All-in-one device data delay processing method and apparatus, electronic device, and medium

Abstract

This invention discloses a method, apparatus, electronic device, and medium for processing data delays in energy-enabled devices. The method includes: filtering at least one first data point from raw data; wherein the raw data represents data obtained from different energy-enabled devices under different systems in an energy-enabled network; the data entry time of the first data point is within a first time range determined based on the current time; adding a first identifier to each first data point based on the difference between its entry time and the corresponding data generation time; grouping the first data points with the added first identifiers based on system codes and device codes; and determining the delay status of the first data points in each group within the first time range based on the first identifier corresponding to the first data points in each group. This method can analyze the causes of data delays in energy-enabled devices and effectively solve the data delay problem based on these causes.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and medium for processing data delays in general-purpose devices. Background Technology

[0002] The Universal Energy Network (UENN) is an energy Internet of Things (IoT) formed by an integrated physical energy network of primary and secondary energy sources built primarily on demand, along with a matching digital energy network. Guided by the concept of ubiquitous energy, the UENN interconnects energy facilities, utilizes digital technology to provide intelligent support to all participants in the energy ecosystem, offers value-added services to users, and enables information-guided orderly energy flow, thereby achieving advanced energy utilization.

[0003] The universal energy network includes universal energy gateways, which are devices that control the interactions between components (energy flow, matter flow, and information flow) in the universal energy flow and the flow path of each component. For information exchange, matter exchange, and energy exchange between energy systems, the universal energy gateway acts as a protocol converter to adapt to the needs of the target energy system. Simultaneously with protocol conversion, the universal energy gateway automatically selects and sets routes, choosing the optimal flow path for each component of the universal energy flow.

[0004] The integrated energy network also deploys multiple integrated energy devices, which are primarily responsible for the production, communication, control, collection, and transmission of energy data at each stage of energy generation, application, storage, transportation, and regeneration. These devices upload collected energy data, allowing the integrated energy network system to process it. This processing includes consumption statistics, meter reading, and generating corresponding control information based on the data processing results. The integrated energy gateway receives this control information from the upper-level system and implements appropriate control measures on-site, thereby achieving optimized control of the entire integrated energy network. However, some unavoidable factors can cause delays during data uploads by these devices. If these unavoidable factors are not prevented or data delays are not addressed promptly, the integrated energy network's control will not be able to adjust accordingly based on the data. Summary of the Invention

[0005] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, one object of this invention is to provide a method, apparatus, electronic device, and medium for processing data latency in ubiquitous devices, capable of timely handling of data latency.

[0006] The data delay processing method for universal energy devices according to an embodiment of the present invention includes the following steps:

[0007] At least one first data point is obtained by filtering the raw data; wherein the raw data represents data obtained from different energy-enabled devices under different systems in the energy-enabled network; the entry time of the first data point is within a first time range determined based on the current time;

[0008] A first identifier is added to each piece of first data based on the difference between the entry time of each piece of first data and the corresponding data generation time; the first identifier is used to mark whether the first data is entered into the warehouse on time.

[0009] Based on system coding and device coding, the first data with the added first identifier is grouped, and the delay of the first data in each group is determined according to the first identifier corresponding to the first data in each group within the first time range.

[0010] In the above scheme, the first data delay situation includes the proportion of delayed warehouse entry. The data delay handling method for general energy equipment also includes:

[0011] Based on the proportion of delayed data entry, the abnormal general-purpose devices corresponding to each group within the first time range are identified; whereby the proportion of delayed data entry represents the ratio of the amount of data delayed in entry to the total amount of data entered.

[0012] In the above scheme, based on the proportion of delayed warehousing, the abnormal general-purpose devices corresponding to each group within the first time range are determined, including:

[0013] If the proportion of delayed entry into the warehouse in a group is greater than the first set value, the general-purpose energy device corresponding to the group is determined to be an abnormal general-purpose energy device.

