Abnormal data determination method, device and system and storage medium

Abstract

The invention discloses an abnormal data determination method, device and system and a storage medium. The method comprises the steps of obtaining index values of a current carbon emission index and a production operation index and index values of a previous carbon emission index and a previous production operation index; when the current-period carbon emission index is in a first preset range, the difference value between the current-period carbon emission index and the previous-period carbon emission index is in a second preset range, and the link ratio of the current-period carbon emission index is in a third preset range, determining that the current-period carbon emission index is in the first preset range; checking the logic relationship between the current carbon emission index and the production operation index based on a preset index logic relationship checking table; when the to-be-checked indexes which do not conform to the logic relations recorded in the index logic relation checking table exist, the change trend of all the to-be-checked indexes is obtained; and determining the to-be-checked indexes inconsistent with the change trends of the other to-be-checked indexes as abnormal indexes. According to the scheme, the efficiency of carbon data monitoring can be improved, and the accuracy of abnormal data determination is improved.

Description

Technical Field

[0001] The present application relates to the field of data monitoring technology, and in particular to a method, device, system and storage medium for determining abnormal data. Background Art

[0002] Carbon emission monitoring is one of the important means of environmental protection and climate change response. The authenticity and reliability of carbon emission index data are closely related to the healthy development of the carbon market. In view of this, the monitoring of abnormal carbon emission indicators is particularly important. In the existing technology, the monitoring of carbon emission indicators is often based on the experience of the staff to analyze the changing trends of various carbon emission indicators. This monitoring method not only requires a lot of work for the staff, but also relies heavily on the experience of the staff, which leads to inconsistent monitoring standards and difficulty in ensuring data quality; in addition, normal carbon emission indicator fluctuations caused by increased production efforts and other reasons are also easily listed as data anomalies.

[0003] Therefore, how to provide a method for determining abnormal data to improve the efficiency of carbon emission indicator monitoring and the accuracy of abnormal data determination has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method, device, system and storage medium for determining abnormal data, so as to improve the efficiency of carbon data monitoring and enhance the accuracy of abnormal data determination.

[0005] The present application provides a method for determining abnormal data, comprising:

[0006] Obtain the index value of the current carbon emission index and the index value of the production and operation index, as well as the index value of the previous carbon emission index and the index value of the production and operation index;

[0007] When the current carbon emission index is within the first preset range, and the difference between the current carbon emission index and the previous carbon emission index is within the second preset range, and the month-on-month increase in the current carbon emission index is within the third preset range, the logical relationship between the current carbon emission index and the production operation index is checked based on the preset index logical relationship check table;

[0008] When there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, obtain the change trend of each indicator to be checked;

[0009] The indicators to be checked that have a changing trend that is inconsistent with other indicators to be checked are determined to be abnormal indicators.

[0010] The beneficial effects of the present application are as follows: the present application first analyzes the extreme abnormality of the indicator from the perspectives of the interval range of the carbon emission indicator, the difference between the current period and the previous period, and the current period's month-on-month change; when the interval range of the carbon emission indicator, the difference between the current period and the previous period, and the current period's month-on-month change are all in a reasonable range, indicating that there is no extreme abnormality, the carbon emission indicator and the production operation indicator are combined, and the logical relationship between the current carbon emission indicator and the production operation indicator is checked based on the preset indicator logical relationship check table. When there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, the change trend of each indicator to be checked is obtained, and the indicators to be checked that are inconsistent with the change trends of other indicators to be checked are determined to be abnormal indicators. Based on the present application, carbon emission data can be automatically monitored, which improves the efficiency of carbon data monitoring. In addition, the present application not only checks the carbon emission indicator itself, but also conducts logical analysis in combination with the production operation data, thereby improving the accuracy of abnormal data determination.

[0011] In one embodiment, the carbon emission indicators include the unit's carbon dioxide emissions, power supply carbon emission intensity and heat supply carbon emission intensity, and the production operation indicators include operating time, load rate, power generation, power supply, heat supply, heating ratio, power supply coal consumption, and heating coal consumption.

