A low-carbon production data optimization method, device and medium

Abstract

The present application relates to the technical field of data processing, in particular to a low-carbon production data optimization method, device and medium, comprising the following steps: S1: collecting the real-time power grid carbon emission intensity value of the region where the target data center is located; S2: comparing the real-time power grid carbon emission intensity value with the preset carbon intensity threshold value, and outputting the maximum precision degradation level when the threshold value is exceeded; S3: performing precision degradation processing on the original production data to generate a degraded data block; S4: if the fluctuation amplitude exceeds the reliability threshold, then start local precision recovery compensation; S5: integrate the degraded data block and the precision level label after verification, and output a low-carbon optimization data package. Through the combination of real-time monitoring and dynamic precision control of power grid carbon emission intensity, the present application realizes low-carbon optimization processing while reducing carbon emissions and ensuring the reliability of key production data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, equipment and medium for optimizing low-carbon production data. Background Technology

[0002] As a crucial component of the energy-intensive industry, data centers are increasingly drawing attention to the indirect carbon emissions they generate during operation. Grid carbon emission intensity fluctuates with time and regional energy structure changes. When carbon emission intensity is high, the continuous high-precision computing and storage in data centers leads to significant high-carbon electricity consumption, increasing the overall carbon footprint. In the context of green computing power and low-carbon operation, how to dynamically adjust the accuracy strategy of production data in accordance with real-time changes in grid carbon intensity to achieve an operating mode that satisfies both business continuity and low-carbon goals has become an important direction for energy conservation and carbon reduction in data centers.

[0003] In existing technologies, production data accuracy control often relies on fixed strategies, failing to dynamically correlate with real-time grid carbon emission intensity. This makes it impossible to quickly reduce data accuracy to decrease carbon load during peak carbon intensity periods, and also lacks mechanisms for detecting and selectively restoring the reliability of key indicators after degradation. This results in a situation where, in low-carbon operation scenarios, either excessively high data accuracy increases carbon emissions, or excessively low accuracy affects business reliability; a dynamic balance between the two is lacking. Therefore, there is an urgent need for a method, equipment, and medium for optimizing low-carbon production data to address these issues. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a method, equipment, and medium for optimizing low-carbon production data.

[0005] A method for optimizing low-carbon production data includes the following steps:

[0006] S1: Collect real-time power grid carbon emission intensity values ​​for the area where the target data center is located;

[0007] S2: Compare the real-time grid carbon emission intensity value with the preset carbon intensity threshold. When the threshold is exceeded, activate the accuracy degradation window and output the maximum accuracy degradation level.

[0008] S3: Based on the maximum accuracy downgrade level, perform accuracy downgrade processing on the original production data to generate downgraded data blocks;

[0009] S4: Perform indicator volatility analysis on the degraded data block. If the volatility exceeds the reliability threshold, initiate local accuracy recovery compensation.

[0010] S5: Integrate the downgraded data blocks and precision level labels after verification, and output a low-carbon optimized data packet.

[0011] Optionally, S1 specifically includes:

[0012] S11: Receive the location information of the target data center and call the geographic information parsing service to convert the location information into a standardized administrative region code for matching the regional index in the power grid carbon emission database;

[0013] S12: Based on standardized administrative region codes, retrieve the current regional power grid carbon emission intensity value from the power grid carbon emission database. The carbon emission intensity value represents the carbon emission amount corresponding to a unit of electricity consumption, and its unit is grams per kilowatt-hour.

[0014] S13: Perform timestamp verification on the received carbon emission intensity value. If the deviation between the collection time and the current time exceeds a preset threshold, request the latest value again.

[0015] S14: Use the carbon emission intensity value that passed the S13 verification as the real-time carbon emission intensity value of the current target data center.

[0016] Optionally, S13 specifically includes:

[0017] S131: Extract the timestamp attached to the received carbon emission intensity value. and get the current system time. ;

[0018] S132: Calculate the time difference between the timestamp and the current system time. Determine whether it exceeds the preset time deviation threshold. ;

[0019] If the time difference satisfies If the carbon emission intensity value fails, it will be considered invalid, triggering a new data request to the grid carbon emission database.

