Data processing method, device and electronic device

Abstract

The present application discloses a method, device and electronic device for data processing. The method comprises: determining whether there is an abnormality in the measurement value; if the measurement value is abnormal, splitting the first time interval corresponding to the measurement value to obtain multiple sub-intervals; processing the measurement values ​​in the multiple sub-intervals to obtain the target measurement value. The present application solves the technical problem that the time interval of the abnormal measurement value automatically detected and eliminated is large, resulting in result deviation and increasing the time spent on handling the abnormality.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular, to a method, device and electronic device for data processing. Background Art

[0002] The traditional automatic metering mode requires human participation. The metering personnel confirm the automatic metering results in a certain period of time. If the instrument needs to be operated, such as changing the meter, resetting, or abnormal data is found, an abnormal record is added, and the metering value of the period is recalculated and the result is uploaded. The discovery and handling of the above-mentioned abnormal measurement depends on personal experience, which has high requirements for operators, and different people are prone to different judgments, resulting in inconsistent measurement results.

[0003] Traditional automatic metering modes also provide detection of some abnormal types and can correct significant errors, but the long span of metering cycles makes the detection process slow. When faced with non-significant abnormalities, or complex situations where two different metering types, cumulative and instantaneous, coexist, a single abnormality detection and processing method is difficult to be effective at the same time.

[0004] In summary, although the existing metering management system provides automatic metering based on real-time database and can automatically obtain metering results periodically, it still has the following problems: the time interval of abnormal data automatically detected and eliminated is large, which often contains normal data that needs to be retained, causing result deviation and increasing the time spent on handling abnormalities.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present application provide a method, device and electronic device for data processing to at least solve the technical problem that the time interval of abnormal measurement values ​​automatically detected and eliminated is large, causing result deviation and increasing the time spent on handling the abnormality.

[0007] According to one aspect of an embodiment of the present application, a data processing method is provided, including: determining whether a measurement value is abnormal; if the measurement value is abnormal, splitting a first time interval corresponding to the measurement value to obtain multiple sub-intervals; and processing the measurement values ​​in the multiple sub-intervals to obtain a target measurement value.

[0008] Optionally, before determining whether there is an abnormality in the measurement value, the method further includes: correcting the data through a correction model to obtain the measurement value, and the correction model is determined at least based on the measurement coefficient and the constant deviation.

[0009] Optionally, after the data is corrected by the correction model to obtain the measurement value, the method also includes: judging the relationship between different types of measurement values ​​and the corresponding historical extreme values ​​and design values, wherein the historical extreme values ​​include historical maximum values ​​and historical minimum values, and the design value is determined based on the rated hourly production or consumption and the collection cycle of the equipment to which the meter belongs; when different types of measurement values ​​are greater than the corresponding historical maximum values ​​and greater than the upper deviation corresponding to the design value, outputting an abnormal warning; or when different types of measurement values ​​are less than the corresponding historical minimum values ​​and less than the lower deviation corresponding to the design value, outputting an abnormal warning.

[0010] Optionally, judging whether the measurement value is abnormal includes: the type of the measurement value includes at least a cumulative measurement value and an instantaneous measurement value, and judging whether different types of measurement values ​​are abnormal according to identifiers corresponding to different types of measurement values.

[0011] Optionally, based on the identifiers corresponding to different types of measurement values, it is judged whether different types of measurement values ​​have abnormalities, including: the identifier corresponding to the cumulative measurement value is a first identifier, when the first identifier is 0 and the cumulative measurement value changes suddenly, it is determined that the cumulative measurement value has an abnormality, wherein the first identifier is 0 and indicates that the instrument value points to the post-reset value when it has not reached the pre-reset value; the identifier corresponding to the instantaneous measurement value is a second identifier, when the second identifier is 0 and the instantaneous measurement value changes suddenly, it is determined that the instantaneous measurement value has an abnormality, wherein the second identifier is 0 and indicates that the instrument reaches a zero value or a negative value instantaneously due to external interference.

[0012] Optionally, when there is an abnormality in the measurement value, the first time interval corresponding to the measurement value is split to obtain multiple sub-intervals, including: when the first identifier is 0, the first time interval corresponding to the measurement value is evenly split into multiple second time intervals; determine the difference in the measurement values ​​of adjacent time intervals in all second time intervals; when the difference in the second time interval is negative, evenly split the second time interval corresponding to the negative difference into a third time interval, wherein the time period of the third time interval is smaller than the time period of the second time interval; determine the difference in the measurement values ​​of adjacent time intervals in all third time intervals; when the difference in the third time interval is negative, recursively split the third time interval corresponding to the negative difference until the split time interval is smaller than the inverse of the collection frequency of the data corresponding to the measurement value, to obtain multiple sub-intervals.

[0013] Optionally, the measurement values ​​in multiple sub-intervals are processed to obtain a target measurement value, including: determining a cumulative measurement value based on the measurement values ​​corresponding to the time intervals with negative differences in the multiple sub-intervals, the measurement values ​​corresponding to other time intervals in the multiple sub-intervals except the time intervals with negative differences, the measurement coefficient, and the constant deviation, and the cumulative measurement value is the target measurement value.

