Optimization method of low-carbon production data, equipment and medium

Abstract

The invention relates to the technical field of data processing, in particular to a low-carbon production data optimization method and device and a medium, and the method comprises the following steps: S1, collecting a real-time power grid carbon emission intensity value of an area where a target data center is located; s2, the real-time power grid carbon emission intensity value is compared with a preset carbon intensity threshold value, and when the real-time power grid carbon emission intensity value exceeds the threshold value, the maximum precision degradation level is output; s3, performing precision degradation processing on the original production data to generate degradation data blocks; s4, if the fluctuation amplitude exceeds the reliability threshold value, local precision recovery compensation is started; and S5, integrating the verified degraded data block and the precision level label, and outputting a low-carbon optimized data packet. According to the invention, by combining real-time monitoring and dynamic precision regulation and control of the power grid carbon emission intensity, low-carbon optimization processing which ensures the reliability of key production data while reducing carbon emission is realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and medium for optimizing low-carbon production data. Background Art

[0002] As an important part of high-energy-consuming industries, the indirect carbon emissions generated during the operation of data centers have gradually become a focus of attention. The carbon emission intensity of the power grid will fluctuate with time and changes in the regional energy structure. When the carbon emission intensity is high, the continuous high-precision computing and storage of data centers will lead to a large amount of high-carbon electricity consumption, increasing the overall carbon footprint. In the context of green computing power and low-carbon operations, how to combine the real-time changes in the carbon intensity of the power grid and dynamically adjust the accuracy strategy of production data to achieve an operating mode that meets both business continuity and low-carbon goals has become an important direction for energy conservation and carbon reduction in data centers.

[0003] Existing technologies often rely on fixed strategies to control production data accuracy, failing to dynamically correlate with real-time grid carbon emission intensity. This makes it impossible to quickly reduce data accuracy during peak carbon intensity periods to reduce carbon load. Furthermore, there is a lack of mechanisms to detect and selectively restore the reliability of key indicators after degradation. This results in either excessively high data accuracy, increasing carbon emissions, or excessively low accuracy, impacting business reliability, in low-carbon operation scenarios. There is a lack of solutions to dynamically balance the two. Therefore, there is an urgent need for a low-carbon production data optimization method, equipment, and media to address these issues. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a method, device and medium for optimizing low-carbon production data.

[0005] A method for optimizing low-carbon production data comprises the following steps: S1: Collect the real-time carbon emission intensity value of the power grid in 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: Perform precision degradation processing on the original production data according to the maximum precision degradation level to generate a degraded data block; S4: Perform indicator volatility analysis on the degraded data block. If the fluctuation exceeds the reliability threshold, initiate local accuracy recovery compensation. S5: Integrate the verified degraded data blocks and precision level labels, and output low-carbon optimized data packets.

[0006] Optionally, the 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 the standardized administrative region code, 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 emissions per 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: The carbon emission intensity value verified in S13 is used as the real-time carbon emission intensity value of the current target data center.

[0007] Optionally, the S13 specifically includes: S131: Extract the timestamp attached to the received carbon emission intensity value , and get the current system time ; S132: Calculate the time difference between the timestamp and the current system time , to determine whether it exceeds the preset time deviation threshold ; If the time difference satisfies , the carbon emission intensity value is deemed invalid, and triggers a new data request to the power grid carbon emission database; If the time difference satisfies , then the received carbon emission intensity value is marked as valid.

[0008] Optionally, the S2 specifically includes: S21: Setting carbon intensity thresholds , which is used to define the acceptable carbon emission level of the power grid and receive the real-time carbon emission intensity value obtained by S1, recorded as ; S22: Calculate carbon emission intensity deviation value , the formula is: ; S23: When the judgment result satisfies When the precision degradation window activation mechanism is triggered, the precision degradation window activation mechanism is triggered according to the extent of carbon emission intensity exceeding the standard. The interval in which it is located extracts the corresponding maximum precision degradation level from the preset hierarchical control mapping table .

[0009] Optionally, the S3 specifically includes: S31: receiving the maximum precision degradation level determined in S2, and obtaining a corresponding data retention granularity parameter from a preset hierarchical control mapping table according to the corresponding level; S32: For the original 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, retain the data to the corresponding decimal place or integer place, and generate a degraded data item; S33: Combining all processed degraded data items to form a degraded data block , and append the accuracy level label.