[0014] The above scheme also includes the following method for handling data delays in general-purpose energy devices:

[0015] Determine the second data delay for each group within the second time range, and based on the second data delay for each group within the second time range, determine the normal power supply devices in each group within the second time range; the second time range is determined based on the current event and is earlier than the first time range;

[0016] The abnormal power supply devices corresponding to each group in the first time range are compared with the normal power supply devices corresponding to each group in the second time range to determine the abnormal time of the abnormal power supply devices corresponding to each group.

[0017] In the above scheme, determining the second data delay for each group within the second time range includes:

[0018] The second data is obtained by filtering the original data; the entry time of the second data is within the second time range.

[0019] Based on the percentage of delayed inbound transactions of the second data in each group within a unit of time, the average percentage of delayed inbound transactions for each group within the second time range is determined; the percentage of delayed inbound transactions of the second data within a unit of time is determined based on the first identifier corresponding to the second data.

[0020] In the above scheme, based on the second data delay of each group within the second time range, the normal power-enabled devices in each group within the second time range are determined, including:

[0021] If the average percentage of delayed warehouse entry corresponding to the group within the second time range is less than the second set value, the general energy equipment corresponding to the group will be identified as normal general energy equipment.

[0022] The above scheme also includes the following method for handling data delays in general-purpose energy devices:

[0023] Based on the abnormal power supply devices corresponding to each group within the first time range, the number of abnormal power supply devices within the first time range is counted.

[0024] According to an embodiment of the present invention, a data delay processing apparatus for universal energy devices includes:

[0025] A filtering module is used to filter at least one first data point from the raw data; wherein the raw data represents data obtained from different energy-enabled devices under different systems in the energy-enabled network; and the entry time of the first data point is within a first time range determined based on the current time.

[0026] The identification module is used to add a first identifier to each piece of first data based on the difference between the entry time of each piece of first data and the corresponding data generation time; the first identifier is used to mark whether the first data is entered into the warehouse on time.

[0027] The determination module is used to group the first data with the added first identifier based on the system code and the device code, and determine the delay of the first data in each group within a first time range according to the first identifier corresponding to the first data in each group.

[0028] An electronic device according to an embodiment of the present invention includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described universal device data delay processing method.

[0029] According to an embodiment of the present invention, a computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for processing data delays in universal energy devices.

[0030] The aforementioned data delay processing method, apparatus, electronic device, and medium for general-purpose energy devices can determine the first data delay situation of each group within a first time range, analyze the cause of the data delay situation, and then effectively solve the data delay problem based on the cause of the data delay in general-purpose energy devices.

[0031] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating a method for processing data delays in a general-purpose energy device, as shown in one embodiment.

[0033] Figure 2 This is a flowchart illustrating the data delay processing method for a general-purpose energy device in yet another embodiment;

[0034] Figure 3 This is a flowchart illustrating the data delay processing method for a general-purpose energy device in yet another embodiment;

[0035] Figure 4 This is a schematic diagram of the data delay processing flow for a general-purpose energy device in one embodiment;

[0036] Figure 5 This is a structural block diagram of a data delay processing device for a general-purpose energy device in one embodiment. Detailed Implementation

[0037] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0038] The implementation details of the technical solutions of the embodiments of the present invention are described in detail below.

[0039] In one embodiment, such as Figure 1 As shown, a method for processing data delay in universal energy devices is provided, which may include the following steps:

[0040] Step S101: Filter out at least one first data from the original data.

[0041] The integrated energy network deploys multiple integrated energy devices, each with multiple measuring points. Energy data is collected from these measuring points across different integrated energy devices within different systems. This energy data primarily includes water, electricity, natural gas, compressed air, cooling, heating, and coal. In practical applications, the integrated energy network collects and manages data from different enterprises, requiring data differentiation. In this embodiment, data is differentiated by the system to which the integrated energy device belongs; data collected by integrated energy devices within the same system belongs to the same enterprise. Therefore, the data collection source can be distinguished using system codes (used to identify the enterprise to which the integrated energy device belongs) and device codes. In practical applications, various types of integrated energy devices exist. For example, integrated energy devices can be air compressors, used to collect operating parameters such as pressure and compression ratio during operation; integrated energy devices can also be boilers, used to collect boiler operating parameters, etc.