[0012] In one embodiment, the method further comprises:

[0013] When the current carbon emission index exceeds the first preset range, or the difference between the current carbon emission index and the previous carbon emission index exceeds the second preset range, or the current carbon emission index exceeds the third preset range on a month-on-month basis, the current carbon emission index is determined to be an abnormal index.

[0014] In one embodiment, the method further comprises:

[0015] When there are no indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, it is determined that there is no data anomaly in the current carbon emission indicators.

[0016] In one embodiment, the method further comprises:

[0017] When the change trends of the indicators to be checked are consistent, the predicted values ​​of the indicators to be checked are obtained;

[0018] When there is an indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that the indicator to be checked is an abnormal indicator.

[0019] In one embodiment, the method further comprises:

[0020] When there is no to-be-checked indicator whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that all the to-be-checked indicators are abnormal indicators.

[0021] In one embodiment, the method further comprises:

[0022] Obtaining an analysis checklist of the abnormal indicator, wherein the analysis checklist records various indicators related to the abnormal indicator, logical relationships between various related indicators, and abnormal causes corresponding to different logical relationships;

[0023] Obtaining the index values ​​of each related index of the abnormal index, and testing the logical relationship between each related index;

[0024] The abnormal reasons corresponding to the abnormal indicators occurring in the current period are determined according to the test results.

[0025] The present application also provides a device for determining abnormal data, comprising:

[0026] A first acquisition module is used to acquire the index value of the carbon emission index and the index value of the production and operation index of the current period, as well as the index value of the carbon emission index and the index value of the production and operation index of the previous period;

[0027] A troubleshooting module, used to troubleshoot the logical relationship between the current carbon emission index and the production operation index based on a preset index logical relationship troubleshooting table when the current carbon emission index is within a first preset range, the difference between the current carbon emission index and the previous carbon emission index is within a second preset range, and the month-on-month increase in the current carbon emission index is within a third preset range;

[0028] The second acquisition module is used to obtain the change trend of each indicator to be checked when there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship checking table;

[0029] The first determination module is used to determine that the indicator to be checked that has a change trend inconsistent with other indicators to be checked is an abnormal indicator.

[0030] In one embodiment, the carbon emission indicators include the unit's carbon dioxide emissions, power supply carbon emission intensity and heat supply carbon emission intensity, and the production operation indicators include operating time, load rate, power generation, power supply, heat supply, heating ratio, power supply coal consumption, and heating coal consumption.

[0031] In one embodiment, the apparatus further comprises:

[0032] The second determination module is used to determine that the current carbon emission index is an abnormal index when the current carbon emission index exceeds the first preset range, or the difference between the current carbon emission index and the previous carbon emission index exceeds the second preset range, or the month-on-month increase in the current carbon emission index exceeds the third preset range.

[0033] In one embodiment, the apparatus further comprises:

[0034] The third determination module is used to determine that there is no data anomaly in the current carbon emission indicators when there are no indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table.

[0035] In one embodiment, the apparatus further comprises:

[0036] The third acquisition module is used to obtain the predicted value of each indicator to be checked when the change trends of each indicator to be checked are consistent;

[0037] The fourth determination module is used to determine that the indicator to be checked is an abnormal indicator when there is an indicator to be checked whose ratio of the indicator value to the predicted value exceeds a fourth preset range.

[0038] In one embodiment, the apparatus further comprises:

[0039] The fifth determination module is used to determine that all the indicators to be checked are abnormal indicators when there is no indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range.

[0040] In one embodiment, the apparatus further comprises:

[0041] A fourth acquisition module is used to obtain an analysis checklist of the abnormal index, wherein the analysis checklist records various related indexes of the abnormal index, logical relationships between the various related indexes, and abnormal causes corresponding to different logical relationships;

[0042] A fifth acquisition module, used to acquire the index values ​​of each related index of the abnormal index, and to check the logical relationship between each related index;

[0043] The sixth determination module is used to determine the abnormal cause corresponding to the abnormal indicator appearing in the current period according to the inspection result.