[0014] Optionally, the measurement values ​​in multiple sub-intervals are processed to obtain target measurement values, and the method also includes: determining target values ​​of instantaneous measurement values ​​in multiple sub-intervals, wherein the target value is the maximum value of each sub-interval in the multiple sub-intervals; comparing the target value with the end-of-period value in each sub-interval, wherein the end-of-period value is the measurement value corresponding to the end time of each sub-interval; and obtaining the instantaneous measurement value after processing the maximum value between the target value and the end-of-period value by a constant deviation, wherein the instantaneous measurement value is the target measurement value.

[0015] According to another aspect of an embodiment of the present application, a data processing device is also provided, including: a judgment module, used to judge whether there is an abnormality in the measurement value; a splitting module, used to split the first time interval corresponding to the measurement value to obtain multiple sub-intervals when the measurement value is abnormal; and a processing module, used to process the measurement values ​​in the multiple sub-intervals to obtain a target measurement value.

[0016] According to another aspect of the embodiments of the present application, an electronic device is also provided, including: a memory for storing program instructions; a processor, connected to the memory, for executing program instructions that implement the following functions: determining whether a measurement value is abnormal; if the measurement value is abnormal, splitting a first time interval corresponding to the measurement value to obtain multiple sub-intervals; and processing the measurement values ​​in the multiple sub-intervals to obtain a target measurement value.

[0017] In an embodiment of the present application, when it is determined that an abnormality exists in the measurement value, the first time interval corresponding to the measurement value is split to obtain multiple sub-intervals, so that the measurement values ​​of the multiple sub-intervals are processed, and the measurement value corresponding to the measurement value is obtained, thereby achieving the purpose of refining the time interval and facilitating the search for abnormal measurement values, thereby achieving the technical effect of obtaining the measurement values ​​of the split time interval while ensuring the calculation efficiency, and aggregating the measurement values ​​to replace the measurement values ​​in the first interval containing the abnormal measurement value, thereby solving the technical problem that the time interval of the abnormal measurement value automatically detected and eliminated is large, causing result deviation, and increasing the time spent on processing the abnormality. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a data processing method according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a data processing method according to an embodiment of the present application;

[0021] Figure 3a is a flow chart for determining the relationship between a measurement value, a historical extreme value, and a design value according to an embodiment of the present application;

[0022] Figure 3b is a flow chart of a filtering model according to an embodiment of the present application;

[0023] Figure 4 is a flow chart for determining whether different types of measurement values ​​are abnormal according to an embodiment of the present application;

[0024] Figure 5 is a flow chart of splitting a first time interval into multiple sub-intervals according to an embodiment of the present application;

[0025] Figure 6 It is a flow chart of obtaining a measurement value corresponding to instantaneous quantity data according to an embodiment of the present application;

[0026] Figure 7 It is a structural diagram of a data processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0030] Metering point: The basic unit of metering management, including various information of metering instruments: instrument name, data type (cumulative quantity, instantaneous quantity), metering coefficient, data source (manual metering, automatic metering), affiliated facilities, automatic reset mark, negative allowance mark, upper and lower limits, mapping formula, accuracy, design value, design deviation, load, unit, etc.

[0031] Meter name: The unique identifier of the metering point, used to associate the meter with the metering point.

[0032] Metric Factor: The weighting factor applied when calculating the metric value.

[0033] Automatic reset flag: It indicates whether the instrument will automatically reset when reaching a certain limit value. It is necessary to configure the value before reset (usually the instrument range or slightly less than the range) and the value after reset.

[0034] Whether negative value is allowed: Indicates whether the instrument allows negative instantaneous value and negative differential value to appear.

[0035] Upper and lower limits: Theoretically, the maximum and minimum values ​​that will occur. The extreme values ​​generated during the actual use of the instrument are within the upper and lower limits.

[0036] Data of real-time database: The instrument value collected by the real-time database is represented by {MP: instrument X}t, which represents the instrument value of instrument X at time t.

[0037] Mapping formula: Map the data in the real-time database to measurement values.

[0038] Precision: The number of decimal places retained in a measurement value.

[0039] Design value: calculated based on the rated hourly production (or consumption) of the facility to which the meter belongs × time period.

[0040] Design deviation: The rated deviation range of the design value.

[0041] Load: The actual resource usage ratio of the facility to which the meter belongs.

[0042] Instrument zeroing: divided into reset zeroing and abnormal zeroing. Reset zeroing is a zeroing operation of the instrument value when the instrument reaches the range or is replaced routinely. The characteristics are rapid zeroing and direct zeroing. After the reset is completed, the measurement is re-measured according to the cumulative law before zeroing; abnormal zeroing is due to the violent fluctuation of the instrument caused by external interference, causing the instrument to approach zero value instantaneously. The characteristics are repeated zeroing phenomenon, obvious fluctuations, and instantaneous high value after zeroing instead of cumulative increase.

[0043] Data spikes: Due to electromagnetic interference, the instrument will have spikes, and the data will increase rapidly in the direction of cumulative measurement values, and after reaching a spike value, it will quickly fall back to the normal cumulative trend.

[0044] Random anomalies: Instrument measurement relies on data collection from real-time databases, which are based on different data protocols. Currently, the most popular industrial data collection protocol is OPC (OLE for Process Control, an industrial standard). Data collection has various random anomalies, such as underlying communication errors, operation timeouts, illegal data, etc.