[0042] In this embodiment, raw data (i.e., energy data) is first obtained from different energy-enabled devices under different systems in the energy-enabled network. Then, at least one first data point that needs to be processed is selected from the raw data. In practical applications, raw data obtained from different energy-enabled devices under different systems in the energy-enabled network can be obtained at set time intervals. For example, raw data from different energy-enabled devices under different systems in the energy-enabled network can be obtained every minute.

[0043] A data warehouse is used to extract, clean, process, summarize, and organize previously scattered data. Hive is a tool for managing data warehouses and processing structured data. Hive's raw data layer (ODS, Operation Data Store) stores raw data, directly loading raw logs and data without processing it. Hive's detailed data layer (DWD, Data Warehouse Detail) cleans the data from the ODS layer (e.g., removing null values ​​and dirty data) and performs data anonymization, essentially performing light data summarization. Hive's data service layer (DWS, Data Warehouse Service) performs summary processing according to various business themes. Hive's application data layer (ADS, Application Data Store) provides data for various statistical reports. In practical applications, the functions built into Hive cannot fully meet business needs. Custom Hive user-defined functions (UDFs) can be used to implement different business requirements with different processing logics.

[0044] In this embodiment, the data entry time refers to the time when the data enters the data warehouse. After the data enters the data warehouse, the Pan-Energy Network system will process the different data in the data warehouse. At least one first data point is selected from the original data. The data entry time of the first data point is within a first time range determined based on the current time. The current time can be the current system time, and the first time range can be set according to actual needs. For example, assuming the current time is 15:00 on February 27, 2023, the first time range is set to one hour before the current time. That is, the data with the data entry time of 14:00 on February 27, 2023 is selected from the original data. The data with the data entry time of 14:00 on February 27, 2023 is the first data point.

[0045] In practical applications, raw data can be grouped hourly based on its inbound time. First, the inbound time is converted to the format: year-month-date-hour. Then, raw data with the same inbound time are grouped together. This allows you to retrieve the first data within a specific timeframe from different data groups based on the current time. Alternatively, you can use a query engine to search for the inbound time of the raw data in Hive, further filtering to obtain the first data.

[0046] Step S102: Add a first identifier to each first data based on the difference between the warehouse entry time of each first data and the corresponding data generation time.

[0047] Here, the difference between the data entry time and the data generation time is used to determine whether there is a delay in the first data. The data generation time refers to the time it takes to read data from the energy distribution equipment. As can be understood, the energy distribution network equipment is responsible for collecting different energy data, uploading the collected energy data, and then successfully uploading the energy data into the data warehouse. The energy distribution network system then processes the energy data in the data warehouse. Therefore, the data delay of the energy distribution equipment is essentially the delay in uploading the energy data by the energy distribution equipment.

[0048] In this embodiment, three scenarios are defined where there is no data delay from the universal energy storage device:

[0049] Scenario 1: Data enters the data warehouse 1 hour earlier, which means the data generation time - the entry time = 1 hour;

[0050] Scenario 2: The data enters the data warehouse exactly on time, that is, the data generation time - the entry time = 0 hours;

[0051] Scenario 3: Data enters the data warehouse with a 1-hour delay, which means the entry time minus the data generation time equals 1 hour.

[0052] The first identifier can be represented by two fields. For example, by configuring a "normal" field and an "abnormal" field for the first data, and combining this with the three scenarios listed above, we can obtain:

[0053] If the difference between the entry time of the first data and the corresponding data generation time meets the three conditions listed above, then the normal field of the first data is assigned a value of 1 and the abnormal field is assigned a value of 0, indicating that there is no data delay in the first data.

[0054] If the difference between the time the first data is entered into the warehouse and the time the corresponding data is generated does not meet the three conditions listed above, then the normal field of the first data is assigned a value of 0 and the abnormal field is assigned a value of 1, indicating that there is a data delay in the first data.

[0055] Step S103: Group the first data with the added first identifier based on the system code and device code, and determine the delay of the first data in each group within the first time range according to the first identifier corresponding to the first data in each group.

