In the case where blank data exists in the monitoring data, determining the blank data as the abnormal data; and, Obtaining the numerical value corresponding to the remaining data, and determining the remaining data corresponding to the numerical value outside the preset numerical range as the abnormal data, wherein the remaining data is the data in the monitoring data except the blank data; and / or, According to the 3sigma principle, determining the abnormal data contained in the remaining data; and / or, The abnormal data contained in the remaining data is determined by using the quartile method.

3. The method according to claim 1, characterized in that: The reconstructing the abnormal data by using the cubic Hemite interpolation method includes: A cubic Hermite interpolation polynomial is established by using the preceding and succeeding data corresponding to the abnormal data, and the abnormal data is reconstructed based on the cubic Hermite interpolation polynomial.

4. A data processing method for an intelligent terminal, characterized in that: The intelligent terminal comprises a control module and a storage module connected to each other, the control module comprises a first processor core and a second processor core connected to each other, the first processor core and the second processor core are both connected to the storage module, and the second processor core is used to connect to an external signal acquisition interface; The method is applied to the second processor core, and the method comprises: In response to the data recollection instruction output by the first processor core, the monitoring data is recollected through the external signal collection interface, and the recollected monitoring data is sent to the storage module for storage.

5. The method according to claim 4, characterized in that The second processor core is also used to connect to the cloud; the method also includes: Periodically sampling the monitoring data through the external signal acquisition interface; Outputting the acquired monitoring data to the storage module for storage; The monitoring data is output to the cloud at preset time intervals.

6. An intelligent terminal, characterized in that: The intelligent terminal comprises a control module and a storage module connected to each other, the control module comprises a first processor core and a second processor core connected to each other, the first processor core and the second processor core are both connected to the storage module, and the second processor core is used to connect to an external signal acquisition interface; The first processor core is used to execute the steps of the method according to any one of claims 1 to 3; the second processor core is used to execute the steps of the method according to any one of claims 4 or 5.

7. The intelligent terminal according to claim 6, characterized in that: The storage module is a shared DDR.

8. The intelligent terminal according to claim 6, characterized in that: The control module also includes a first bus, and the first processor core and the second processor core are both connected to the storage module through the first bus.

9. The intelligent terminal according to claim 6, characterized in that: The control module also includes a second bus, and the second processor core is connected to the cloud via the second bus.

10. The intelligent terminal according to claim 6, characterized in that: The external signal acquisition interface includes an ADC sampling interface, an RS232 communication interface, an RS485 communication interface and a telemetry signal interface.

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