[0020] If the time difference satisfies If the received carbon emission intensity value is valid, then the received carbon emission intensity value will be marked as valid.

[0021] Optionally, S2 specifically includes:

[0022] S21: Set carbon intensity threshold This is used to define acceptable levels of carbon emissions from the power grid and to receive real-time carbon intensity values ​​acquired by S1, denoted as... ;

[0023] S22: Calculate the carbon emission intensity deviation value The formula is: ;

[0024] S23: When the judgment result satisfies When this occurs, the accuracy degradation window activation mechanism is triggered, based on the extent to which carbon emission intensity exceeds the standard. The range in question is determined by extracting the corresponding maximum precision degradation level from a pre-defined hierarchical control mapping table. .

[0025] Optionally, S3 specifically includes:

[0026] S31: Receive the maximum precision degradation level determined in S2, and obtain the corresponding data retention granularity parameter from the preset hierarchical control mapping table according to the corresponding level;

[0027] S32: For the raw production data set currently to be processed in the target data center, perform precision simplification processing on each data item according to the data retention granularity parameter, retaining it to the corresponding decimal or integer place, and generating downgraded data items;

[0028] S33: Combine all processed downgraded data items into a downgraded data block. And attach a precision level label.

[0029] Optionally, S4 specifically includes:

[0030] S41: Divide the downgraded data block generated by S3 into several consecutive data segments in chronological order, and extract the value range and mean of each data segment for each specified production indicator;

[0031] S42: Based on the preset reference standard, calculate the fluctuation range of each specified production indicator under the degraded state, and compare the fluctuation range with the corresponding reliability threshold.

[0032] S43: When the fluctuation range of one or more indicators exceeds its corresponding reliability threshold, it is judged as abnormal data distortion, and the local accuracy recovery compensation process is initiated.

[0033] S44: In the compensation process, the high-precision value of the corresponding position in the original data is called, and the abnormal fluctuation segment is repaired by data overlay according to the principle of minimum replacement, so as to generate a combined data block containing the compensated data segment and the unchanged data segment, and mark the compensation position and compensation range.

[0034] Optionally, S44 specifically includes:

[0035] S441: Determine the set of abnormal data segments in S43 that are judged to have fluctuation amplitudes exceeding the reliability threshold, and locate the index position of each abnormal segment in the degraded data block;

[0036] S442: For each anomalous segment, extract the high-precision data segment at the corresponding position from the original undegraded data block as candidate compensation data;

[0037] S443: Perform a difference analysis on each candidate compensation data and its corresponding downgrade segment. Following the principle of minimum replacement, only replace data points that exceed the change tolerance threshold. The difference determination formula is as follows: ,in, The first in the high-precision data segment One data point; For the corresponding data point in the downgraded data segment; The minimum effective change threshold is set.

[0038] S444: Concatenate the replaced data segment with the unchanged downgraded segment to form a combined data block. .

[0039] Optionally, S5 specifically includes:

[0040] S51: Receive the combined data block processed by S4 This includes compensated abnormal segments and unchanged downgraded segments, and performs integrity checks on the overall data structure, including data continuity, missing value detection, and data segment label verification.

[0041] S52: Read the maximum precision degradation level determined in S2 and bind it as tag information to the global metadata of the data block to form a tagged data structure;

[0042] S53: The integrated data structure is serialized and encapsulated according to a predetermined data format to construct a low-carbon optimized data packet, including the main data content, precision label field, compensation information field, timestamp field, and region identifier field.

[0043] A device for optimizing low-carbon production data, used to implement the aforementioned method for optimizing low-carbon production data, includes the following modules:

[0044] Carbon intensity acquisition module: used to acquire real-time power grid carbon emission intensity values ​​in the area where the target data center is located, associate the intensity values ​​with their corresponding timestamps and area identifiers, and output them to the carbon intensity determination module;

[0045] Carbon intensity determination module: This module receives the carbon emission intensity value output by the carbon intensity acquisition module and compares it with a preset carbon intensity threshold. When the carbon intensity exceeds the threshold, the precision control module is activated, and the maximum precision downgrade level is output.