[0056] Understandably, the first data is collected from different energy-enabled devices under different systems. The first data, marked with a first identifier, is categorized based on system and device codes. In practical applications, the first data carries both a system code and a device code. The system code points to the company where the energy-enabled device that collected the first data is located, and the device code points to the energy-enabled device that collected the first data. This allows all the first data to be divided into different groups. Within the same group, the system code and device code corresponding to the first data are identical, meaning that the first data in the same group was collected by the same energy-enabled device under the same system.

[0057] After grouping the first data with added first identifiers, the latency of the first data in each group can be statistically analyzed based on the first identifier corresponding to the first data in each group. For example, by using the first identifier corresponding to the first data in a group, the amount of data that is delayed and the amount of data that is delivered on time can be determined within the group within the first time range. After determining the latency of the first data in each group, the causes of data latency by the energy-saving devices can be analyzed, thereby effectively solving the data latency problem.

[0058] In practical applications, after determining the first data delay situation, a report as shown in Table 1 can be output. Table 1 is used to record the data entry time and data delay situation, and also includes system code, device code, on-time entry quantity, delayed entry quantity, delayed entry percentage, and entry time. Among them, system b-device B1 is regarded as one group, and system b-device B2 is regarded as another group.

[0059] Table 1

[0060]

[0061] In practical applications, there are several reasons that cause data delays in power distribution equipment, the main ones being:

[0062] The first type is data latency between systems. Different systems need to synchronize data, and this synchronization process takes time, ranging from seconds to hours. If further operations are performed on that data during this period, inconsistencies may occur, leading to data latency in the power distribution equipment.

[0063] The second type is transaction incompleteness caused by synchronization timeout. This means there is a possibility that a transaction might be interrupted halfway through execution, but cannot be rolled back.

[0064] The third type is that abnormalities or breaks in the power supply equipment can cause data delays.

[0065] The fourth scenario is that a code error suddenly occurs in one of the programs in the entire synchronous data flow, causing the program to fail to run normally and resulting in data delays in the general-purpose energy devices.

[0066] The fifth issue is that the underlying data was not uploaded during the data processing, resulting in discontinuous data from the energy storage devices.

[0067] The sixth possibility is that there are abnormalities in the network or program, such as an abnormality in the Kafka cluster (a distributed stream processing system used for storing and transmitting data), which causes data to accumulate on the ubiquitous energy network devices and slows down the upload speed.

[0068] The seventh method is to resume data transmission after the IoT card expires or the network malfunctions.

[0069] Data delays caused by ubiquitous network equipment can lead to direct economic and informational losses, as well as various indirect losses. By analyzing the initial data delay of each packet, the causes of data delays can be identified, and corresponding solutions can be proposed. Data preprocessing can also be performed to avoid data loss.

[0070] The general-purpose energy equipment can be applied to the chemical industry. The following example uses the compressed air system for bio-fermentation in the chemical industry as an application scenario to illustrate this embodiment.

[0071] The integrated energy network is equipped with various integrated energy devices that monitor parameters such as pressure, temperature, flow rate, and relative humidity at the gas consumption points in the fermentation workshop. These collected parameters are then fed back to the upper-level system of the integrated energy network. The upper-level system uses these parameters to quickly grasp the operating status, energy consumption and efficiency levels, and promptly locate anomalies. It can accurately reflect and analyze demand and supply data, optimize the energy efficiency of air compressors and the economic matching of the air compression system, and reduce compressed air consumption per unit. However, if the integrated energy devices experience data delays, they cannot promptly feed real-time data back to the upper-level system. This results in the upper-level system being unable to accurately analyze the operating status of the fermentation workshop, leading to the issuance of incorrect control commands and affecting the normal operation of the fermentation workshop.

[0072] Based on this, by analyzing the data latency of each energy-enabled device, the problem of energy-enabled data latency can be addressed in a targeted manner, which can prevent and avoid the occurrence of data latency, thereby facilitating the processing of data collected by the energy-enabled devices by the energy-enabled network.

