Automatic quality control system for long-term positioning observation data of ecological system

Abstract

The invention relates to the technical field of ecological data processing, in particular to an ecological system long-term positioning observation data automatic quality control system which comprises a data acquisition module, an inspection module, a processing module, an adjustment module and a mark output module. By means of automatic data processing, data quality control is remarkably improved, the requirement for manual intervention is reduced, the system can adapt to data processing requirements in different scenes through a flexible processing mode and a dynamic adjustment mechanism, it is ensured that data can still be accurately processed in a complex environment, and particularly, abnormal data is finely processed, so that the data processing efficiency is improved. The method helps to improve the quality and reliability of observation data of an ecological system, enables the system to automatically optimize the resource distribution and processing sequence through a priority strategy adjustment function, and effectively solves the problems that the data quality is poor and decision support is not timely enough due to frequent manual intervention and insufficient anomaly detection capability.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological data processing, and particularly to an automated quality control system for long-term fixed-point observation data of an ecosystem. Background Art

[0002] With the increasing impact of global climate change and human activities on the natural environment, the stability and sustainability of ecosystems face severe challenges, and the demand for long-term monitoring of the ecological environment by scientific research and management departments is continuously increasing. In order to accurately grasp the dynamic changes of ecosystems and ensure the reliability and scientific nature of observation data, an automated and intelligent quality control system has emerged to improve data processing efficiency, ensure the accuracy and consistency of ecological observation data, and thus provide strong support for ecological protection and environmental management.

[0003] The patent document with the Chinese patent application publication number CN118469338A discloses a method and system for determining anomalies in ecological environment detection data. The method includes the following steps: obtaining detection data from an ecological environment laboratory information management system; evaluating the detection data through a percentile value analysis table of pollutant historical data, and giving an early warning when the detection data is in the boundary interval; performing a rationality analysis on the detection data to determine whether the detection data is abnormal; and determining whether the detection data is abnormal according to the standard limit value in the detection standard.

[0004] It can be seen that the method for determining data anomalies in this method requires manual rationality analysis and standard limit value checking, resulting in slow response speed and the necessity of manual intervention; its early warning mechanism only gives an early warning when the data is in the boundary interval, resulting in some potential abnormal data being missed and comprehensive monitoring not being achieved; this method does not clearly describe the dynamic adjustment of data processing strategies, and only evaluates according to historical data, lacking flexibility; its marking method is relatively simple, mainly focusing on the identification of abnormal data, and cannot provide rich quality control information. Summary of the Invention

[0005] Therefore, the present invention provides an automated quality control system for long-term fixed-point observation data of an ecosystem to overcome the problems of poor data quality and untimely decision support caused by frequent manual intervention and insufficient anomaly detection ability in the prior art.

[0006] To achieve the above object, the present invention provides an automated quality control system for long-term fixed-point observation data of an ecosystem, including:

[0007] A data acquisition module for obtaining observation data of an ecological station, where the observation data includes moisture data, soil data, biological data, and meteorological data;

[0008] An inspection module, which is connected to the data acquisition module, is used to perform screening processing according to the observed data and the corresponding preset standard range to determine normal data and abnormal data;

[0009] A processing module, which is connected to the inspection module, is used to determine the processing modes and processing priorities of the normal data and the abnormal data in turn according to the preset priority processing strategy, and to determine a number of parallel data and a number of asynchronous data according to the corresponding processing modes, the processing priorities and the processing speeds, wherein the processing modes include a parallel processing mode and an asynchronous processing mode, and the processing speeds include a preset parallel processing speed and a preset asynchronous processing speed;

[0010] An adjustment module, which is connected to the processing module, includes a speed adjustment unit for obtaining a number of corrected parallel data and a number of corrected asynchronous data based on a corrected parallel processing speed and a corrected asynchronous processing speed, and a priority strategy adjustment unit for adjusting the preset priority processing strategy according to a secondary response duration; wherein the speed adjustment unit determines the corrected parallel processing speed and the corrected asynchronous processing speed based on the corresponding primary response durations obtained from each of the asynchronous data and each of the parallel data,

[0011] The secondary response duration is the total response duration of the corrected parallel data and the corrected asynchronous data within a preset adjustment duration by the priority strategy adjustment unit;

[0012] A marking output module, which is connected to the adjustment module, is used to mark the corrected parallel data and the corrected asynchronous data to form a quality control marked data set and an abnormal data set.