[0045] The data processing method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or electronic device) for implementing a data processing method is shown. Figure 1 As shown, the computer terminal 10 (or electronic device 10) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0046] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or electronic device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0047] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned data processing method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0048] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0049] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0050] It should be noted that, in some optional embodiments, the above Figure 1 The computer device (or electronic device) shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above-described computer device (or electronic device).

[0051] The measuring instruments of enterprise facilities are of great significance for accurate measurement and analytical calculation in the statistical dimension. They are a prerequisite for ensuring the accuracy of report data and are also the basic basis for subsequent balancing operations.

[0052] Metering methods are mainly divided into manual metering and automatic metering. With the promotion and application of real-time databases, automatic metering has been widely used. It has the advantages of fast, accurate, and time-effective. In addition, by using MES (manufacturing execution system) products, it can also connect meter reading, metering statistics, material balance, and report management to form an integrated business process.

[0053] Manual metering relies on the long-term experience accumulated by its operators to quickly identify and eliminate abnormal data, but automatic metering is difficult to automatically handle various types of abnormalities. It still needs to manually determine and add instrument abnormality records to ensure the accuracy of the metering results. This is one of the reasons why many companies find it difficult to achieve full automation of metering management. Therefore, continuous production companies need an abnormal data detection and processing method to replace manual judgment and processing of abnormal metering data, ensure metering accuracy, reduce the time spent on detection and processing, and make the automatic metering process faster and more accurate.

[0054] Under the above operating environment, the embodiment of the present application provides the following Figure 2 It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0055] This application is based on the data continuity of the metering instrument, analyzes various types of instrument operations and anomalies that may occur during the metering management process, and provides abnormal data detection and processing models for the two types of measurement: cumulative and instantaneous quantities. At present, many companies rely on manual judgment for automatic metering error correction, and even directly use abnormal values ​​as the final result. This application can quickly and accurately identify the time interval where the anomaly occurs and eliminate it, and then recalculate the measurement value to achieve the purpose of correcting the erroneous results, greatly improving the efficiency and credibility of automatic metering.

[0056] Figure 2 is a flow chart of a data processing method according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0057] Step S202, determining whether there is any abnormality in the measurement value;

[0058] Step S204: when the measurement value is abnormal, split the first time interval corresponding to the measurement value to obtain multiple sub-intervals;

[0059] Step S206: Process the measurement values ​​in the multiple sub-intervals to obtain the target measurement value.

[0060] In step S202 of the above data processing method, before determining whether the measurement value is abnormal, the method further includes: correcting the data by using a correction model to obtain the measurement value, and the correction model is determined at least based on the measurement coefficient and the constant deviation.

[0061] In the process of automatic metering, the real-time database is responsible for collecting and recording instrument values, and its stable and accurate operation is the basis of metering management. However, the data provided by the real-time data (RTDB) does not take into account external interference factors such as time period deviation, measurement unit conversion, and instrument fixed error, and cannot be directly used as the measurement value (MPV). Therefore, according to the configuration of the real-time database and combined with the on-site working conditions, a correction model is formulated, including: mapping formula f, measurement coefficient a, constant deviation C, and the following correction model is obtained:

[0062]

[0063] In the formula of the above correction model, the formula in the first row represents the calculation formula corresponding to the cumulative measurement value, and the formula in the second row represents the calculation formula corresponding to the instantaneous measurement value. k is the kth interval in the set of mapping formulas under time intervals, st is the start time of the kth interval, et is the end time of the kth interval, {MP} st is the data stored in the real-time database corresponding to the start time, {MP} et The data stored in the real-time database corresponding to the end time is the instrument value.

[0064] In the above steps, the data is corrected by the correction model to obtain the measured value, such as Figure 3a The method further specifically includes the following steps:

[0065] Step S302, determining the relationship between different types of metering values ​​and corresponding historical extreme values ​​and design values, wherein the historical extreme values ​​include historical maximum values ​​and historical minimum values, and the design value is determined based on the rated hourly production or consumption of the equipment to which the meter belongs and the collection cycle;

[0066] Step S304: when different types of measurement values ​​are greater than the corresponding historical maximum values ​​and greater than the upper deviation corresponding to the design values, output an abnormal warning; or,

[0067] Step S306: When different types of measurement values ​​are smaller than the corresponding historical minimum values ​​and smaller than the lower deviation corresponding to the design values, an abnormal warning is output.

[0068] The generation of metering values ​​is continuous and has certain rules. Therefore, the metering values ​​are filtered by filtering composed of the design value des, the load ldr, the historical maximum value hmax, and the historical minimum value hmin, so as to eliminate significant errors. Among them, the historical extreme value will be recorded after each metering value is confirmed, and it will be obtained within the time interval set in the metering rules when used. In the above steps S302 to S304, different types of metering values ​​are obtained by correcting the model, and then the relationship between the metering value and the historical extreme value and the design value is compared, so as to eliminate the abnormal metering value whose metering value is greater than the historical maximum value and the metering value is greater than the upper deviation corresponding to the design value, or the metering value is less than the historical minimum value and less than the lower deviation corresponding to the design value, and output an abnormal warning.

[0069] The filter model is as follows Figure 3b As shown, by combining the three constraint objects of historical extreme value, design value and load, while filtering the measurement value, the correct and reliable confirmation value is selected to update the filter library to form feedback, so as to continuously expand the constraint objects in different time intervals and improve the filtering link.