[0046] Precision control module: Used to perform precision downgrading processing on the original production data according to the maximum precision downgrading level, generate downgraded data blocks, and output them to the fluctuation analysis module;

[0047] Fluctuation Analysis Module: This module receives degraded data blocks, performs volatility analysis on each specified production indicator, and determines whether there are any abnormal fluctuation segments that exceed the reliability threshold. If so, it calls the data compensation module to repair the data.

[0048] Data compensation module: Based on the anomaly judgment results of the fluctuation analysis module, it extracts the corresponding high-precision data segments from the original data, and performs data overlay repair on the abnormal fluctuation segments according to the principle of minimum replacement, generating combined data blocks;

[0049] Optimized data output module: Used to integrate and combine data blocks and their corresponding precision level labels to construct a structured, low-carbon optimized data package.

[0050] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for optimizing low-carbon production data.

[0051] The beneficial effects of this invention are:

[0052] This invention collects the carbon emission intensity value of the power grid in the area where the target data center is located in real time and compares it with a preset threshold. When the carbon intensity exceeds the threshold, a precision degradation mechanism is dynamically triggered. The maximum allowable precision level is determined according to the extent of the carbon intensity exceeding the threshold, and the production data is processed accordingly to reduce the carbon emissions caused by high-precision data processing during periods of high power grid carbon load.

[0053] This invention performs volatility analysis on data blocks. When the fluctuation of key indicators exceeds the reliability threshold, local accuracy recovery compensation is initiated. Only abnormal segments are replaced and repaired with minimal effort. Subsequently, the compensated data and accuracy tags are encapsulated together into a low-carbon optimized data package, achieving a balance between low-carbon goals and data reliability, and improving the dynamic adaptability of data centers in green computing scenarios. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of a method for optimizing low-carbon production data according to an embodiment of the present invention;

[0056] Figure 2 This is a schematic diagram of a device for optimizing low-carbon production data according to an embodiment of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0058] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0059] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1 As shown, a method for optimizing low-carbon production data includes the following steps:

[0061] S1: Collect real-time power grid carbon emission intensity values ​​for the area where the target data center is located;

[0062] S1 specifically includes:

[0063] S11: Receive the location information of the target data center and call the geographic information parsing service to convert the location information into a standardized administrative region code for matching the regional index in the power grid carbon emission database;

[0064] Table 1. Power Grid Carbon Emission Database

[0065]

[0066] In Table 1 above, the administrative region code adopts the national standard administrative division code (GB / T 2260) to ensure the consistency of regional matching and facilitate accurate coding through geographic information parsing in S11; the region name serves as a textual explanation of the code, facilitating manual review and system log output; the collection timestamp is used in the timestamp verification step in S13 to ensure a reliable basis for data real-time judgment; the carbon emission intensity value, in grams per kilowatt-hour (g / kWh), is directly used for carbon intensity threshold comparison in S2 and subsequent accuracy degradation judgment; the data source identifier records the data collection channel, facilitating weight allocation during source tracing and multi-source data fusion; the data validity flag is marked after timeliness verification and integrity detection, TRUE indicates that it can be directly used for accuracy control, while FALSE requires re-collection; and the update time is used for internal database maintenance and synchronization strategies to ensure traceability of data version management.

[0067] S12: Based on standardized administrative region codes, retrieve the current regional power grid carbon emission intensity value from the power grid carbon emission database. The carbon emission intensity value represents the amount of carbon emissions corresponding to a unit of electricity consumption, and its unit is grams per kilowatt-hour.

[0068] S13: Perform timestamp verification on the received carbon emission intensity value. If the deviation between the collection time and the current time exceeds a preset threshold, request the latest value again to ensure data real-time performance.