[0073] In one embodiment, the first data delay scenario includes the percentage of delayed warehouse entry, and the method for handling data delay in general-purpose energy equipment further includes:

[0074] Based on the proportion of delayed warehouse entry, identify the abnormal general-purpose energy devices corresponding to each group within the first time frame.

[0075] Here, the first data delay situation includes the delayed entry ratio, which is calculated based on the ratio between the amount of delayed data in a group and the total amount of data in that group. The delayed entry ratio determines the distribution of delayed data in each group and identifies the abnormal general-purpose devices corresponding to each group. Specifically, we can count the amount of data with "normal = 1" in a group (i.e., the amount of data that entered on time), and then count the amount of data with "abnormal = 1" in the same group (i.e., the amount of data that entered on time). The delayed entry ratio is calculated as: delayed entry data volume / (on-time entry data volume + delayed entry data volume). Here, the sum of on-time and delayed entry data is the total amount of data in that group.

[0076] It should be noted that "abnormal power supply devices" can be understood as power supply devices with relatively severe data latency. Since abnormal power supply devices are determined based on the percentage of delayed inbound shipments within the first time frame, the identified abnormal power supply devices are those that exhibit data latency within the first time frame.

[0077] In this embodiment, by using the percentage of delayed inbound shipments, the abnormal general-purpose energy devices corresponding to the group can be identified. By analyzing the reasons for the abnormality of the general-purpose energy devices, the data delay problem can be solved in a targeted manner, and the occurrence of data delays due to the same reasons can also be prevented.

[0078] In one embodiment, after identifying the abnormal power supply devices corresponding to each group within a first time range, the total number of abnormal power supply devices within that first time range is counted. This allows for a comprehensive understanding of the causes of the power supply device anomalies, enabling analysis and targeted solutions to data latency issues. In practical applications, Table 2 can be output based on the count of abnormal power supply devices. Table 2 includes the number of abnormal power supply devices, the measurement point data entry time (format: year-month-date-hour), and the measurement point data entry time (format: year-month), thus determining the number of abnormal power supply devices within a month.

[0079] Table 2

[0080]

[0081] In one embodiment, if the proportion of delayed inbound data in a group exceeds a first preset value, message backlog can be considered to exist, thereby identifying the device corresponding to that group as an abnormal device. In this embodiment, the first preset value can be set to 0.5, meaning that if the proportion of delayed inbound data in a group exceeds 0.5, the device in that group is considered an abnormal device.

[0082] In one embodiment, such as Figure 2 As shown, the data delay processing method for general energy devices also includes:

[0083] Step S201: Determine the second data delay status of each group within the second time range, and determine the normal power supply devices in each group within the second time range based on the second data delay status of each group within the second time range.

[0084] Here, the second time range is determined based on the current time and is earlier than the first time range. In this embodiment, the second time range can be set to the day before the current time. For example, if the current time is 15:00 on February 27, 2023, the corresponding first time range is 14:00 on February 27, 2023, and the corresponding second time range is February 26, 2023.

[0085] Determine the normal power supply devices in each group within the second time range, wherein the normal power supply devices are determined based on the second data delay condition of each group within the second time range.

[0086] In one embodiment, such as Figure 3As shown, the second data delay for each group within the second time range is determined, including:

[0087] Step S301: Filter the original data to obtain the second data.

[0088] Here, the second data refers to the data of the warehouse entry time within the second time range. In practical applications, the raw data collected from different energy-saving devices under different systems will be stored and recorded. Therefore, the second data can be obtained by filtering from the raw data, and the second delay of each group within the second time range can be determined based on the second data.

[0089] Step S302: Based on the percentage of delayed inbound data in each group within a unit of time, determine the average percentage of delayed inbound data for each group within the second time range.

[0090] Here, after obtaining the second data, the second data is grouped according to the system code and device code carried by the second data, so as to obtain the second data under each group.