[0013] Further, the processing module is used to obtain the number of types of the normal data and the number of types of the abnormal data, obtain the number of types of normal data and the number of types of abnormal data, and determine that the processing mode of the normal data corresponding to the number of types of normal data greater than the number of types of abnormal data is the parallel processing mode;

[0014] The processing module is also used to determine that the processing mode of the abnormal data corresponding to the number of types of abnormal data less than the number of types of normal data is the asynchronous processing mode.

[0015] Further, the processing module is also used to determine that the processing mode of the abnormal data corresponding to the number of types of abnormal data greater than the number of types of normal data is the parallel processing mode;

[0016] The processing module is also used to determine that the processing mode of the normal data corresponding to the number of types of normal data less than the number of types of abnormal data is the asynchronous processing mode.

[0017] Further, the processing module is further configured to determine, according to a preset priority processing policy, that the processing priority of normal data in the parallel processing mode is a high priority;

[0018] The processing module is further configured to determine, according to a preset priority processing policy, that the processing priority of abnormal data in the asynchronous processing mode is a low priority.

[0019] Further, the processing module is further configured to determine, according to a preset priority processing policy, that the processing priority of abnormal data in the parallel processing mode is a high priority;

[0020] The processing module is further configured to determine, according to a preset priority processing policy, that the processing priority of normal data in the asynchronous processing mode is a low priority.

[0021] Further, the processing module is further configured to process the normal data and the abnormal data according to the parallel processing mode and the preset parallel processing speed to obtain a number of parallel data.

[0022] Further, the processing module is further configured to process the normal data and the abnormal data according to the asynchronous processing mode and the asynchronous processing speed to obtain a number of asynchronous data.

[0023] Further, the speed adjustment unit is configured to determine that the secondary response duration greater than the maximum value of the standard response duration range is too long, and increase the preset parallel processing speed or the preset asynchronous processing speed according to the secondary response duration, the preset standard response duration range, and a preset adjustment coefficient to form a corrected parallel processing speed and a corrected asynchronous processing speed;

[0024] The speed adjustment unit is further configured to determine that the secondary response duration less than the minimum value of the standard response duration range is too short, and decrease the preset parallel processing speed or the preset asynchronous processing speed according to the secondary response duration, the preset standard response duration range, and a preset adjustment coefficient to form a corrected parallel processing speed and a corrected asynchronous processing speed;

[0025] The speed adjustment unit is further configured to process the normal data and the abnormal data according to the parallel processing mode and the corrected parallel processing speed to obtain a number of corrected parallel data;

[0026] The speed adjustment unit is further configured to process the normal data and the abnormal data according to the asynchronous processing mode and the corrected asynchronous processing speed to obtain a number of corrected asynchronous data.

[0027] Further, the priority policy adjustment unit is used to calculate the standard deviation of all the secondary response durations of the corrected asynchronous data and the corrected parallel data within a preset adjustment duration to obtain a response duration standard deviation, determine that the response duration standard deviation greater than the preset response duration fluctuation value is too large, and it is necessary to adjust the priority processing policy.