[0070] exist Figure 3b In the flowchart of the filter model shown, the metering value obtained after the correction model is input into the filter model to determine whether the metering value is less than the minimum value. When the metering value is greater than the minimum value, determine whether the metering value is greater than the maximum value. If the metering value does not exceed the maximum value, it means that the metering value is not an obviously abnormal metering value, and then the metering value is output. In the above process, when it is determined that the metering value is less than the minimum value, the size relationship between the metering value and the lower deviation is determined, where the lower deviation = instrument design value × load - design deviation. When the metering value is less than the lower deviation, the metering value is filtered and an abnormal warning is output; if the metering value is greater than or equal to the lower deviation, the minimum value corresponding to the time interval of the metering rule is found by searching the historical extreme value records, and the size relationship between the metering value and the minimum value is determined again. Similarly, when it is determined that the metering value is greater than the maximum value, the relationship between the metering value and the upper deviation is determined, where the upper deviation = instrument design value × load + design deviation. When the metering value is greater than the upper deviation, the metering value is filtered and an abnormal warning is output; if the metering value is less than or equal to the upper deviation, the maximum value corresponding to the time interval of the metering rule is found by searching the historical extreme value records, and the relationship between the metering value and the maximum value is determined again.

[0071] In step S202 of the above data processing method, it is determined whether there is an abnormality in the measurement value, including: the type of the measurement value at least includes a cumulative measurement value and an instantaneous measurement value, and according to the identifiers corresponding to different types of measurement values, it is determined whether there is an abnormality in different types of measurement values.

[0072] In the above steps, based on the identifiers corresponding to different types of measurement values, it is determined whether different types of measurement values ​​are abnormal, such as Figure 4 The flowchart shown specifically includes the following steps:

[0073] Step S402, the identifier corresponding to the cumulative measurement value is the first identifier. When the first identifier is 0 and the cumulative measurement value changes suddenly, it is determined that the cumulative measurement value is abnormal. When the first identifier is 0, it indicates that the instrument value does not reach the pre-reset value and points to the post-reset value;

[0074] Step S404, the identifier corresponding to the instantaneous quantity measurement value is the second identifier. When the second identifier is 0 and the instantaneous quantity measurement value changes suddenly, it is determined that the instantaneous quantity measurement value is abnormal. The second identifier is 0, which means that the meter reaches zero or negative value instantaneously due to external interference.

[0075] In the above steps S402 to S404, the value of the cumulative measurement value changes unidirectionally and steadily, while the value of the instantaneous measurement value changes disorderly and fluctuates. Based on this feature, two types of identification marks are established: automatic reset TFR and whether TFN is allowed. Combined with the value change characteristics of different types of measurement values, the correctness of the measurement process is judged, and it is convenient to switch and adjust the type of measurement value later.

[0076] Automatic reset TFR: When this flag is 1, the instrument supports automatic reset. When the instrument value reaches the pre-reset value, it will reset to the post-reset value in a very short time, and then start to approach the pre-reset value as a whole. Therefore, when this flag is 0, it means that the instrument value does not reach the pre-reset value and points to the post-reset value. If the measurement value does not conform to the consistent change trend, it will be identified as an abnormal measurement value.

[0077] Whether negative TFN is allowed: If this mark is 1, the instantaneous measurement value is allowed to be negative, and the cumulative measurement value is allowed to be less than the initial value at the end of the period; if this mark is 0, the initial value of the instrument is 0 or a constant positive value, and the cumulative measurement value increases positively. If there is no automatic reset, the final value is greater than the initial value in any time interval. Therefore, when this mark is 0, a negative instantaneous value at a single point or a negative change rate as a whole is an abnormal measurement value. The positive cumulative and constant positive instantaneous measurement values ​​are set to 0, and the negative cumulative and instantaneous measurement values ​​are set to 1. Among them, the initial value is the measurement value at the beginning of the statistical time period, and the final value is the measurement value at the end of the statistical time period.

[0078] The instrument zeroing phenomenon is that the initial value of the cycle is higher, the final value is lower, and the difference is less than zero. If TFR is set to 1, the recording instrument will automatically reset within the cycle. At this time, if TFN is 0, the instrument zeroing exception processing is performed:

[0079]

[0080] In the above formula (1), the formula in the first row represents the calculation formula of the MPV corresponding to the cumulative measurement value, and the formula in the second row represents the calculation formula of the MPV corresponding to the instantaneous measurement value. ZS is the measurement value before returning to zero, and ZE is the measurement value after returning to zero. {MP} et is the data corresponding to the end time et of the measurement period, {MP} st The data corresponding to the start time st of the metering cycle is the instrument value, that is, the data stored in the real-time database.

[0081] If TFN is 1, the value change caused by returning to zero is accepted:

[0082]

[0083] In the above formula (2), the formula in the first row represents the calculation formula of the MPV corresponding to the cumulative measurement value, and the formula in the second row represents the calculation formula of the MPV corresponding to the instantaneous measurement value.