[0091] In one approach, for the second data within a group, the percentage of delayed arrivals within a unit of time is calculated based on the arrival time of the second data and the corresponding identifier of the second data within that unit of time. This identifier is used to indicate whether the second data was arrived on time. Then, based on the percentage of delayed arrivals within each unit of time, the average percentage of delayed arrivals for the group within the second time range is calculated. It should be noted that the unit of time is essentially the first time range. Assuming the first time range is set to the hour preceding the current time, that is, one hour. For example, if the second time range is February 26, 2023, firstly, the percentage of delayed arrivals for a group at 0:00 on February 26, 2023 (x1), the percentage of delayed arrivals at 1:00 on February 26, 2023 (x2), and so on (24 hours in total). Then, based on the percentage of delayed arrivals for each hour, the average percentage of delayed arrivals for the group on February 26, 2023 is calculated, which is the average percentage of delayed arrivals.

[0092] In another approach, assuming the second time range is February 26, 2023, the data within the first time range will also be processed using steps S101 to S103 on that day to obtain the data latency information (mainly the data latency percentage) for each group within the first time range. Based on this, it can be understood that the second data is composed of data from multiple first time ranges, where the first time range is a unit of time. For example, assuming the first time range is the hour before the current time, i.e., February 26, 2023, the data latency percentage for each group in each hour can be obtained. In practical applications, the data latency percentage within the first time range will be stored on February 26, 2023. Based on this, by obtaining the latency percentage of each group within a unit of time on February 26, 2023, the average latency percentage of each group on February 26, 2023 can be calculated. Specifically, the average latency percentage... Where x1 represents the percentage of delayed entry in the first unit of time within the second time range, x2 represents the percentage of delayed entry in the second unit of time within the second time range, and so on.

[0093] In practical applications, since it is necessary to filter the second data within the second time range from the original data, after step S103, the entry time of the first data for which the first data delay statistics have been completed is formatted, and the entry time of the first data is converted from "year-month-date-hour" to "year-month-date". The converted entry time "year-month-date" can also be recorded in Table 1.

[0094] In one embodiment, if the average percentage of delayed inbound shipments corresponding to groups within a second time range is less than a second preset value, the devices corresponding to those groups are identified as normal general-purpose power supply devices. In this embodiment, the second preset value can be set to 0.1, meaning that devices with an average percentage of delayed inbound shipments less than 0.1 are considered normal general-purpose power supply devices within the second time range.

[0095] Step S202: Compare the abnormal power supply devices corresponding to each group in the first time range with the normal power supply devices corresponding to each group in the second time range to determine the abnormal time of the abnormal power supply devices corresponding to each group.

[0096] Here, for the same group of interconnected devices, assuming they are normal interconnected devices in the second time range and abnormal interconnected devices in the first time range, it can be determined that the interconnected device is abnormal in the first time range, that is, the abnormal time of the abnormal interconnected device in the first time range. For example, for interconnected device A, on February 26, 2023 (that is, the second time range), device A is a normal interconnected device with basically no data delay. However, at 15:00 on February 27, 2023 (that is, the first time range), interconnected device A is determined to be an abnormal interconnected device. It can be determined that interconnected device A is abnormal at 15:00 on February 27, 2023. Furthermore, based on the abnormal time of the abnormal interconnected device, the cause of the device's abnormality can be further analyzed, which is helpful in solving the data delay problem.

[0097] In the above embodiments, at least one first data is obtained by filtering from the original data. A first identifier is added to each first data according to the difference between the entry time of each first data and the corresponding data generation time. The first data with the first identifier are grouped based on the system code and the device code. Based on the first identifier corresponding to the first data in each group, the delay of the first data in each group is determined within a first time range. Thus, the cause of data delay in the energy-saving device can be analyzed based on the first data delay, and the data delay problem can be solved in a targeted manner to avoid the recurrence of data delay due to the same reason. This enables real-time adjustment of the optimized control of the energy-saving network.

[0098] The present invention also provides an application embodiment, such as Figure 4 As shown, Figure 4 A schematic diagram of a data delay processing flow for a general-purpose energy device is shown.

[0099] Step 1: Extract the first data from the raw data. The entry time of the first data is the hour preceding the current time. The raw data is collected from different energy-enabled devices under different systems in the energy-enabled network.