[0028] Further, the inspection module is used to determine that the moisture data not within the preset standard range corresponding to the moisture data is abnormal data;

[0029] The inspection module is also used to determine that the soil data not within the preset standard range corresponding to the soil data is abnormal data;

[0030] The inspection module is also used to determine that the biological data not within the preset standard range corresponding to the biological data is abnormal data;

[0031] The inspection module is also used to determine that the meteorological data not within the preset standard range corresponding to the meteorological data is abnormal data.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows: Through automated data processing, the system significantly improves data quality control, reduces the need for manual intervention. The flexible processing mode and dynamic adjustment mechanism enable the system to adapt to data processing requirements in different scenarios, ensuring that data can still be accurately processed in complex environments. Especially the refined processing of abnormal data helps to improve data quality and reliability. In addition, the built-in priority policy adjustment function of the system enables the system to automatically optimize resource allocation and processing order according to the actual operation situation, thereby enhancing the stability and response speed of the overall system, providing strong technical support for long-term positioning observation of the ecosystem, and effectively solving the problems of poor data quality and untimely decision support caused by frequent manual intervention and insufficient abnormal detection ability.

[0033] Further, by dynamically determining the data processing mode based on the types and quantities of normal data and abnormal data, the system can flexibly adjust the data processing strategy in different situations. When there are more types of normal data, parallel processing is preferred, which improves the efficiency of data processing; when there are more types of abnormal data, asynchronous processing is preferred to ensure that abnormal data can be promptly concerned and processed.

[0034] Further, by dynamically adjusting the processing mode according to the comparison of the types and quantities of normal data and abnormal data, the system can flexibly optimize the processing strategy under different data distributions. When there are more types of abnormal data, parallel processing is adopted to ensure rapid response and processing of a large number of abnormal data; when there are more types of normal data, asynchronous processing is adopted to effectively reduce resource occupancy and ensure processing efficiency.

[0035] Furthermore, by allocating processing modes and priorities based on a preset priority processing strategy, the system can effectively optimize resource utilization, ensure that important normal data is processed in a timely manner, and thus maintain the normal operation of the system. At the same time, abnormal data is set to a low priority when resources are scarce, avoiding excessive resource consumption and system performance degradation.

[0036] Furthermore, by preferentially processing abnormal data in the parallel mode, the system can promptly identify and handle potential data anomalies. Meanwhile, setting normal data to a low priority and adopting an asynchronous processing mode reduces the system's immediate resource consumption and improves the flexibility and efficiency of overall resource scheduling.

[0037] Furthermore, by adopting a parallel processing mode and a preset processing speed, the system can maximize processing efficiency when processing multiple data types simultaneously.

[0038] Furthermore, by adopting an asynchronous processing mode, the system can flexibly adjust according to the specific processing requirements of each data type, effectively avoiding situations of excessive resource consumption and system overload.

[0039] Furthermore, the dynamic adjustment mechanism ensures that the system's processing speed matches the actual demand. It can increase the speed to accelerate the processing progress when the processing time is too long, and reduce the speed to avoid resource waste when the processing time is too short.

[0040] Furthermore, by monitoring the standard deviation of the response duration and comparing it with a preset fluctuation value, this mechanism can promptly identify and correct problems of abnormal response fluctuations, thereby maintaining the stability and processing consistency of the system.

[0041] Furthermore, by strictly checking moisture, soil, biological, and meteorological data, abnormal situations in the data can be quickly identified, which helps improve data quality and the credibility of analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the automated quality control system for long-term fixed-point observation data of the ecosystem in this embodiment;

[0043] Figure 2 It is a flowchart of soil data marking in this embodiment;

[0044] Figure 3 It is a decision logic diagram for the speed adjustment unit to determine and adjust the processing speed in this embodiment;

[0045] Figure 4 It is a decision logic diagram for the priority strategy adjustment unit to determine and adjust the priority processing strategy in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the objectives and advantages of the present invention more clearly understood, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention.

[0047] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0048] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0049] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] Please refer to Figure 1 as shown, which is a schematic diagram of the automated quality control system for long-term fixed-point observation data of the ecosystem in this embodiment;

[0051] This embodiment provides an automated quality control system for long-term fixed-point observation data of an ecosystem, including:

[0052] A data acquisition module for obtaining the observation data of the ecological station, where the observation data includes moisture data, soil data, biological data, and meteorological data;

[0053] An inspection module connected to the data acquisition module for screening according to the observation data and the corresponding preset standard range to determine normal data and abnormal data;