[0044] The present application also provides a system for determining abnormal data, comprising:

[0045] at least one processor; and,

[0046] a memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the method for determining abnormal data recorded in any of the above embodiments.

[0048] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the abnormal data determination system, the abnormal data determination system can implement the abnormal data determination method recorded in any of the above embodiments.

[0049] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0050] The technical solution of the present application is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0052] Figure 1 This is a flow chart of a method for determining abnormal data in one embodiment of the present application;

[0053] Figure 2 This is a logic diagram for analyzing the carbon emission intensity and coal consumption of power supply in one embodiment of the present application;

[0054] Figure 3 This is a schematic diagram of the structure of a device for determining abnormal data in an embodiment of the present application;

[0055] Figure 4 The figure is a schematic diagram of the hardware structure of a system for determining abnormal data in one embodiment of the present application. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0057] Figure 1 Flow chart of a method for determining abnormal data in one embodiment of the present application. Figure 1 As shown, the method can be implemented as the following steps S101-S104:

[0058] In step S101, the index value of the current carbon emission index and the index value of the production operation index, as well as the index value of the previous carbon emission index and the index value of the production operation index are obtained;

[0059] In step S102, when the current carbon emission index is within the first preset range, and the difference between the current carbon emission index and the previous carbon emission index is within the second preset range, and the year-on-year increase in the current carbon emission index is within the third preset range, the logical relationship between the current carbon emission index and the production operation index is checked based on the preset index logical relationship check table;

[0060] In step S103, when there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship checking table, the change trend of each indicator to be checked is obtained;

[0061] In step S104, the indicator to be checked that has a change trend inconsistent with other indicators to be checked is determined to be an abnormal indicator.

[0062] In this application, the index value of the current carbon emission index and the index value of the production operation index, as well as the index value of the previous carbon emission index and the index value of the production operation index are obtained. In this application, the carbon emission index is monitored from the two levels of carbon emission index measurement and production process to see if it is abnormal. Therefore, not only the carbon emission index needs to be obtained, but also the production operation index needs to be obtained. Among them, the carbon emission index mainly includes carbon emissions and carbon emission intensity. Carbon emissions refer to the total amount of carbon dioxide (CO2) and other greenhouse gases (such as methane, nitrogen oxides, etc.) released into the atmosphere during production, transportation, use and waste treatment. Carbon emission intensity refers to the carbon emissions generated per unit output value or unit activity level, which reflects the degree of influence of unit economic activities on carbon emissions. Specifically, in one embodiment of the present application, the carbon emission index includes unit carbon dioxide emissions, power supply carbon emission intensity and heating carbon emission intensity. The production operation index monitors the production and operation of the enterprise, such as operating time, load rate, power generation, power supply, heat supply, heating ratio, power supply coal consumption, heating coal consumption, output power, etc.

[0063] After obtaining the carbon emission index and production operation index, this application first monitors the value and fluctuation of the index itself to exclude abnormal situations that exceed the reasonable range or fluctuate greatly. Specifically, monitor whether the current carbon emission index is within the first preset range, and whether the difference between the current carbon emission index and the previous carbon emission index is within the second preset range, and whether the current carbon emission index is within the third preset range. When the current carbon emission index exceeds the first preset range, or the difference between the current carbon emission index and the previous carbon emission index exceeds the second preset range, or the current carbon emission index exceeds the third preset range, the current carbon emission index is determined to be an abnormal indicator. At this time, an alarm reminder is issued in time to assist enterprises in timely locating and stopping data anomalies.