[0100] Step 2: Assign values ​​to the `normal` and `abnormal` fields of the first data based on the difference between the data entry time and the corresponding data generation time. Specifically, if data generation time - entry time = 1 hour (data entered 1 hour early), data generation time - entry time = 0 hours (data entered exactly on time), and entry time - data generation time = 1 hour (data entered 1 hour late), the `normal` field of the first data will be 1, and the `abnormal` field will be 0. Otherwise, the `normal` field will be 0, and the `abnormal` field will be 1.

[0101] Step 3: Group the first data according to the system code and device code of the first data.

[0102] Step 4: After completing the grouping, perform statistics on the first data in each group whose normal field is 1, and also perform statistics on the first data in each group whose abnormal field is 1.

[0103] Step 5: Determine the percentage of delayed inbound shipments for each group based on the first data in each group where the normal field is 1 and the first data in each group where the abnormal field is 1.

[0104] Step 6: Based on the delayed inbound percentage of each group, determine the abnormal devices for each group in the previous hour at the current time. Specifically, if the delayed inbound percentage of a group is greater than 0.5, the corresponding general-purpose energy device for that group is identified as an abnormal device.

[0105] Step 7: Extract the second data from the original data, where the entry time of the second data is the day before the current time.

[0106] Step 8: Determine the average delayed entry percentage for each group based on the delayed entry percentage for each group within a unit of time. The delayed entry percentage for each group within a unit of time refers to the delayed entry percentage for each hour of the previous day, which is obtained by processing the data in steps 1 to 5 on the day before the current time.

[0107] Step 9: Based on the average percentage of delayed inbound shipments for each group, determine the normal general-purpose power supply equipment corresponding to each group. Specifically, if the average percentage of delayed inbound shipments for a group is less than 0.1, the general-purpose power supply equipment for that group is determined to be normal general-purpose power supply equipment.

[0108] Step 10: Determine the abnormal time of the abnormal power supply device by comparing the normal power supply device in the previous day and the abnormal power supply device in the previous hour.

[0109] Step 11: Count the number of abnormal power devices in the previous hour.

[0110] In one embodiment, a data delay processing device for general-purpose devices is provided, with reference to... Figure 5 As shown, the data delay processing device 500 for general-purpose energy devices may include: a filtering module 501, an identification module 502, and a determination module 503.

[0111] The filtering module 501 is used to filter at least one first data from the original data; wherein the original data represents data obtained from different energy-using devices under different systems in the energy-using network; the entry time of the first data is within a first time range determined based on the current time; the identification module 502 is used to add a first identifier to each first data according to the difference between the entry time of each first data and the corresponding data generation time; the first identifier is used to mark whether the first data is entered on time; the determination module 503 is used to group the first data with the added first identifier based on the system code and the device code, and determine the delay of the first data in each group within the first time range according to the first identifier corresponding to the first data in each group.

[0112] Furthermore, the first data delay situation includes the delayed data entry ratio. The determination module 503 is also used to determine the abnormal general-purpose devices corresponding to each group within the first time range based on the delayed data entry ratio; wherein, the delayed data entry ratio represents the ratio of the amount of delayed data to the total amount of data in the warehouse.

[0113] In one embodiment, the determining module 503 is specifically used to determine that the device corresponding to the group is an abnormal general-purpose device when the proportion of delayed entry into the warehouse of the group is greater than a first set value.

[0114] Furthermore, the determining module 503 is also used to determine the second data delay status of each group within the second time range, and to determine the normal power supply devices in each group within the second time range based on the second data delay status of each group within the second time range; the second time range is determined based on the current time and is earlier than the first time range; and to compare the abnormal power supply devices corresponding to each group within the first time range with the normal power supply devices corresponding to each group within the second time range to determine the abnormal time of the abnormal power supply devices corresponding to each group.

[0115] In one embodiment, the determining module 503 is specifically used to: filter out second data from the original data; the warehouse entry time of the second data is within a second time range; determine the average value of the delayed warehouse entry ratio corresponding to each group within the second time range based on the delayed warehouse entry ratio of the second data in each group per unit time; the delayed warehouse entry ratio of the second data per unit time is determined based on the first identifier corresponding to the second data.