[0084] In step S204 of the above data processing method, when there is an abnormality in the measurement value, the first time interval corresponding to the measurement value is split to obtain multiple sub-intervals, such as Figure 5 The flowchart shown specifically includes the following steps:

[0085] Step S502: when the first identifier is 0, the first time interval corresponding to the measurement value is evenly divided into multiple second time intervals;

[0086] Step S504, determining the difference between the measurement values ​​of adjacent time intervals in all second time intervals;

[0087] Step S506, when the difference in the second time interval is negative, evenly split the second time interval corresponding to the negative difference into third time intervals, wherein the time period of the third time interval is smaller than the time period of the second time interval;

[0088] Step S508, determining the difference between the measurement values ​​of adjacent time intervals in all third time intervals;

[0089] Step S510: when the difference in the third time interval is negative, recursively split the third time interval corresponding to the negative difference until the split time interval is less than the inverse of the collection frequency of the data corresponding to the measurement value, thereby obtaining multiple sub-intervals.

[0090] In step S206 of the above-mentioned method for processing measurement values, the measurement values ​​in multiple sub-intervals are processed to obtain a target measurement value, which specifically includes the following steps: determining a cumulative measurement value based on the measurement values ​​corresponding to the time intervals with negative differences in the multiple sub-intervals, the measurement values ​​corresponding to other time intervals in the multiple sub-intervals except the time intervals with negative differences, the measurement coefficient, and the constant deviation, and the cumulative measurement value is the target measurement value.

[0091] In step S206 of the above data processing method, the measurement values ​​in the multiple sub-intervals are processed to obtain the target measurement value, such as Figure 6 The flowchart shown specifically also includes the following steps:

[0092] Step S602, determining a target value of the instantaneous quantity measurement value in a plurality of subintervals, wherein the target value is a maximum value of each of the plurality of subintervals;

[0093] Step S604, comparing the target value with the end value in each sub-interval, wherein the end value is the measurement value corresponding to the end time of each sub-interval;

[0094] Step S606: After processing the maximum value between the target value and the end-of-period value by using the constant deviation, an instantaneous quantity measurement value is obtained, and the instantaneous quantity measurement value is the target measurement value.

[0095] In the above steps S502 to S510 and S602 to S606, when TFR is set to 0, the recording instrument returns to zero abnormally. At this time, regardless of whether the value of TFN is 0 or 1, the time interval needs to be differentially sliced, and the time interval △t is split into N equal coarse slices, and it is determined whether the measurement difference △dv of the new time interval is negative. If it is negative, a recursive method is formed to further differentially slice the time interval corresponding to the negative measurement difference into M equal fine slices, where M is less than or equal to N / 10, until the granularity of the time interval reaches the reciprocal 1 / v of the real-time database acquisition frequency v, then the data is regressed and the time interval is no longer divided. It should be noted that the above M is less than or equal to N / 10, in which 10 is an empirical value. In the embodiment of the present application, any positive integer greater than or equal to 2 can be selected according to the actual situation.

[0096] When TFR is set to 0, the calculation formula of MPV is as follows:

[0097]

[0098] In the above formula (3), the formula in the first row represents the calculation formula of the MPV corresponding to the cumulative measurement value, and the formula in the second row represents the calculation formula of the MPV corresponding to the instantaneous measurement value. The recursive function Fac() after differentiation is for the set of slices where △dv is less than or equal to 0, and Fam() is for the set of slices where the maximum measurement value is located.mt is the mapping formula when the maximum value of the measurement value occurs. The recursive formula is as follows:

[0099]

[0100]

[0101] Through the above formula (4) and formula (5), the target measurement value after the abnormality of the cumulative measurement value and the instantaneous measurement value is finally calculated.

[0102] In an embodiment of the data processing method, when it is determined that an abnormality exists in the measurement value, a first time interval corresponding to the measurement value is split to obtain multiple sub-intervals, so that the measurement values ​​of the multiple sub-intervals are processed and the measurement values ​​corresponding to the measurement values ​​are obtained, thereby achieving the purpose of refining the time interval and facilitating the search for abnormal measurement values, thereby achieving the technical effect of obtaining the measurement values ​​of the split time interval while ensuring the calculation efficiency, and aggregating the measurement values ​​to replace the measurement values ​​in the first interval containing the abnormal measurement value, thereby solving the technical problem that the time interval of the abnormal measurement value automatically detected and eliminated is large, causing result deviation and increasing the time spent on processing the abnormality.

[0103] This application is a new type of calculation model, which fully considers the anomalies that may occur during the automatic metering process and establishes a data anomaly library. By analyzing the differences between the two types of measurement, cumulative and instantaneous, adding identification marks, and then identifying and calculating in the calculation model, it solves the problem that the objects processed in the prior art are relatively single and cannot simultaneously meet the metering management mode where both on-site cumulative and instantaneous quantities coexist. Finally, differential data slicing is used to refine the metering granularity while minimizing the time interval required for recursive calculation, so that this application can become an accurate and efficient method for detecting and processing abnormal metering values.

[0104] In summary, the significance of this application is mainly reflected in the following aspects: 1. Establish an error correction model to extract the interference factors before the measurement value is generated, and abstract it into a mapping formula to perform basic correction on the instrument value, avoiding the direct use of real-time database data. 2. Combine the three constraint objects of historical extreme values, design values, and loads to form a filtering model. While filtering the data, select correct and reliable confirmation values ​​to update the filter library, form feedback, and continuously expand the constraint objects under different time intervals and improve the filtering link. 3. Use coarse and fine slices to granulate the broad time interval, and then further divide it through a targeted recursive model, and continuously extract the valid measurement values ​​that need to be merged, so as to obtain the measurement values ​​in the small interval for aggregation while ensuring the calculation efficiency, and replace the measurement values ​​of the large interval containing abnormal measurement values.