[0064] When the current carbon emission index is within the first preset range, and the difference between the current carbon emission index and the previous carbon emission index is within the second preset range, and the current carbon emission index is within the third preset range, it means that the fluctuation of the carbon emission index itself is normal. Therefore, in this application, the logical relationship between the current carbon emission index and the production operation index is checked based on the preset index logical relationship check table. Among them, the data logical relationship check table is pre-constructed and stored, about the logical relationship between each carbon emission index and the production operation index, wherein the logical relationship can be a direct relationship, for example, load rate = (actual output power / rated power) × 100%; heating ratio = = (heating coal consumption × heat supply) / (heating coal consumption × heat supply + power supply coal consumption × power supply), etc., and the logical relationship can also be a qualitative expression of positive correlation, negative correlation, etc. between indicators. When checking the logical relationship between the current carbon emission index and the production operation index based on the preset index logical relationship check table, specifically, each logical relationship in the logical relationship check table is checked one by one. For example, the relationship between indicator a and indicator b should be close to a linear relationship, a = kb + c. When the change in the coefficient k of indicator a and indicator b in a certain month is greater than a specific value, the scope of investigation of the cause of abnormal data is narrowed, that is, a or b may be abnormal. Similarly, when the logical relationship between the target indicators does not conform to the logical relationship recorded in the data logical relationship investigation table, it means that the cause of the abnormality lies in these target indicators, thereby narrowing the scope of investigation. For another example, if the three indicators a, b and c are positively correlated, but the change trends are inconsistent, such as indicator a is on an upward trend, indicator b is on an upward trend, but indicator c is on a downward trend, then indicator c is considered abnormal.

[0065] Of course, when there are no indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, it is determined that there is no data anomaly in the current carbon emission indicators.

[0066] After determining that several indicators do not conform to the logical relationship of indicators, it is necessary to further investigate these indicators. Therefore, when there are indicators to be investigated that do not conform to the logical relationship recorded in the indicator logical relationship investigation table, obtain the change trend of each indicator to be investigated. Among them, the change trend of each indicator to be investigated is the comparison result of the current value of each indicator to be investigated and the statistical index of historical data. Specifically, the statistical index of historical data of each indicator to be investigated in the past period, such as the mean, variance, etc., is obtained, and the ratio of the current value of each indicator to be investigated to the statistical index of historical data is calculated. When the positive and negative signs in the ratio of each indicator to be investigated are inconsistent or the ratio of each indicator to be investigated exceeds the preset value, it is determined that the change trend of the indicator to be investigated is inconsistent with the change trend of other indicators to be investigated. Determine that the indicator to be investigated that is inconsistent with the change trend of other indicators to be investigated is an abnormal indicator. In other words, when there are several inconsistent indicators, the historical data of these indicators are further investigated. When the change trend of a certain indicator and historical data is inconsistent with the change trend of other indicators and historical data, it means that the indicator is abnormal.

[0067] When the changing trends of the indicators to be checked are consistent, the predicted values ​​of the indicators to be checked are obtained; when there is an indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range, the indicator to be checked is determined to be an abnormal indicator. Among them, the predicted value is a predicted value given based on a preset prediction model. The prediction model can be a first prediction model constructed based on the historical data of each indicator itself. For example, a prediction model for the current value is constructed through data within the past year. Through this model, the first predicted value of the indicator based on the historical trend can be analyzed; the first prediction model can be expressed by the following formula:

[0068]

[0069] Among them, y 1 is the first predicted value of the indicator to be checked; α i is the historical index value of each period of the index to be checked; a i is the coefficient of each period of the indicator to be checked; n is the number of periods for statistical analysis of the indicator to be checked; c is a constant.

[0070] The prediction model may also be a second prediction model obtained by fitting multiple carbon emission indicators and production operation indicators, based on which a second prediction value of the indicator can be obtained based on the current production operation indicator and other carbon emission indicators; the second prediction model can be expressed by the following formula:

[0071]

[0072] Among them, y 2 is the second predicted value of the indicator to be checked; β iis the positive indicator among the current production and operation indicators and other carbon emission indicators, that is, the indicator value of the positive indicator is approximately large, and the indicator value of the indicator to be checked is approximately large; γ j is the reverse indicator among the current production and operation indicators and other carbon emission indicators, that is, the indicator value of the reverse indicator is larger, and the indicator value of the indicator to be checked is smaller; b i 、c j are the coefficients corresponding to the positive indicators and the negative indicators respectively; m is the number of positive indicators, and l is the number of negative indicators.