[0010] Optionally, the S4 specifically includes: S41: Divide the degraded data block generated in S3 into several continuous data segments in chronological order, and extract the value range and mean value in each data segment for each specified production indicator; S42: Calculate the fluctuation range of each designated production indicator in a degraded state according to a preset reference standard, and compare the fluctuation range with a corresponding reliability threshold; S43: When the fluctuation range of one or more indicators exceeds the corresponding reliability threshold, it is determined that the data is abnormally distorted, 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 data of the abnormal fluctuation segment is overwritten and repaired according to the minimum replacement principle 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.

[0011] Optionally, the S44 specifically includes: S441: Determine a set of abnormal data segments whose fluctuation amplitudes exceed the reliability threshold determined in S43, and locate the index position of each abnormal segment in the degraded data block; S442: For each abnormal segment, extract a high-precision data segment at a corresponding position from the original non-degraded data block as candidate compensation data; S443: Perform a difference analysis on each candidate compensation data and its corresponding degraded segment. According to the minimum replacement principle, only the data points exceeding the change tolerance threshold are replaced. The difference determination formula is: ,in, The first data points; is the corresponding data point in the degraded data segment; is the minimum effective change threshold set; S444: Splice the replaced data segment with the unchanged degraded segment to form a combined data block .

[0012] Optionally, the S5 specifically includes: S51: receiving the combined data block processed by S4 , including the compensated abnormal segments and unchanged degraded segments, and perform integrity verification on the overall data structure, including data continuity, missing value detection and data segment tag verification; S52: Read the maximum precision degradation level determined in S2, and bind it as label information to the global metadata of the data block to form a labeled data structure; S53: Serialize and encapsulate the integrated data structure according to a predetermined data format to construct a low-carbon optimized data packet, including a data content body, a precision tag field, a compensation information field, a timestamp field, and a region identification field.

[0013] A low-carbon production data optimization device, used to implement the above-mentioned low-carbon production data optimization method, includes the following modules: Carbon intensity acquisition module: used to collect the real-time carbon emission intensity value of the power grid in the area where the target data center is located, associate the intensity value with its corresponding timestamp and area identifier, and output it to the carbon intensity determination module; Carbon intensity determination module: used to receive the carbon emission intensity value output by the carbon intensity acquisition module and compare it with the preset carbon intensity threshold; when the carbon intensity exceeds the threshold, the accuracy control module is activated and the maximum accuracy degradation level is output; Precision control module: used to perform precision degradation processing on the original production data according to the maximum precision degradation level, generate degradation data blocks, and output them to the fluctuation analysis module; Fluctuation analysis module: used to receive degraded data blocks, perform fluctuation analysis on each specified production indicator, and determine whether there is an abnormal fluctuation segment exceeding the reliability threshold. If so, the data compensation module is called to repair it; Data compensation module: It is used to extract the corresponding high-precision data segments from the original data according to the abnormal judgment results of the fluctuation analysis module, and perform data overwriting and repair on the abnormal fluctuation segments according to the minimum replacement principle to generate combined data blocks; Optimization data output module: used to integrate combined data blocks and corresponding precision level labels to build structured low-carbon optimization data packets.

[0014] A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for optimizing low-carbon production data.

[0015] Beneficial effects of the present invention: The present 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, the accuracy degradation mechanism is dynamically triggered. The maximum allowable accuracy level is determined according to the extent of the carbon intensity exceeding the threshold, and the production data is subjected to corresponding accuracy degradation processing, thereby effectively reducing the carbon emissions caused by high-precision data processing during periods of high carbon load on the power grid.

[0016] The present invention performs volatility analysis on data blocks. When it detects that the fluctuation amplitude of key indicators exceeds the reliability threshold, it starts local precision recovery compensation and performs minimum replacement repair on only the abnormal segment. The compensated data and precision label are then packaged into a low-carbon optimized data packet, achieving a balance between low-carbon goals and data reliability, and improving the dynamic adaptability of data centers in green computing power scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a method for optimizing low-carbon production data according to an embodiment of the present invention; Figure 2 Schematic diagram of the low-carbon production data optimization equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1 As shown, a method for optimizing low-carbon production data includes the following steps: S1: Collect the real-time carbon emission intensity value of the power grid in the area where the target data center is located; 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; Table 1 Power grid carbon emission database

[0023] 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 analysis in S11; the region name, as a textual explanation of the code, is convenient for manual review and system log output; the collection timestamp is used in the timestamp verification step of S13 to ensure a reliable basis for real-time data judgment; the carbon emission intensity value, in grams per kilowatt-hour (g / kWh), is directly used for S2 carbon intensity threshold comparison and subsequent accuracy degradation judgment; the data source identifier is used to record the collection channel of the data to facilitate traceability and weight allocation during 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 triggering re-collection; the update time is used for database maintenance and synchronization strategies within the system to ensure traceability of data version management.