[0069] S14: The carbon emission intensity value verified in S13 is used as the real-time carbon emission intensity value of the current target data center and input into step S2 for subsequent processing. Through the above acquisition process, it is ensured that the obtained power grid carbon emission intensity value has spatial correspondence and temporal validity, which is conducive to improving the response accuracy and regional adaptability of the precision degradation control strategy.

[0070] S13 specifically includes:

[0071] S131: Extract the timestamp attached to the received carbon emission intensity value. and get the current system time. ;

[0072] S132: Calculate the time difference between the timestamp and the current system time. Determine whether it exceeds the preset time deviation threshold. The formula for calculating the time difference is: ;

[0073] If the time difference satisfies If the carbon emission intensity value fails, it will be considered invalid, triggering a new data request to the grid carbon emission database to obtain the latest value at the current moment.

[0074] If the time difference satisfies The received carbon emission intensity value is then marked as valid for subsequent carbon intensity comparison and precision control. Through the above timestamp verification mechanism, it can be ensured that the collected carbon emission intensity data has strict timeliness, thereby improving the real-time performance and judgment accuracy of the subsequent precision degradation trigger strategy, and enhancing the dynamic response capability of the entire low-carbon optimization method.

[0075] S2: Compare the real-time grid carbon emission intensity value with the preset carbon intensity threshold. When the threshold is exceeded, activate the accuracy degradation window and output the maximum accuracy degradation level.

[0076] S2 specifically includes:

[0077] S21: Set carbon intensity threshold This is used to define acceptable levels of carbon emissions from the power grid and to receive real-time carbon intensity values ​​acquired by S1, denoted as... ;

[0078] S22: Calculate the carbon emission intensity deviation value This is used to determine whether the current carbon intensity exceeds the standard. The formula is: ;

[0079] S23: When the judgment result satisfies When this occurs, the accuracy degradation window activation mechanism is triggered, based on the extent to which carbon emission intensity exceeds the standard. The range in question is determined by extracting the corresponding maximum precision degradation level from a pre-defined hierarchical control mapping table. The hierarchical control mapping table divides the carbon emission intensity deviation into multiple ranges, each range corresponding to a precision level. The precision level defines the minimum acceptable data precision granularity, which is then called upon in the subsequent step S3 for degradation processing. Through the above comparison and control strategies, the acceptable data precision level can be dynamically adjusted according to the real-time change in carbon emission intensity, realizing the production data precision regulation during peak grid carbon load periods, and providing clear parameter basis for subsequent data degradation and reliability control.

[0080] Table 2 Hierarchical Control Mapping Table

[0081]

[0082] In Table 2 above, the deviation interval number is used to uniquely identify the interval of each carbon emission intensity deviation, facilitating rapid indexing by the program logic; the carbon emission intensity deviation range corresponds to the range of deviation values ​​calculated in S22, used to determine the interval in which the current real-time carbon intensity is located; the corresponding maximum precision degradation level, the larger the value, the greater the degradation magnitude, and the lower the allowed data precision after degradation, which is directly called by step S23; the precision granularity description is used to clarify the specific data retention granularity under this precision degradation level, facilitating subsequent degradation processing in S3; the control priority, when multiple degradation strategies coexist, is used to indicate the execution order, the smaller the value, the higher the priority.

[0083] S3: Based on the maximum accuracy downgrade level, perform accuracy downgrade processing on the original production data to generate downgraded data blocks;

[0084] S3 specifically includes:

[0085] S31: Receive the maximum precision degradation level determined in S2, and obtain the corresponding data retention granularity parameter from the preset hierarchical control mapping table according to the corresponding level;

[0086] S32: For the raw production data set currently to be processed in the target data center, perform precision simplification processing on each data item according to the data retention granularity parameter, retaining it to the corresponding decimal or integer place, and generating downgraded data items;

[0087] S33: Combine all processed downgraded data items into a downgraded data block. The data is then labeled with a precision level for subsequent data volatility analysis and final output. Through the above precision downgrading process, the precision of the data can be reduced in a targeted manner when carbon emission intensity exceeds the standard, while retaining key trend information. This enables production data to adapt quickly to low-carbon strategies and lays a data foundation for subsequent reliability assessment and compensation mechanisms.