[0073] Of course, a third prediction model can also be obtained by weighted summing the prediction values ​​obtained by the above two prediction models. The third prediction model can be expressed by the following formula:

[0074] y 3 =k 1 y 1 +k 2 y 2 ;

[0075] Among them, y 3 is the third predicted value of the indicator to be checked; 1 is the first predicted value of the indicator to be checked; 2 is the second predicted value of the indicator to be checked; k 1 , k 2 is the corresponding coefficient.

[0076] The coefficients of the above-mentioned prediction models are obtained by fitting historical data. Of course, they can also be automatically updated in real time based on historical data.

[0077] When there is no indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that all the indicators to be checked are abnormal indicators. At this time, it is to avoid missing abnormal data and to remind the staff to further analyze the indicators to be checked.

[0078] Finally, after determining the abnormal data, the abnormal cause can be further analyzed based on the abnormal data. Specifically, obtain an analysis and troubleshooting table of the abnormal indicators, wherein the analysis and troubleshooting table records the indicators related to the abnormal indicators, the logical relationship between the related indicators, and the abnormal causes corresponding to the different logical relationships; obtain the indicator values ​​of the indicators related to the abnormal indicators, and test the logical relationship between the related indicators. Combined with the aforementioned abnormal data troubleshooting process, it can be checked to determine whether the measurement of the data of each related indicator is accurate and whether the production process has changed; determine the abnormal cause corresponding to the abnormal indicator in the current period according to the test results.

[0079] As shown in Table 1, taking the carbon emission data of a cogeneration enterprise's self-owned power plant in June 2023 as an example, the carbon emission index analysis and processing process is explained.

[0080] Table 1 Carbon emission data of a power plant of a cogeneration enterprise before 2023

[0081]

[0082]

[0083] As can be seen from Table 1, under the condition that the process production operation did not change much (operating hours exceeded 200h, and the monthly data of units were comparable), the power supply coal consumption, power supply carbon emission intensity and heat supply ratio in May all fluctuated greatly. Among them, the power supply coal consumption and power supply carbon emission intensity increased by 1.36 times compared with April, the heat supply ratio decreased by 5% month-on-month, and the power supply coal consumption exceeded its reasonable range. In summary, it can be concluded that the carbon data in May was abnormal.

[0084] Then the abnormal data is analyzed. Figure 2 This is a logic diagram for analyzing the carbon emission intensity and coal consumption of power supply in one embodiment of the present application. Figure 2 As shown in the figure, the influencing factors of the carbon emission intensity and coal consumption of power supply of the unit are analyzed respectively. It is found that the carbon emission intensity of power supply is related to the power supply and the carbon dioxide emissions of the unit, among which the power supply is related to the power generation of the unit, and the carbon dioxide emissions of the unit are related to the fossil fuel consumption and the elemental carbon content of fossil fuels (fossil fuel total water, dry elemental carbon content); the coal consumption of power supply is related to the power supply of the unit, the heat supply ratio, and the standard coal consumption of coal. Among them, the heat supply of the unit is related to the heat supply of the boiler (boiler steam volume, feed water volume, generator steam volume), the power supply is related to the power generation, and the standard coal consumption is related to the fossil fuel consumption and the low calorific value of fossil fuels.

[0085] Each factor was investigated one by one from the two dimensions of measurement and production process, as shown in Table 2, Analysis of the causes of abnormal data of power supply carbon emission intensity and power supply coal consumption. After investigation, it was found that the main reason was the failure of the back pressure steam metering instrument of the generator in May, which made the measurement inaccurate, resulting in increased heat loss of the generator set and reduced effective heat supply of the unit, thus leading to high power supply coal consumption.