[0054] A processing module connected to the inspection module for sequentially determining the processing modes and processing priorities of the normal data and the abnormal data according to the preset priority processing strategy, and determining a number of parallel data and a number of asynchronous data according to the corresponding processing modes, processing priorities, and processing speeds, where the processing modes include a parallel processing mode and an asynchronous processing mode, and the processing speeds include a preset parallel processing speed and a preset asynchronous processing speed;

[0055] An adjustment module, which is connected to the processing module, includes a speed adjustment unit for obtaining a plurality of corrected parallel data and a plurality of corrected asynchronous data based on a corrected parallel processing speed and a corrected asynchronous processing speed, and a priority policy adjustment unit for adjusting the priority of the preset priority processing policy according to the secondary response duration; wherein, the speed adjustment unit determines the corrected parallel processing speed and the corrected asynchronous processing speed based on the corresponding primary response duration obtained according to each of the asynchronous data and each of the parallel data,

[0056] The secondary response duration is the total response duration of the corrected parallel data and the corrected asynchronous data by the priority policy adjustment unit within a preset adjustment duration;

[0057] A marking output module, connected to the adjustment module, for marking the corrected parallel data and the corrected asynchronous data to form a quality control marking data set and an abnormal data set.

[0058] Among them, the inspection module includes a moisture data inspection sub-unit, a soil data inspection sub-unit, a biological data inspection sub-unit, and a meteorological data inspection sub-unit;

[0059] The moisture data inspection sub-unit is provided with a moisture data standard range, and the moisture data standard range includes:

[0060] Moisture fixed item range: including plot code and plot name to ensure consistency with the records in the standard table;

[0061] Theoretical threshold range of soil moisture content:

[0062] Determine the reasonable range of soil moisture content, which should theoretically be 0%-100%;

[0063] Include the interval range of wilting moisture content and field water holding capacity of a specific plot, including:

[0064] Wilting moisture content: The soil moisture reaches the minimum limit for plant growth. Below this value, plants may show wilting phenomena;

[0065] Field water holding capacity: The maximum value that the soil can hold water. Beyond this value, the soil may have waterlogging or poor drainage;

[0066] The soil data inspection sub-unit is provided with a soil data standard range, and the soil data standard range includes:

[0067] Soil fixed item range:

[0068] Include plot code, plot name, soil type and parent material, vegetation type to ensure consistency with the standard table;

[0069] Sampling depth range:

[0070] The sampling depth in the data should conform to the preset depth range listed in the sampling depth standard table;

[0071] Organic matter content threshold range:

[0072] According to the sampling depth, the organic matter content in the soil should be within the range specified in the threshold table. Data exceeding the threshold will be determined as abnormal.

[0073] The biological data inspection subunit is set with a biological data standard range, and the biological data standard range includes:

[0074] Biological fixed item inspection range:

[0075] Plot code and plot name inspection: Check whether the plot code and plot name in the biological data are consistent with the standard table by referring to the plot standard table to ensure the accuracy of the data source information;

[0076] Species name inspection: Check whether the species names (including Chinese names and Latin scientific names) recorded in the biological data are within the standard range by referring to the species comparison table to ensure the correctness of species classification;

[0077] Missing measurement inspection range:

[0078] Individual data inspection: Check whether there are null values or missing measurement data for important biological indicators such as the number of individuals, individual weight, body length, etc. of each species in the biological data table to ensure the integrity of the basic biological characteristic data of each species;

[0079] Threshold inspection range:

[0080] Species individual index range inspection: Check whether the weight, body length, etc. of individuals of each species in the biological data exceed the reasonable biological range by referring to the preset threshold range (such as the maximum and minimum values of individual weight, body length, etc.). Data exceeding the range will be marked as abnormal data;

[0081] Time consistency inspection range:

[0082] Biological indicator change trend inspection: For regularly observed biological data, check whether the data at different times (such as the number of individuals, body length, weight, etc.) show a growth or decline trend as expected. If the number or growth index of a certain species suddenly fluctuates abnormally in a certain year, it is necessary to further check whether there are recording errors or abnormal situations in the data;

[0083] The meteorological data inspection subunit is set with a meteorological data standard range, and the meteorological data standard range includes:

[0084] Format standardization range:

[0085] Check whether the formats of all meteorological elements comply with the regulations. For example, the wind direction should be an integer between 0° and 360°, the air pressure should be a value with a decimal, the air temperature should be a floating-point number with a positive or negative sign, etc.