[0024] S12: Based on the standardized administrative region code, the current regional power grid carbon emission intensity value is retrieved from the power grid carbon emission database. The carbon emission intensity value represents the carbon emissions corresponding to unit 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 to ensure the real-time nature of the data. S14: The carbon emission intensity value verified by S13 is used as the real-time carbon emission intensity value of the current target data center, and is input into step S2 for subsequent processing; through the above-mentioned collection 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.

[0025] S13 specifically includes: S131: Extract the timestamp attached to the received carbon emission intensity value , and get the current system time ; S132: Calculate the time difference between the timestamp and the current system time , to determine whether it exceeds the preset time deviation threshold , the time difference is calculated as: ; If the time difference satisfies , the carbon emission intensity value is deemed invalid, and triggers a new data request to the power grid carbon emission database to obtain the latest value at the current moment; If the time difference satisfies , the received carbon emission intensity value will be marked as valid for subsequent carbon intensity comparison and precision control; through the above-mentioned timestamp verification mechanism, the collected carbon emission intensity data can be ensured to have strict timeliness, thereby improving the real-time performance and judgment accuracy of the subsequent precision degradation triggering strategy, and enhancing the dynamic response capability of the entire low-carbon optimization method.

[0026] 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. S2 specifically includes: S21: Setting carbon intensity thresholds , which is used to define the acceptable carbon emission level of the power grid and receive the real-time carbon emission intensity value obtained by S1, recorded as ; S22: Calculate carbon emission intensity deviation value , used to determine whether the current carbon intensity exceeds the standard, the formula is: ; S23: When the judgment result satisfies When the precision degradation window activation mechanism is triggered, the precision degradation window activation mechanism is triggered according to the extent of carbon emission intensity exceeding the standard. The interval in which it is located extracts the corresponding maximum precision degradation level from the preset hierarchical control mapping table ; The hierarchical control mapping table divides the carbon emission intensity deviation into multiple ranges, each range corresponds to an accuracy level, and the accuracy level defines the minimum granularity of acceptable data accuracy for the subsequent step S3 to perform downgrade processing; through the above comparison and control strategy, the acceptable data accuracy level can be dynamically adjusted according to the change range of real-time carbon emission intensity, and the production data accuracy control during the peak period of carbon load of the power grid can be realized, providing a clear parameter basis for subsequent data degradation and reliability control.

[0027] Table 2 Hierarchical control mapping table

[0028] In Table 2 above, the deviation interval number is used to uniquely identify the interval of each carbon emission intensity deviation, which is convenient for quick indexing by program logic; the carbon emission intensity deviation range corresponds to the deviation value range calculated by S22, which is used to determine the interval in which the current real-time carbon intensity is located; it corresponds to the maximum precision degradation level, and the larger the value, the greater the degradation, and the lower the data accuracy allowed after degradation, which is directly called by step S23; the precision granularity description is used to clarify the specific data retention granularity under the precision degradation level, which is convenient for subsequent S3 to implement degradation processing; the control priority is used to indicate the execution order when multiple degradation strategies coexist, and the smaller the value, the higher the priority.

[0029] S3: Perform precision degradation processing on the original production data according to the maximum precision degradation level to generate a degraded data block; S3 specifically includes: S31: receiving the maximum precision degradation level determined in S2, and obtaining a corresponding data retention granularity parameter from a preset hierarchical control mapping table according to the corresponding level; S32: For the original 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, retain the data to the corresponding decimal place or integer place, and generate a degraded data item; S33: Combining all processed degraded data items to form a degraded data block , and attach precision level labels for subsequent data volatility analysis and final output; through the above precision degradation processing steps, data precision can be targetedly reduced in situations where carbon emission intensity exceeds the standard, while retaining key trend information, enabling production data to quickly adapt to low-carbon strategies, and laying a data foundation for subsequent reliability assessment and compensation mechanisms.

[0030] S4: Perform indicator volatility analysis on the degraded data block. If the fluctuation exceeds the reliability threshold, initiate local accuracy recovery compensation. S4 specifically includes: S41: Divide the degraded data block generated in S3 into several continuous data segments in chronological order, and extract the value range and mean value in each data segment for each specified production indicator; S42: Calculate the fluctuation range of each designated production indicator in a degraded state according to a preset reference standard, and compare the fluctuation range with a corresponding reliability threshold; The formula for calculating the fluctuation range is: ,in, The volatility is used to measure the overall volatility of the indicator in the downgraded state; The number of continuous data segments divided in the degraded data block; For the A set of specified production indicator values ​​within a data segment; For the The maximum value of the indicator in the data segment; For the The minimum value of the indicator in the data segment; S43: When the fluctuation range of one or more indicators exceeds the corresponding reliability threshold, it is determined that the data is abnormally distorted, 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 data of the abnormal fluctuation segment is overwritten and repaired according to the minimum replacement principle 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-mentioned volatility analysis and accuracy recovery compensation mechanism, dynamic evaluation and selective repair of the degraded data quality can be achieved, ensuring that the low-carbon optimization data still has statistical reliability under the premise of meeting the carbon intensity limit, providing stable support for data application.