[0105] The data collected by the above real-time database can truly reflect the changes in instrument values ​​within a certain time interval. After correction by the correction coefficient and two types of identification branches, cumulative quantity and instantaneous quantity, a continuous measurement process can be repeatedly decomposed into continuous measurements in smaller time intervals, forming a recursion based on differentiation, thereby avoiding the influence of anomalies and errors on the overall measurement results.

[0106] Figure 7 is a structural diagram of a data processing device according to an embodiment of the present application, such as Figure 7 As shown, the device includes: a judgment module 702, which is used to judge whether there is an abnormality in the measurement value; a splitting module 704, which is used to split the first time interval corresponding to the measurement value to obtain multiple sub-intervals when the measurement value is abnormal; and a processing module 706, which is used to process the measurement values ​​in the multiple sub-intervals to obtain the target measurement value.

[0107] Before the judgment module 702 in the data processing device judges whether the measurement value is abnormal, the device also includes the following process: correcting the data through a correction model to obtain the measurement value, and the correction model is determined at least based on the measurement coefficient and the constant deviation.

[0108] In the above process, after the data is corrected by the correction model to obtain the measurement value, the device also includes the following process: judging the relationship between different types of measurement values ​​and the corresponding historical extreme values ​​and design values, wherein the historical extreme values ​​include historical maximum values ​​and historical minimum values, and the design value is determined based on the rated hourly production or consumption and the collection cycle of the equipment to which the instrument belongs; when different types of measurement values ​​are greater than the corresponding historical maximum values ​​and greater than the upper deviation corresponding to the design value, outputting an abnormal warning; or when different types of measurement values ​​are less than the corresponding historical minimum values ​​and less than the lower deviation corresponding to the design value, outputting an abnormal warning.

[0109] The judgment module 702 in the data processing device judges whether there is an abnormality in the measurement value, including: the type of the measurement value at least includes the cumulative measurement value and the instantaneous measurement value, and according to the identifiers corresponding to the different types of measurement values, judges whether there is an abnormality in different types of measurement values.

[0110] In the above steps, based on the identifiers corresponding to different types of measurement values, it is judged whether different types of measurement values ​​have abnormalities, which specifically includes the following process: the identifier corresponding to the cumulative measurement value is the first identifier. When the first identifier is 0 and the cumulative measurement value changes suddenly, it is determined that the cumulative measurement value has an abnormality, wherein the first identifier is 0, indicating that the instrument value points to the post-reset value when it has not reached the pre-reset value; the identifier corresponding to the instantaneous measurement value is the second identifier. When the second identifier is 0 and the instantaneous measurement value changes suddenly, it is determined that the instantaneous measurement value has an abnormality, wherein the second identifier is 0, indicating that the instrument reaches a zero value or a negative value instantaneously due to external interference factors.

[0111] The splitting module 704 in the data processing device, when there is an abnormality in the measurement value, splits the first time interval corresponding to the measurement value to obtain multiple sub-intervals, specifically including the following process: when the first identifier is 0, evenly split the first time interval corresponding to the measurement value into multiple second time intervals; determine the difference in the measurement values ​​of adjacent time intervals in all second time intervals; when the difference in the second time interval is negative, evenly split the second time interval corresponding to the negative difference into third time intervals, wherein the time period of the third time interval is smaller than the time period of the second time interval; determine the difference in the measurement values ​​of adjacent time intervals in all third time intervals; when the difference in the third time interval is negative, recursively split the third time interval corresponding to the negative difference until the split time interval is smaller than the inverse of the acquisition frequency of the data corresponding to the measurement value, thereby obtaining multiple sub-intervals.

[0112] In the processing module 706 in the data processing device, the measurement values ​​in multiple sub-intervals are processed to obtain the target measurement value, which specifically includes the following process: the cumulative measurement value is determined based on the measurement values ​​corresponding to the time intervals with negative differences in the multiple sub-intervals, the measurement values ​​corresponding to other time intervals in the multiple sub-intervals except the time intervals with negative differences, the measurement coefficient, and the constant deviation, and the cumulative measurement value is the target measurement value.

[0113] In the processing module 706 in the data processing device, the measurement values ​​in multiple sub-intervals are processed to obtain the target measurement value, which specifically includes the following processes: determining the target value of the instantaneous measurement value in multiple sub-intervals, wherein the target value is the maximum value of each sub-interval in the multiple sub-intervals; comparing the target value with the end value in each sub-interval, wherein the end value is the measurement value corresponding to the end time of each sub-interval; after processing the maximum value between the target value and the end value by a constant deviation, an instantaneous measurement value is obtained, and the instantaneous measurement value is the target measurement value.

[0114] The data processing device in the present application is based on the automatic metering method of the real-time database. Combined with the abnormal situations that may occur in the metering process, an automatic metering mode is established to quickly eliminate abnormal recalculations in small time intervals, so as to solve the automatic metering anomaly detection and processing needs of continuous production enterprises in metering management.

[0115] It should be noted that Figure 7 The data processing device shown is used to perform Figures 2 to 6 The data processing method shown, therefore the relevant explanations in the above data processing method are also applicable to the data processing device, and will not be repeated here.