[0086] Missing measurement range:

[0087] It is stipulated that meteorological elements (such as air temperature, air pressure, rainfall, etc.) should be completely collected every hour, every day or within a specific time period, and check whether there is any missing data.

[0088] Threshold range:

[0089] The theoretical range or climatological boundary value set for meteorological elements:

[0090] Air temperature: -80°C - 60°C.

[0091] Relative humidity: 0% - 100%.

[0092] Air pressure: 300 hPa - 1100 hPa.

[0093] Wind speed: 0 m / s - 75 m / s.

[0094] Rainfall: 0 mm - 2440 mm.

[0095] Internal consistency range:

[0096] Check the logical consistency between multiple meteorological elements at the same time point to ensure that the data conforms to meteorological logic:

[0097] Relationship of air temperature: minimum air temperature ≤ current air temperature ≤ maximum air temperature.

[0098] Relationship of air pressure: minimum air pressure ≤ current air pressure ≤ maximum air pressure.

[0099] Relationship of relative humidity: minimum relative humidity ≤ current relative humidity ≤ 100%.

[0100] Temporal consistency range:

[0101] For the change of meteorological elements within a continuous time period, check whether the change amplitude conforms to the normal change:

[0102] Check the maximum change amplitude within 2 consecutive hours: For example, the change in air temperature should be within a reasonable range, and data with a difference exceeding the threshold is regarded as abnormal.

[0103] Check the maximum change amplitude within 24 hours: Compare the difference between the maximum value and the minimum value. If it exceeds the set change amplitude, the data is considered problematic.

[0104] Check the duration of continuous non-change: If there is no change in meteorological elements for a long time, it is marked as abnormal data and needs further inspection.

[0105] The marker formation process of the marker output module includes a moisture data marking process, a soil data marking process, a biological data marking process, and a meteorological data marking process. Each process takes the original observation data table, sample plot table, and corresponding data characteristic constant table prepared according to the sample rules as inputs;

[0106] Among them, the purpose of the moisture data marking process is to add an identification column to the original observation data table to identify the records that do not pass various inspections.

[0107] Specific steps of the moisture data marking process:

[0108] Add an identification column: Add a column to the original observation data table to record the marking status.

[0109] Sample plot code check: Traverse all moisture data records and check whether the sample plot code is consistent with the standard table. If not, mark it as "not passed" in the identification column.

[0110] Sample plot name check: Check whether the sample plot name meets the standard requirements. If not, mark it as "not passed" in the identification column.

[0111] Threshold check: Check the threshold range of the moisture data. If the data exceeds the specified range, mark it as "not passed" in the identification column.

[0112] Internal consistency check: Check the logical relationship of the moisture data within the same time period. If there are inconsistent records, mark them as "not passed" in the identification column.

[0113] Please continue to refer to Figure 2 As shown, it is the flow chart of the soil data marking in this embodiment;

[0114] Specific steps of the soil data marking process:

[0115] Add an identification column: Add a column to the original observation data table to record the quality control marking status.

[0116] Sample plot code check: Check whether the sample plot code of each soil data record is consistent with the sample plot code in the standard table.

[0117] For inconsistent records, mark them as "failed sample plot code check" in the identification column.

[0118] Sample plot name check: Check the sample plot name to ensure it is consistent with the standard table.

[0119] If the sample plot name does not meet the standard, mark it as "failed sample plot name check" in the identification column.

[0120] Soil type and parent material inspection: Check whether the soil type and parent material meet the preset standards.

[0121] Records that do not meet the standards are marked as "Failed soil type and parent material inspection" in the identification column.

[0122] Vegetation type inspection: Check whether the vegetation type recorded in the soil data is consistent with the standard table.