[0086] Table 2 Analysis of abnormal data heating coal consumption causes

[0087]

[0088]

[0089] The beneficial effects of the present application are as follows: the present application first analyzes the extreme abnormality of the indicator from the perspectives of the interval range of the carbon emission indicator, the difference between the current period and the previous period, and the current period's month-on-month change; when the interval range of the carbon emission indicator, the difference between the current period and the previous period, and the current period's month-on-month change are all in a reasonable range, indicating that there is no extreme abnormality, the carbon emission indicator and the production operation indicator are combined, and the logical relationship between the current carbon emission indicator and the production operation indicator is checked based on the preset indicator logical relationship check table. When there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, the change trend of each indicator to be checked is obtained, and the indicators to be checked that are inconsistent with the change trends of other indicators to be checked are determined to be abnormal indicators. Based on the present application, carbon emission data can be automatically monitored, which improves the efficiency of carbon data monitoring. In addition, the present application not only checks the carbon emission indicator itself, but also conducts logical analysis in combination with the production operation data, thereby improving the accuracy of abnormal data determination.

[0090] In one embodiment, the carbon emission indicators include the unit's carbon dioxide emissions, power supply carbon emission intensity and heat supply carbon emission intensity, and the production operation indicators include operating time, load rate, power generation, power supply, heat supply, heating ratio, power supply coal consumption, and heating coal consumption.

[0091] In one embodiment, the method can also be implemented as follows:

[0092] When the current carbon emission index exceeds the first preset range, or the difference between the current carbon emission index and the previous carbon emission index exceeds the second preset range, or the current carbon emission index exceeds the third preset range on a month-on-month basis, the current carbon emission index is determined to be an abnormal index.

[0093] In one embodiment, the method can also be implemented as follows:

[0094] When there are no indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, it is determined that there is no data anomaly in the current carbon emission indicators.

[0095] In one embodiment, the method can also be implemented as the following steps A1-A2:

[0096] When the change trends of the indicators to be checked are consistent, the predicted values ​​of the indicators to be checked are obtained;

[0097] When there is an indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that the indicator to be checked is an abnormal indicator.

[0098] In one embodiment, the method can also be implemented as follows:

[0099] When there is no to-be-checked indicator whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that all the to-be-checked indicators are abnormal indicators.

[0100] In one embodiment, the method can also be implemented as the following steps B1-B3:

[0101] Obtaining an analysis checklist of the abnormal indicator, wherein the analysis checklist records various indicators related to the abnormal indicator, logical relationships between various related indicators, and abnormal causes corresponding to different logical relationships;

[0102] Obtaining the index values ​​of each related index of the abnormal index, and testing the logical relationship between each related index;

[0103] The abnormal reasons corresponding to the abnormal indicators occurring in the current period are determined according to the test results.

[0104] Figure 3 FIG. 1 is a schematic diagram of a structure of a device for determining abnormal data in an embodiment of the present application. Figure 2 As shown, the device comprises:

[0105] The first acquisition module 301 is used to acquire the index value of the carbon emission index and the index value of the production and operation index of the current period, as well as the index value of the carbon emission index and the index value of the production and operation index of the previous period;

[0106] A troubleshooting module 302 is used to troubleshoot the logical relationship between the current carbon emission index and the production operation index based on a preset index logical relationship troubleshooting table when the current carbon emission index is within a first preset range, the difference between the current carbon emission index and the previous carbon emission index is within a second preset range, and the year-on-year increase in the current carbon emission index is within a third preset range;

[0107] The second acquisition module 303 is used to acquire the change trend of each indicator to be checked when there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship checking table;

[0108] The first determining module 304 is used to determine that the indicator to be checked that has a change trend inconsistent with other indicators to be checked is an abnormal indicator.