[0116] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following data processing method: determine whether there is an abnormality in the measurement value; if there is an abnormality in the measurement value, split the first time interval corresponding to the measurement value to obtain multiple sub-intervals; process the measurement values ​​in the multiple sub-intervals to obtain a target measurement value.

[0117] In the data processing method, before determining whether the measurement value is abnormal, the method further includes the following steps: correcting the data by using a correction model to obtain the measurement value, wherein the correction model is determined at least based on the measurement coefficient and the constant deviation.

[0118] In the data processing method, after the data is corrected by the correction model to obtain the measurement value, the method also includes the following steps: judging the relationship between different types of measurement values ​​and the corresponding historical extreme values ​​and design values, wherein the historical extreme values ​​include historical maximum values ​​and historical minimum values, and the design value is determined based on the rated hourly production or consumption and the collection cycle of the equipment to which the instrument belongs; when different types of measurement values ​​are greater than the corresponding historical maximum values ​​and greater than the upper deviation corresponding to the design value, outputting an abnormal warning; or when different types of measurement values ​​are less than the corresponding historical minimum values ​​and less than the lower deviation corresponding to the design value, outputting an abnormal warning.

[0119] In the data processing method, judging whether there is an abnormality in the measurement value specifically includes the following steps: the type of the measurement value includes at least a cumulative measurement value and an instantaneous measurement value, and judging whether there is an abnormality in different types of measurement values ​​based on the identifiers corresponding to different types of measurement values.

[0120] In the data processing method, based on the identifiers corresponding to different types of measurement values, it is judged whether different types of measurement values ​​have abnormalities, which specifically includes the following steps: the identifier corresponding to the cumulative measurement value is a first identifier, and when the first identifier is 0 and the cumulative measurement value changes suddenly, it is determined that the cumulative measurement value has an abnormality, wherein the first identifier is 0, indicating that the instrument value points to the post-reset value when it has not reached the pre-reset value; the identifier corresponding to the instantaneous measurement value is a second identifier, and when the second identifier is 0 and the instantaneous measurement value changes suddenly, it is determined that the instantaneous measurement value has an abnormality, wherein the second identifier is 0, indicating that the instrument reaches a zero value or a negative value instantaneously due to external interference.

[0121] In the data processing method, when there is an abnormality in the measurement value, the first time interval corresponding to the measurement value is split to obtain multiple sub-intervals, which specifically includes the following steps: when the first identifier is 0, the first time interval corresponding to the measurement value is evenly split into multiple second time intervals; the difference in the measurement values ​​of adjacent time intervals in all the second time intervals is determined; when the difference in the second time interval is negative, the second time interval corresponding to the negative difference is evenly split into a third time interval, wherein the time period of the third time interval is smaller than the time period of the second time interval; the difference in the measurement values ​​of adjacent time intervals in all the third time intervals is determined; when the difference in the third time interval is negative, the third time interval corresponding to the negative difference is recursively split until the split time interval is smaller than the inverse of the collection frequency of the data corresponding to the measurement value, thereby obtaining multiple sub-intervals.

[0122] In the data processing method, the measurement values ​​in multiple sub-intervals are processed to obtain a target measurement value, which specifically includes the following steps: determining a cumulative measurement value based on the measurement values ​​corresponding to the time intervals with negative differences in the multiple sub-intervals, the measurement values ​​corresponding to other time intervals in the multiple sub-intervals except the time intervals with negative differences, the measurement coefficient, and the constant deviation, and the cumulative measurement value is the target measurement value.

[0123] In the data processing method, the measurement values ​​in multiple sub-intervals are processed to obtain target measurement values, and the method also includes the following steps: determining the target value of the instantaneous measurement value in the multiple sub-intervals, wherein the target value is the maximum value of each sub-interval in the multiple sub-intervals; comparing the target value with the end-of-period value in each sub-interval, wherein the end-of-period value is the measurement value corresponding to the end time of each sub-interval; and obtaining the instantaneous measurement value after processing the maximum value between the target value and the end-of-period value by a constant deviation, wherein the instantaneous measurement value is the target measurement value.

[0124] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0125] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0130] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for data processing, characterized in that: include: Determine whether there is any abnormality in the measurement value; When the measurement value is abnormal, split the first time interval corresponding to the measurement value to obtain multiple sub-intervals; Processing the measurement values ​​in the multiple subintervals to obtain a target measurement value; In the case where the metering value is abnormal, splitting the first time interval corresponding to the metering value to obtain multiple sub-intervals includes: when the first identifier corresponding to the cumulative metering value is 0 and the cumulative metering value changes suddenly, determining that the cumulative metering value is abnormal, evenly splitting the first time interval corresponding to the metering value into multiple second time intervals, wherein the first identifier is 0, indicating that the instrument value points to the reset value when it has not reached the pre-reset value, and the cumulative metering value is a type of the metering value; judging the difference between metering values ​​of adjacent time intervals in all the second time intervals; when the difference in the second time interval is negative, evenly splitting the second time interval corresponding to the negative difference into third time intervals, wherein the time period of the third time interval is less than the time period of the second time interval; judging the difference between metering values ​​of adjacent time intervals in all the third time intervals; when the difference in the third time interval is negative, recursively splitting the third time interval corresponding to the negative difference until the time interval after splitting is less than the inverse of the acquisition frequency of the data corresponding to the metering value, to obtain the multiple sub-intervals.