[0123] If they are inconsistent, it will be marked as "Failed vegetation type inspection" in the identification column.

[0124] Format compliance inspection: Check whether the data format meets the regulations, such as numerical type, unit, etc.

[0125] Records with non-compliant formats are marked as "Failed format compliance inspection" in the identification column.

[0126] Threshold inspection: Conduct a threshold range inspection on the soil data to ensure that all indicators are within a reasonable range.

[0127] Records exceeding the preset threshold range are marked as "Failed threshold inspection" in the identification column.

[0128] Specific steps of the biological data marking process:

[0129] Add an identification column: Add an "identification column" to the original observation data table to record whether the quality control inspection is passed.

[0130] For records that fail the inspection, the identification column will be marked with the corresponding error type.

[0131] Plot code inspection: Check whether the plot code in the biological data is consistent with the code in the standard table.

[0132] If they are inconsistent, the identification column will be marked as "Failed plot code inspection".

[0133] Plot name inspection: Check whether the plot name in the biological data is consistent with the name in the standard table.

[0134] If the names do not match, the identification column will be marked as "Failed plot name inspection".

[0135] Missing measurement inspection: Check whether there are null values or missing measurement data for important indicators such as the number of individuals, body length, and weight in the biological data table.

[0136] If there are missing values, the identification column will be marked as "Failed missing measurement inspection".

[0137] Species name inspection: Check whether the species name in the biological data is consistent with the species name in the standard species table by referring to the species comparison table.

[0138] If the species name does not match, the identification column will be marked as "Failed plant species name check".

[0139] Latin name check: Check whether the Latin scientific name being examined is consistent with the standard table to ensure the accuracy of classification.

[0140] If the Latin name does not match, the identification column will be marked as "Failed Latin name check".

[0141] Threshold check: Conduct a preset threshold range check on individual biological indicators (such as body length, body weight, etc.).

[0142] If it exceeds the reasonable threshold range, the identification column will be marked as "Failed threshold check".

[0143] Temporal consistency check: Data change trend: Whether the changes in biological indicators at different time points conform to the growth pattern.

[0144] If the change exceeds the expectation or is abnormal, the identification column will be marked as "Failed temporal consistency check".

[0145] Specific steps of the meteorological data marking process:

[0146] Add an identification column: Add an "identification column" to the original meteorological observation data table to record whether it passes the quality control check.

[0147] For records that fail the check, the identification column will be marked with the corresponding error type or data missing situation.

[0148] Format standardization check: Check whether the meteorological data format conforms to the standard regulations. For example:

[0149] The wind direction should be an integer between 0° and 360°;

[0150] The air temperature should be a floating-point number with positive or negative signs;

[0151] The air pressure should be a numerical value with decimals.

[0152] If it does not meet the format requirements, the identification column will be marked as "Failed format standardization check".

[0153] Threshold check: Conduct a threshold range check on the meteorological data. For example:

[0154] The air temperature should be between -80°C and 60°C;

[0155] The relative humidity should be between 0% and 100%;

[0156] The air pressure should be between 300 hPa and 1100 hPa.

[0157] Data outside the reasonable range will be marked as "Failed threshold check".

[0158] Internal consistency check: Check the logical consistency among multiple meteorological elements at the same time point. For example:

[0159] Temperature relationship: Minimum temperature ≤ current temperature ≤ maximum temperature;

[0160] Relative humidity relationship: Minimum relative humidity ≤ current relative humidity ≤ 100%.

[0161] If the logical consistency is not met, the identification column will be marked as "Failed internal consistency check".

[0162] Maximum change range check for 2 consecutive hours: Check whether the change range of meteorological data within 2 consecutive hours exceeds the preset change range. For example: The change difference of temperature within 2 hours should not exceed the threshold.

[0163] Records exceeding the threshold will be marked as "Failed maximum change range check for 2 consecutive hours".

[0164] Maximum change range check for 24 hours: Check the maximum change range of meteorological data within 24 hours. For example: The difference between the maximum and minimum values of temperature should not exceed the set threshold.