[0031] S44 specifically includes: S441: Determine a set of abnormal data segments whose fluctuation amplitudes exceed the reliability threshold determined in S43, and locate the index position of each abnormal segment in the degraded data block; S442: For each abnormal segment, extract a high-precision data segment at a corresponding position from the original non-degraded data block as candidate compensation data; S443: Perform a difference analysis on each candidate compensation data and its corresponding degraded segment. According to the minimum replacement principle, only the data points exceeding the change tolerance threshold are replaced. The difference determination formula is: ,in, The first data points; is the corresponding data point in the degraded data segment; is the minimum effective change threshold set; S444: Splice the replaced data segment with the unchanged degraded segment to form a combined data block , and mark the start and end indexes and replacement ratio of each compensated segment for subsequent data traceability and verification; through the compensation mechanism of the minimum replacement principle mentioned above, it can not only retain as much original degraded data as possible to reduce carbon emission load, but also ensure that the accuracy of key data in abnormal segments can be restored, thereby achieving a dynamic balance between reliability and low-carbon goals.

[0032] S5: Integrate the verified degraded data blocks and precision level labels, and output low-carbon optimized data packets; S5 specifically includes: S51: receiving the combined data block processed by S4 , including the compensated abnormal segments and unchanged degraded segments, and perform integrity verification on the overall data structure, including data continuity, missing value detection and data segment tag verification; S52: Read the maximum precision degradation level determined in S2, and bind it as label information to the global metadata of the data block to form a labeled data structure; S53: Serialize and encapsulate the integrated data structure according to the predetermined data format to construct a low-carbon optimized data packet, including the data content body, precision label field, compensation information field, timestamp field and region identification field; through the above data integration and structure encapsulation process, it can ensure that the degraded data and compensation information are effectively associated, and have a complete label system, providing a structured output basis for data regulation and subsequent analysis in the low-carbon production process.

[0033] like Figure 2 As shown, a low-carbon production data optimization device is used to implement the above-mentioned low-carbon production data optimization method, including the following modules: Carbon intensity collection module: used to collect the real-time carbon emission intensity value of the power grid in the area where the target data center is located, associate the intensity value with its corresponding timestamp and area identifier, and output it to the carbon intensity determination module; Carbon intensity determination module: used to receive the carbon emission intensity value output by the carbon intensity acquisition module and compare it with the preset carbon intensity threshold; when the carbon intensity exceeds the threshold, the accuracy control module is activated and the maximum accuracy degradation level is output; Precision control module: used to perform precision degradation processing on the original production data according to the maximum precision degradation level, generate degradation data blocks, and output them to the fluctuation analysis module; Fluctuation analysis module: used to receive degraded data blocks, perform fluctuation analysis on each specified production indicator, and determine whether there is an abnormal fluctuation segment exceeding the reliability threshold. If so, the data compensation module is called to repair it; Data compensation module: It is used to extract the corresponding high-precision data segments from the original data according to the abnormal judgment results of the fluctuation analysis module, and perform data overwriting and repair on the abnormal fluctuation segments according to the minimum replacement principle to generate combined data blocks; Optimization data output module: used to integrate combined data blocks and corresponding precision level labels to build structured low-carbon optimization data packets.

[0034] A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for optimizing low-carbon production data.

[0035] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0036] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for optimizing low-carbon production data, characterized in that: The following steps are involved: S1: Collect the real-time carbon emission intensity value of the power grid in 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: Perform precision degradation processing on the original production data according to the maximum precision degradation level to generate a degraded data block; S4: Perform indicator volatility analysis on the degraded data block. If the fluctuation exceeds the reliability threshold, initiate local accuracy recovery compensation. S5: Integrate the verified degraded data blocks and precision level labels, and output low-carbon optimized data packets.

2. The method for optimizing low-carbon production data according to claim 1, characterized in that: Said 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 the standardized administrative region code, 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 emissions per 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: The carbon emission intensity value verified in S13 is used 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: The S13 specifically includes: S131: Extract the timestamp attached to the received carbon emission intensity value , and get the current system time ; S132: Calculate the time difference between the timestamp and the current system time , to determine whether it exceeds the preset time deviation threshold ; If the time difference satisfies , the carbon emission intensity value is deemed invalid, and triggers a new data request to the power grid carbon emission database; If the time difference satisfies , then the received carbon emission intensity value is marked as valid.