[0088] S4: Perform indicator volatility analysis on the degraded data block. If the volatility exceeds the reliability threshold, initiate local accuracy recovery compensation.

[0089] S4 specifically includes:

[0090] S41: Divide the downgraded data block generated by S3 into several consecutive data segments in chronological order, and extract the value range and mean of each data segment for each specified production indicator;

[0091] S42: Based on the preset reference standard, calculate the fluctuation range of each specified production indicator under the degraded state, and compare the fluctuation range with the corresponding reliability threshold.

[0092] The formula for calculating the fluctuation range is: ,in, The fluctuation range is used to measure the overall degree of fluctuation of the indicator under the downgraded state; This represents the number of consecutive data segments that are divided within the downgraded data block. For the first A set of specified production indicator values ​​within a data segment; For the first The maximum value of the indicator in each data segment; For the first The minimum value of the indicator in each data segment;

[0093] S43: When the fluctuation range of one or more indicators exceeds its corresponding reliability threshold, it is judged as abnormal data distortion, and the local accuracy recovery compensation process is initiated.

[0094] S44: In the compensation process, the high-precision value of the corresponding position in the original data is called, and the abnormal fluctuation segment is repaired by data overlay according to the principle of minimum replacement, so as to generate a combined data block containing the compensated data segment and the unchanged data segment, and the compensation position and compensation range are marked as the input of step S5; through the above fluctuation analysis and precision recovery compensation mechanism, dynamic evaluation and selective repair of the degraded data quality can be realized, ensuring that the low-carbon optimization data still has statistical reliability under the premise of meeting the carbon intensity limit, and providing stable support for data application.

[0095] S44 specifically includes:

[0096] S441: Determine the set of abnormal data segments in S43 that are judged to have fluctuation amplitudes exceeding the reliability threshold, and locate the index position of each abnormal segment in the degraded data block;

[0097] S442: For each anomalous segment, extract the high-precision data segment at the corresponding position from the original undegraded data block as candidate compensation data;

[0098] S443: Perform a difference analysis on each candidate compensation data and its corresponding downgrade segment. Following the principle of minimum replacement, only replace data points that exceed the change tolerance threshold. The difference determination formula is as follows: ,in, The first in the high-precision data segment One data point; For the corresponding data point in the downgraded data segment; The minimum effective change threshold is set.

[0099] S444: Concatenate the replaced data segment with the unchanged downgraded segment to form a combined data block. The start and end indices and replacement ratio of each compensated segment are marked for subsequent data tracing and verification. Through the compensation mechanism based on the minimum replacement principle, as much original degraded data as possible can be retained to reduce carbon emission load, while ensuring that the accuracy of key data in abnormal segments can be restored, thereby achieving a dynamic balance between reliability and low-carbon goals.

[0100] S5: Integrate the downgraded data blocks and precision level labels after verification, and output a low-carbon optimized data packet;

[0101] S5 specifically includes:

[0102] S51: Receive the combined data block processed by S4 This includes compensated abnormal segments and unchanged downgraded segments, and performs integrity checks on the overall data structure, including data continuity, missing value detection, and data segment label verification.

[0103] S52: Read the maximum precision degradation level determined in S2 and bind it as tag information to the global metadata of the data block to form a tagged data structure;

[0104] S53: The integrated data structure is serialized and encapsulated according to a predetermined data format to construct a low-carbon optimized data package, including the main data content, precision label field, compensation information field, timestamp field, and region identifier field. Through the above data integration and structure encapsulation process, it can be ensured that the degraded data and compensation information are effectively associated and have a complete labeling system, providing a structured output basis for data control and subsequent analysis in the low-carbon production process.