2. The data processing method according to claim 1, characterized in that: Before determining whether the measurement value is abnormal, the method further includes: correcting the data through a correction model to obtain the measurement value, and the correction model is determined at least based on the measurement coefficient and the constant deviation.

3. The data processing method according to claim 2, characterized in that: After the data is corrected by the correction model to obtain the measurement value, the method further includes: Determine the relationship between different types of measurement values ​​and corresponding historical extreme values ​​and design values, wherein the historical extreme values ​​include historical maximum values ​​and historical minimum values, and the design value is determined based on the rated hourly production or consumption and the collection cycle of the equipment to which the meter belongs; When the measurement values ​​of different types are greater than the corresponding historical maximum values ​​and greater than the upper deviation corresponding to the design values, an abnormal warning is output; or, When the measurement values ​​of the different types are smaller than the corresponding historical minimum values ​​and smaller than the lower deviation corresponding to the design value, an abnormal warning is output.

4. The data processing method according to claim 1, characterized in that: Judging whether there is an abnormality in the measurement value includes: the type of the measurement value also includes an instantaneous measurement value, and judging whether there is an abnormality in the different types of measurement values ​​according to identifiers corresponding to the different types of measurement values.

5. The data processing method according to claim 4, characterized in that: Judging whether the different types of measurement values ​​are abnormal according to the identifiers corresponding to the different types of measurement values ​​includes: The identifier corresponding to the instantaneous quantity measurement value is a second identifier. When the second identifier is 0 and the instantaneous quantity measurement value changes suddenly, it is determined that there is an abnormality in the instantaneous quantity measurement value, wherein the second identifier is 0, indicating that the instrument instantaneously reaches zero or a negative value due to external interference.

6. The data processing method according to claim 4, characterized in that: Processing the measurement values ​​in the multiple subintervals to obtain a target measurement value includes: The cumulative measurement value is determined according to the measurement values ​​corresponding to the time interval with a negative difference in the multiple subintervals, the measurement values ​​corresponding to other time intervals in the multiple subintervals except the time interval with a negative difference, the measurement coefficient, and the constant deviation, and the cumulative measurement value is the target measurement value.

7. The data processing method according to claim 4, characterized in that: Processing the measurement values ​​in the multiple subintervals to obtain a target measurement value, the method further includes: Determining target values ​​of the instantaneous quantity measurement value in the plurality of subintervals, wherein the target value is a maximum value of each subinterval in the plurality of subintervals; Comparing the target value with the end-of-period value in each sub-interval, wherein the end-of-period value is a measurement value corresponding to the end time of each sub-interval; After processing the maximum value between the target value and the end-of-period value by means of a constant deviation, the instantaneous quantity measurement value is obtained, and the instantaneous quantity measurement value is the target measurement value.

8. A data processing device, characterized in that: include: A judgment module is used to judge whether there is an abnormality in the measurement value; a splitting module, configured to split the first time interval corresponding to the measurement value into a plurality of sub-intervals when the measurement value is abnormal; A processing module, used for processing the measurement values ​​in the multiple sub-intervals to obtain a target measurement value; The splitting module is also used to determine that the cumulative quantity metering value is abnormal when the first identifier corresponding to the cumulative quantity metering value is 0 and the cumulative quantity metering value changes suddenly, and evenly split the first time interval corresponding to the metering value into multiple second time intervals, wherein the first identifier is 0, indicating that the instrument value points to the post-reset value when it has not reached the pre-reset value, and the cumulative quantity metering value is a type of the metering value; judge the difference between the metering values ​​of adjacent time intervals in all the second time intervals; when the difference in the second time interval is negative, evenly split the second time interval corresponding to the negative difference into a third time interval, wherein the time period of the third time interval is less than the time period of the second time interval; judge the difference between the metering values ​​of adjacent time intervals in all the third time intervals; when the difference in the third time interval is negative, recursively split the third time interval corresponding to the negative difference until the split time interval is less than the inverse of the acquisition frequency of the data corresponding to the metering value, and obtain the multiple sub-intervals.

9. An electronic device, characterized in that: include: A memory for storing program instructions; A processor, connected to the memory, is used to execute program instructions to implement the following functions: determine whether the measurement value is abnormal; When the measurement value is abnormal, split the first time interval corresponding to the measurement value to obtain multiple sub-intervals; Processing the measurement values ​​in the multiple subintervals to obtain a target measurement value; In the case where the metering value is abnormal, splitting the first time interval corresponding to the metering value to obtain multiple sub-intervals includes: when the first identifier corresponding to the cumulative metering value is 0 and the cumulative metering value changes suddenly, determining that the cumulative metering value is abnormal, evenly splitting the first time interval corresponding to the metering value into multiple second time intervals, wherein the first identifier is 0, indicating that the instrument value points to the reset value when it has not reached the pre-reset value, and the cumulative metering value is a type of the metering value; judging the difference between metering values ​​of adjacent time intervals in all the second time intervals; when the difference in the second time interval is negative, evenly splitting the second time interval corresponding to the negative difference into third time intervals, wherein the time period of the third time interval is less than the time period of the second time interval; judging the difference between metering values ​​of adjacent time intervals in all the third time intervals; when the difference in the third time interval is negative, recursively splitting the third time interval corresponding to the negative difference until the time interval after splitting is less than the inverse of the acquisition frequency of the data corresponding to the metering value, to obtain the multiple sub-intervals.

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