[0165] If the change range exceeds the expectation, the identification column will be marked as "Failed maximum change range check for 24 hours".

[0166] Check for continuous unchanged duration: Check whether meteorological elements remain unchanged continuously for a period of time. For example, data such as temperature and air pressure that remain unchanged for a long time may be problematic.

[0167] If the duration of unchanged data exceeds the reasonable range, the identification column will be marked as "Failed check for continuous unchanged duration".

[0168] Missing data check:

[0169] Check whether there is missing meteorological data. For example, whether there are blanks or missing measurements for temperature, air pressure, wind speed, etc. in certain time periods.

[0170] For missing data, corresponding date and hour data records need to be added to the original data table.

[0171] Missing data will be recorded as "Data missing" in the identification column.

[0172] An ecological station is the specific location for conducting ecosystem observations. Usually, a representative ecological environment area is selected. In this embodiment, a forest ecosystem is selected. This setting helps to collect data with broad ecological significance.

[0173] The water content data of the ecological station are obtained through two observation methods: neutron probe observation and TDR automatic observation. The water content data include:

[0174] Water content fixed items:

[0175] Plot code: The code that uniquely identifies the sampling location;

[0176] Plot name: The name of the sampling location.

[0177] Soil water content:

[0178] Theoretical threshold range: 0% - 100%;

[0179] Wilting water content: The lowest water content for plant growth;

[0180] Field water holding capacity: The maximum amount of water that the soil can hold.

[0181] The soil data of the ecological station include:

[0182] Soil fixed items:

[0183] Plot code: The code that uniquely identifies the sampling location;

[0184] Plot name: The name of the sampling location;

[0185] Soil type: Such as sandy soil, loam, and clay;

[0186] Parent material: The parent rock or material from which the soil is formed;

[0187] Vegetation type: The types of vegetation growing on this soil.

[0188] Sampling depth:

[0189] Preset depth range: The sampling depth that conforms to the experimental or observational specifications.

[0190] Organic matter content:

[0191] Threshold range: The range of soil organic matter content based on the sampling depth.

[0192] The biological data of the ecological station include:

[0193] Biological fixed items:

[0194] Plot code: The code that uniquely identifies the sampling location;

[0195] Plot name: The name of the sampling location.

[0196] Species name:

[0197] Verified species: Whether the recorded species name (including Chinese name and Latin name) conforms to the standard.

[0198] Missing data check:

[0199] Individual data: Check if there are null values or missing measurements for the DBH and tree height of trees.

[0200] Threshold check:

[0201] Range of individual indicators: Check if the DBH and tree height of trees are within a reasonable range.

[0202] Temporal consistency check:

[0203] Temporal consistency check for DBH: Perform a temporal consistency check on the DBH data of trees recorded within the same time period to ensure that its changes conform to the expected growth or decline pattern.

[0204] If the DBH data changes abnormally, the identification column will be marked as "Failed temporal consistency check for DBH".

[0205] Temporal consistency check for tree height: Perform a temporal consistency check on the recorded tree height data to ensure that the data conforms to the reasonable trend of biological growth.

[0206] If the change in tree height exceeds the expected value or is abnormal, the identification column will be marked as "Failed temporal consistency check for tree height".

[0207] The meteorological data in the ecological station includes:

[0208] Format standardization:

[0209] Format of meteorological elements: such as wind direction (0° - 360°), air pressure (with decimals), and air temperature (floating point number with sign).

[0210] Missing data range:

[0211] Collection integrity: Check if the meteorological elements are collected completely every hour, daily, or within a specific time period.

[0212] Threshold range:

[0213] Air temperature: -80°C to 60°C;

[0214] Relative humidity: 0% to 100%;

[0215] Air pressure: 300 hPa to 1100 hPa;

[0216] Wind speed: 0 m / s to 75 m / s;

[0217] Rainfall: 0 mm to 2440 mm.

[0218] Internal consistency:

[0219] Logical consistency: The relationship between meteorological elements at the same point in time, such as whether the temperature and air pressure are reasonable.