[0105] like Figure 2 As shown, a low-carbon production data optimization device, used to implement the aforementioned low-carbon production data optimization method, includes the following modules:

[0106] Carbon intensity acquisition module: used to collect real-time power grid carbon emission intensity values ​​of the target data center area, associate the intensity values ​​with their corresponding timestamps and area identifiers, and output them to the carbon intensity determination module;

[0107] Carbon intensity determination module: This module receives the carbon emission intensity value output by the carbon intensity acquisition module and compares it with a preset carbon intensity threshold. When the carbon intensity exceeds the threshold, the precision control module is activated, and the maximum precision downgrade level is output.

[0108] Precision control module: Used to perform precision downgrading processing on the original production data according to the maximum precision downgrading level, generate downgraded data blocks, and output them to the fluctuation analysis module;

[0109] Fluctuation Analysis Module: This module receives degraded data blocks, performs volatility analysis on each specified production indicator, and determines whether there are any abnormal fluctuation segments that exceed the reliability threshold. If so, it calls the data compensation module to repair the data.

[0110] Data compensation module: Based on the anomaly judgment results of the fluctuation analysis module, it extracts the corresponding high-precision data segments from the original data, and performs data overlay repair on the abnormal fluctuation segments according to the principle of minimum replacement, generating combined data blocks;

[0111] Optimized data output module: Used to integrate and combine data blocks and their corresponding precision level labels to construct a structured, low-carbon optimized data package.

[0112] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for optimizing low-carbon production data.

[0113] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0114] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing low-carbon production data, characterized in that, Includes the following steps: S1: Collect real-time power grid carbon emission intensity values ​​for the area where the target data center is located; S2: Compare the real-time grid carbon emission intensity value with the preset carbon intensity threshold. When the threshold is exceeded, activate the accuracy degradation window and output the maximum accuracy degradation level. S3: Based on the maximum accuracy downgrade level, perform accuracy downgrade processing on the original production data to generate downgraded data blocks; S4: Perform indicator volatility analysis on the degraded data block. If the volatility exceeds the reliability threshold, initiate local accuracy recovery compensation. S4 specifically includes: S41: Divide the downgraded data block generated by S3 into several consecutive data segments in chronological order, and extract the value range and mean of each data segment for each specified production indicator; S42: Based on the preset reference standard, calculate the fluctuation range of each specified production indicator under the degraded state, and compare the fluctuation range with the corresponding reliability threshold. S43: When the fluctuation range of one or more indicators exceeds its corresponding reliability threshold, it is judged as abnormal data distortion, and the local accuracy recovery compensation process is initiated. S44: In the compensation process, the high-precision value of the corresponding position in the original data is called, and the abnormal fluctuation segment is repaired by data overlay according to the principle of minimum replacement, so as to generate a combined data block containing the compensated data segment and the unchanged data segment, and mark the compensation position and compensation range. S44 specifically includes: S441: Determine the set of abnormal data segments in S43 that are judged to have fluctuation amplitudes exceeding the reliability threshold, and locate the index position of each abnormal segment in the degraded data block; S442: For each anomalous segment, extract the high-precision data segment at the corresponding position from the original undegraded data block as candidate compensation data; S443: Perform a difference analysis on each candidate compensation data and its corresponding downgrade segment. Following the principle of minimum replacement, only replace data points that exceed the change tolerance threshold. The difference determination formula is as follows: in, This refers to the j-th data point in the high-precision data segment. For the corresponding data point in the downgraded data segment; ε is the set minimum effective change threshold; S444: Concatenate the replaced data segment with the unchanged downgraded segment to form a combined data block D″; S5: Integrate the downgraded data blocks and precision level labels after verification, and output a low-carbon optimized data packet.

2. The method for optimizing low-carbon production data according to claim 1, characterized in that, S1 specifically includes: S11: Receive the location information of the target data center and call the geographic information parsing service to convert the location information into a standardized administrative region code for matching the regional index in the power grid carbon emission database; S12: Based on standardized administrative region codes, retrieve the current regional power grid carbon emission intensity value from the power grid carbon emission database. The carbon emission intensity value represents the carbon emission amount corresponding to a unit of electricity consumption, and its unit is grams per kilowatt-hour. S13: Perform timestamp verification on the received carbon emission intensity value. If the deviation between the collection time and the current time exceeds a preset threshold, request the latest value again. S14: Use the carbon emission intensity value that passed the S13 verification as the real-time carbon emission intensity value of the current target data center.