4. The method for optimizing low-carbon production data according to claim 1, characterized in that: The S2 specifically includes: S21: Setting carbon intensity thresholds , which is used to define the acceptable carbon emission level of the power grid and receive the real-time carbon emission intensity value obtained by S1, recorded as ; S22: Calculate carbon emission intensity deviation value , the formula is: ; S23: When the judgment result satisfies When the precision degradation window activation mechanism is triggered, the precision degradation window activation mechanism is triggered according to the extent of carbon emission intensity exceeding the standard. The interval in which it is located extracts the corresponding maximum precision degradation level from the preset hierarchical control mapping table .

5. The method for optimizing low-carbon production data according to claim 1, characterized in that: The S3 specifically includes: S31: receiving the maximum precision degradation level determined in S2, and obtaining a corresponding data retention granularity parameter from a preset hierarchical control mapping table according to the corresponding level; S32: For the original 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, retain the data to the corresponding decimal place or integer place, and generate a degraded data item; S33: Combining all processed degraded data items to form a degraded data block , and append the accuracy level label.

6. The method for optimizing low-carbon production data according to claim 1, characterized in that: The S4 specifically includes: S41: Divide the degraded data block generated in S3 into several continuous data segments in chronological order, and extract the value range and mean value in each data segment for each specified production indicator; S42: Calculate the fluctuation range of each designated production indicator in a degraded state according to a preset reference standard, and compare the fluctuation range with a corresponding reliability threshold; S43: When the fluctuation range of one or more indicators exceeds the corresponding reliability threshold, it is determined that the data is abnormally distorted, 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 data of the abnormal fluctuation segment is overwritten and repaired according to the minimum replacement principle 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.

7. The method for optimizing low-carbon production data according to claim 6, characterized in that: The S44 specifically includes: S441: Determine a set of abnormal data segments whose fluctuation amplitudes exceed the reliability threshold determined in S43, and locate the index position of each abnormal segment in the degraded data block; S442: For each abnormal segment, extract a high-precision data segment at a corresponding position from the original non-degraded data block as candidate compensation data; S443: Perform a difference analysis on each candidate compensation data and its corresponding degraded segment. According to the minimum replacement principle, only the data points exceeding the change tolerance threshold are replaced. The difference determination formula is: ,in, The first data points; is the corresponding data point in the degraded data segment; is the minimum effective change threshold set; S444: Splice the replaced data segment with the unchanged degraded segment to form a combined data block .

8. The method for optimizing low-carbon production data according to claim 7, characterized in that: The S5 specifically includes: S51: receiving the combined data block processed by S4 , including the compensated abnormal segments and unchanged degraded segments, and perform integrity verification on the overall data structure, including data continuity, missing value detection and data segment tag verification; S52: Read the maximum precision degradation level determined in S2, and bind it as label information to the global metadata of the data block to form a labeled data structure; S53: Serialize and encapsulate the integrated data structure according to a predetermined data format to construct a low-carbon optimized data packet, including a data content body, a precision tag field, a compensation information field, a timestamp field, and a region identification field.

9. A low-carbon production data optimization device, used to implement a low-carbon production data optimization method according to any one of claims 1 to 8, characterized in that: Includes the following modules: Carbon intensity acquisition module: used to collect the real-time carbon emission intensity value of the power grid in the area where the target data center is located, associate the intensity value with its corresponding timestamp and area identifier, and output it to the carbon intensity determination module; Carbon intensity determination module: used to receive the carbon emission intensity value output by the carbon intensity acquisition module and compare it with the preset carbon intensity threshold; when the carbon intensity exceeds the threshold, the accuracy control module is activated and the maximum accuracy degradation level is output; Precision control module: used to perform precision degradation processing on the original production data according to the maximum precision degradation level, generate degradation data blocks, and output them to the fluctuation analysis module; Fluctuation analysis module: used to receive degraded data blocks, perform fluctuation analysis on each specified production indicator, and determine whether there is an abnormal fluctuation segment exceeding the reliability threshold. If so, the data compensation module is called to repair it; Data compensation module: It is used to extract the corresponding high-precision data segments from the original data according to the abnormal judgment results of the fluctuation analysis module, and perform data overwriting and repair on the abnormal fluctuation segments according to the minimum replacement principle to generate combined data blocks; Optimization data output module: used to integrate combined data blocks and corresponding precision level labels to build structured low-carbon optimization data packets.

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

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