[0109] In one embodiment, the carbon emission indicators include the unit's carbon dioxide emissions, power supply carbon emission intensity and heat supply carbon emission intensity, and the production operation indicators include operating time, load rate, power generation, power supply, heat supply, heating ratio, power supply coal consumption, and heating coal consumption.

[0110] In one embodiment, the apparatus further comprises:

[0111] The second determination module is used to determine that the current carbon emission index is an abnormal index when the current carbon emission index exceeds the first preset range, or the difference between the current carbon emission index and the previous carbon emission index exceeds the second preset range, or the month-on-month increase in the current carbon emission index exceeds the third preset range.

[0112] In one embodiment, the apparatus further comprises:

[0113] The third determination module is used to determine that there is no data anomaly in the current carbon emission indicators when there are no indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table.

[0114] In one embodiment, the apparatus further comprises:

[0115] The third acquisition module is used to obtain the predicted value of each indicator to be checked when the change trends of each indicator to be checked are consistent;

[0116] The fourth determination module is used to determine that the indicator to be checked is an abnormal indicator when there is an indicator to be checked whose ratio of the indicator value to the predicted value exceeds a fourth preset range.

[0117] In one embodiment, the apparatus further comprises:

[0118] The fifth determination module is used to determine that all the indicators to be checked are abnormal indicators when there is no indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range.

[0119] In one embodiment, the apparatus further comprises:

[0120] A fourth acquisition module is used to obtain an analysis checklist of the abnormal index, wherein the analysis checklist records various related indexes of the abnormal index, logical relationships between the various related indexes, and abnormal causes corresponding to different logical relationships;

[0121] A fifth acquisition module, used to acquire the index values ​​of each related index of the abnormal index, and to check the logical relationship between each related index;

[0122] The sixth determination module is used to determine the abnormal cause corresponding to the abnormal indicator appearing in the current period according to the inspection result.

[0123] Figure 4 FIG. 1 is a schematic diagram of the hardware structure of a system for determining abnormal data in one embodiment of the present application. Figure 4 As shown, the abnormal data determination system includes:

[0124] at least one processor 420; and,

[0125] A memory 404 in communication with the at least one processor 420; wherein,

[0126] The memory 404 stores instructions that can be executed by the at least one processor 420, and the instructions are executed by the at least one processor 420 to implement the method for determining abnormal data recorded in any of the above embodiments.

[0127] Reference Figure 4 The abnormal data determination system 400 may include one or more of the following components: a processing component 402, a memory 404, a power component 406, a multimedia component 408, an audio component 410, an input / output (I / O) interface 412, a sensor component 414, and a communication component 416.

[0128] The processing component 402 generally controls the overall operation of the determination system 400 of abnormal data. The processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 402 may include one or more modules to facilitate the interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate the interaction between the multimedia component 408 and the processing component 402.

[0129] The memory 404 is configured to store various types of data to support the operation of the abnormal data determination system 400. Examples of such data include instructions for any application or method operating on the abnormal data determination system 400, such as text, pictures, videos, etc. The memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0130] The power supply component 406 provides power to various components of the abnormal data determination system 400. The power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the vehicle control system 400.

[0131] The multimedia component 408 includes a screen providing an output interface between the determination system 400 of abnormal data and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 may also include a front camera and / or a rear camera. When the determination system 400 of abnormal data is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0132] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC), and when the abnormal data determination system 400 is in an operation mode, such as an alarm mode, a recording mode, a voice recognition mode, and a voice output mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 404 or sent via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.

[0133] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0134] The sensor assembly 414 includes one or more sensors for providing various aspects of status assessment for the abnormal data determination system 400. For example, the sensor assembly 414 may include a sound sensor. In addition, the sensor assembly 414 may detect the open / closed state of the abnormal data determination system 400, the relative positioning of the components, such as the display and keypad of the abnormal data determination system 400, and the sensor assembly 414 may also detect the operating state of the abnormal data determination system 400 or a component of the abnormal data determination system 400, the orientation or acceleration / deceleration of the abnormal data determination system 400 and the temperature change of the abnormal data determination system 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 414 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, a material stacking thickness sensor or a temperature sensor.