3. The method for optimizing low-carbon production data according to claim 2, characterized in that, S13 specifically includes: S131: Extract the timestamp T attached to the received carbon emission intensity value. s And obtain the current system time T c ; S132: Calculate the time difference ΔT between the timestamp and the current system time, and determine whether it exceeds the preset time deviation threshold T. th ; If the time difference satisfies ΔT>T th If the carbon emission intensity value fails, it will be considered invalid, triggering a new data request to the grid carbon emission database. If the time difference satisfies ΔT≤T th If the received carbon emission intensity value is valid, then the received carbon emission intensity value will be marked as valid.

4. The method for optimizing low-carbon production data according to claim 1, characterized in that, S2 specifically includes: S21: Set the carbon intensity threshold C th This is used to define acceptable levels of carbon emissions from the power grid and to receive real-time carbon intensity values ​​obtained by S1, denoted as C. r ; S22: Calculate the carbon emission intensity deviation value ΔC using the formula: ΔC = C r -C th ; S23: When the judgment result satisfies ΔC>0, the precision degradation window activation mechanism is triggered. Based on the range in which the carbon emission intensity exceeds the standard by ΔC, the corresponding maximum precision degradation level L is extracted from the preset graded control mapping table. max .

5. The method for optimizing low-carbon production data according to claim 1, characterized in that, S3 specifically includes: S31: Receive the maximum precision degradation level determined in S2, and obtain the corresponding data retention granularity parameter from the preset hierarchical control mapping table according to the corresponding level; S32: For the raw production data set currently to be processed in the target data center, perform precision simplification processing on each data item according to the data retention granularity parameter, retaining it to the corresponding decimal or integer place, and generating downgraded data items; S33: Combine all processed degraded data items into a degraded data block D′ and attach a precision level label.

6. The method for optimizing low-carbon production data according to claim 1, characterized in that, S5 specifically includes: S51: Receive the combined data block D″ processed by S4, including the compensated abnormal segment and the unchanged degraded segment, and perform integrity verification on its overall data structure, including data continuity, missing value detection and data segment mark verification. S52: Read the maximum precision degradation level determined in S2 and bind it as tag information to the global metadata of the data block to form a tagged data structure; S53: The integrated data structure is serialized and encapsulated according to a predetermined data format to construct a low-carbon optimized data packet, including the main data content, precision label field, compensation information field, timestamp field, and region identifier field.

7. A device for optimizing low-carbon production data, used to implement a method for optimizing low-carbon production data as described in any one of claims 1-6, characterized in that, Includes the following modules: Carbon intensity acquisition module: used to acquire real-time power grid carbon emission intensity values ​​in the area where the target data center is located, associate the intensity values ​​with their corresponding timestamps and area identifiers, and output them to the carbon intensity determination module; Carbon intensity determination module: This module receives the carbon emission intensity value output by the carbon intensity acquisition module and compares it with a preset carbon intensity threshold. When the carbon intensity exceeds the threshold, the precision control module is activated, and the maximum precision downgrade level is output. Precision control module: Used to perform precision downgrading processing on the original production data according to the maximum precision downgrading level, generate downgraded data blocks, and output them to the fluctuation analysis module; Fluctuation Analysis Module: This module receives degraded data blocks, performs volatility analysis on each specified production indicator, and determines whether there are any abnormal fluctuation segments that exceed the reliability threshold. If so, it calls the data compensation module to repair the data. Data compensation module: Based on the anomaly judgment results of the fluctuation analysis module, it extracts the corresponding high-precision data segments from the original data, and performs data overlay repair on the abnormal fluctuation segments according to the principle of minimum replacement, generating combined data blocks; Optimized data output module: Used to integrate and combine data blocks and their corresponding precision level labels to construct a structured, low-carbon optimized data package.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for optimizing low-carbon production data according to any one of claims 1 to 6.

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