[0116] In one embodiment, the determining module 503 is specifically used to determine the general-purpose energy device corresponding to the group as a normal general-purpose energy device when the average value of the delayed entry ratio of the group within the second time range is less than a second set value.

[0117] Furthermore, the determining module 503 is also used to count the number of abnormal power devices in the first time range based on the abnormal power devices corresponding to each group in the first time range.

[0118] In one embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a universal device data delay processing method.

[0119] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for processing data delays in a universal device.

[0120] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0121] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0123] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0124] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for processing data delay of a universal device, characterized in that, The method comprises: screening at least one first data from original data; wherein the original data represents data obtained from different all-ability devices under different systems in the all-ability network; the warehousing time of the first data is within a first time range determined based on the current time; adding a first identifier to each first data according to the difference between the warehousing time of each first data and the corresponding data generation time; the first identifier is used to mark whether the first data is timely warehoused; the data generation time refers to the time of reading data from the all-ability device; grouping the first data with the first identifier based on system coding and device coding, and determining the first data delay condition of each group within the first time range according to the first identifier of the first data in each group.

2. The pan-energetic device data delay processing method of claim 1, wherein, The first data delay condition includes a delay warehousing proportion, and the method further comprises: determining the abnormal all-ability device corresponding to each group within the first time range according to the delay warehousing proportion; wherein the delay warehousing proportion represents the ratio of the amount of delayed warehousing data to the total amount of warehoused data.

3. The pan-energetic device data delay processing method of claim 2, wherein, Determining the abnormal all-ability device corresponding to each group within the first time range according to the delay warehousing proportion comprises: determining the device corresponding to the group as an abnormal all-ability device when the delay warehousing proportion of the group is greater than a first set value.

4. The pan-energetic device data delay processing method of claim 2 or 3, wherein, The method further comprises: determining a second data delay condition of each group within a second time range, and determining normal all-ability devices in each group within the second time range according to the second data delay condition of each group within the second time range; the second time range is determined based on the current time and is earlier than the first time range; comparing the abnormal all-ability device corresponding to each group within the first time range with the normal all-ability device corresponding to each group within the second time range to determine the abnormal time of the abnormal all-ability device corresponding to each group.

5. The pan-energetic device data delay processing method of claim 4, wherein, The determination of the second data delay condition of each group within the second time range comprises: screening second data from the original data; the warehousing time of the second data is within the second time range; determining the average value of the delay warehousing proportion corresponding to each group within the second time range according to the delay warehousing proportion per unit time of the second data in each group; the delay warehousing proportion per unit time of the second data is determined according to the first identifier corresponding to the second data.

6. The pan-energetic device data delay processing method of claim 5, wherein, The determination of the normal all-ability device in each group within the second time range according to the second data delay condition of each group within the second time range comprises: determining the device corresponding to the group as a normal all-ability device when the average value of the delay warehousing proportion corresponding to the group within the second time range is less than a second set value.

7. The universal device data delay processing method of claim 2, wherein, The method further comprises: counting the number of abnormal all-ability devices within the first time range according to the abnormal all-ability device corresponding to each group within the first time range.

8. A pan-energetic device data delay processing apparatus, characterized by, The method comprises the following steps: a screening module is configured to screen at least one first data from original data; wherein the original data represents data obtained from different all-ability devices under different systems in the all-ability network; and the storage time of the first data is within a first time range determined based on the current time; an identification module is configured to add a first identification to each first data according to the difference between the storage time of each first data and the corresponding data generation time; wherein the first identification is used to mark whether the first data is timely stored; and the data generation time refers to the time when the data is read from the all-ability device; a determination module is configured to group the first data with the first identification based on system coding and device coding, and determine the first data delay condition of each group within the first time range according to the first identification of the first data in each group. 9.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to realize the steps of the all-ability device data delay processing method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the all-ability device data delay processing method in any one of claims 1 to 7.

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