[0135] The communication component 416 is configured to enable the abnormal data determination system 400 to provide the ability to communicate with other devices and cloud platforms in a wired or wireless manner. The abnormal data determination system 400 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0136] In an exemplary embodiment, the abnormal data determination system 400 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the abnormal data determination method described in any of the above embodiments.

[0137] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the abnormal data determination system, the abnormal data determination system can implement the abnormal data determination method recorded in any of the above embodiments.

[0138] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0139] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0140] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0142] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining abnormal data, characterized in that: include: Obtain the index value of the current carbon emission index and the index value of the production and operation index, as well as the index value of the previous carbon emission index and the index value of the production and operation index; When the current carbon emission index is within the first preset range, and the difference between the current carbon emission index and the previous carbon emission index is within the second preset range, and the month-on-month increase in the current carbon emission index is within the third preset range, the logical relationship between the current carbon emission index and the production operation index is checked based on the preset index logical relationship check table; When there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, obtain the change trend of each indicator to be checked; The indicators to be checked that have a changing trend that is inconsistent with other indicators to be checked are determined to be abnormal indicators.

2. The method according to claim 1, characterized in that The carbon emission indicators include the unit's carbon dioxide emissions, power supply carbon emission intensity and heat supply carbon emission intensity; the production operation indicators include operating time, load rate, power generation, power supply, heat supply, heating ratio, power supply coal consumption and heating coal consumption.

3. The method according to claim 1, characterized in that The method further comprises: When the current carbon emission index exceeds the first preset range, or the difference between the current carbon emission index and the previous carbon emission index exceeds the second preset range, or the current carbon emission index exceeds the third preset range on a month-on-month basis, the current carbon emission index is determined to be an abnormal index.

4. The method according to claim 1, characterized in that The method further comprises: When there are no indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship check table, it is determined that there is no data anomaly in the current carbon emission indicators.

5. The method according to claim 1, characterized in that The method further comprises: When the change trends of the indicators to be checked are consistent, the predicted values ​​of the indicators to be checked are obtained; When there is an indicator to be checked whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that the indicator to be checked is an abnormal indicator.

6. The method according to claim 5, characterized in that The method further comprises: When there is no to-be-checked indicator whose ratio of the indicator value to the predicted value exceeds the fourth preset range, it is determined that all the to-be-checked indicators are abnormal indicators.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining an analysis checklist of the abnormal indicator, wherein the analysis checklist records various indicators related to the abnormal indicator, logical relationships between various related indicators, and abnormal causes corresponding to different logical relationships; Obtaining the index values ​​of each related index of the abnormal index, and testing the logical relationship between each related index; The abnormal reasons corresponding to the abnormal indicators occurring in the current period are determined according to the test results.

8. A device for determining abnormal data, characterized in that: include: A first acquisition module is used to acquire the index value of the carbon emission index and the index value of the production and operation index of the current period, as well as the index value of the carbon emission index and the index value of the production and operation index of the previous period; A troubleshooting module, used to troubleshoot the logical relationship between the current carbon emission index and the production operation index based on a preset index logical relationship troubleshooting table when the current carbon emission index is within a first preset range, the difference between the current carbon emission index and the previous carbon emission index is within a second preset range, and the month-on-month increase in the current carbon emission index is within a third preset range; The second acquisition module is used to obtain the change trend of each indicator to be checked when there are indicators to be checked that do not conform to the logical relationship recorded in the indicator logical relationship checking table; The first determination module is used to determine that the indicator to be checked that has a change trend inconsistent with other indicators to be checked is an abnormal indicator.

9. A system for determining abnormal data, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the method for determining abnormal data according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor corresponding to the abnormal data determination system, the abnormal data determination system can implement the abnormal data determination method according to any one of claims 1 to 7.

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