[0220] The preset priority processing strategy refers to the strategy of preferentially selecting a certain processing mode according to the normal or abnormal state of the data when processing data. Specifically, when three of the four types of data are normal data and one is abnormal data, the parallel processing mode is preferentially selected, that is, the normal data is first processed in parallel to quickly process the normal data and timely feedback the normal situation, and then the abnormal data is processed. This strategy can improve the overall processing efficiency, ensure that the system quickly responds to normal data, and at the same time perform subsequent processing on abnormal data. In this case, the amount of normal data is the majority, and the system chooses to process the normal data first to ensure the stability and efficiency of the system, and then process the abnormal data, which can avoid affecting the processing speed of normal data due to the delay in processing abnormal data; when three of the four types of data are abnormal data and one is normal data, the asynchronous processing mode is preferentially selected, that is, the abnormal data is first processed asynchronously to quickly solve potential abnormal problems, and at the same time the normal data can be processed asynchronously during idle time. In this case, the amount of abnormal data is the majority, and the system chooses to process the abnormal data first to timely solve possible system abnormal problems and ensure the normal operation of the system, and then process the normal data to avoid the performance problems of the overall system caused by abnormal data. This strategy can reduce the processing delay of abnormal data and improve the system's response ability to abnormal situations.

[0221] The processing mode refers to the way of data processing, including the parallel processing mode and the asynchronous processing mode, which depends on the complexity of data processing and resource conditions.

[0222] The processing priority refers to the priority order of data during processing, which depends on the importance and urgency of the data and is usually determined according to the preset priority processing strategy.

[0223] The preset parallel processing speed refers to the speed of processing data in parallel, which depends on the processing ability of the system and is usually based on the maximum resource utilization rate. In this embodiment, it is set to 80% CPU utilization. Such a setting can effectively utilize system resources.

[0224] The preset asynchronous processing speed refers to the speed of processing data asynchronously, which depends on the system load and the complexity of processing tasks and is usually set to a speed that does not affect critical tasks. In this embodiment, it is set to 60% CPU utilization. Such a setting helps to balance the system load.

[0225] Parallel data refers to multiple sets of data processed simultaneously, which depends on the processing mode and the amount of data and is usually adopted when the amount of data is large.

[0226] Asynchronous data refers to data that is processed with a delay. Depending on the urgency of the data and the system load, it is usually used when there is insufficient processing resources.

[0227] The secondary response time refers to the time required for the system to process a single data task. Depending on the complexity of data processing and system resources, the time standard is usually set according to the task urgency. In this embodiment, it is 1 second. Such a setting can ensure the response ability of the system.

[0228] The preset standard response time range refers to the ideal response time range set to ensure the stable and efficient operation of the system during data processing. The setting basis for this range includes factors such as the system's processing ability, data complexity, and network latency. The maximum response time that the system can accept depends on the speed and computing power of the processor. Generally, the preset standard response time range may be set from several seconds to dozens of seconds. In this embodiment, the preset standard response time range is set to 10 to 15 seconds. This value is determined according to the actual processing ability of the system and data processing requirements to ensure better processing efficiency in most cases.

[0229] The preset adjustment time refers to the time window considered when adjusting the processing strategy. Depending on the periodicity of data processing and the system state, it is usually one data processing cycle. In this embodiment, it is 5 minutes. Such a setting can timely adapt to changes in system load.

[0230] The preset response time fluctuation value refers to the standard deviation used to measure the response time, which is used to evaluate the fluctuation degree of the system response time. It reflects the change range of the system response time within a certain period, depending on the system's performance requirements and actual application scenarios. Generally, a lower standard deviation indicates that the system response time is relatively stable, while a higher standard deviation indicates that the response time fluctuates greatly. It is usually determined based on the system's performance requirements and historical data. In this embodiment, the preset response time fluctuation value is set to 0.5 seconds, which helps to ensure a certain stability of the system during processing and avoid frequent strategy adjustments due to small fluctuations.

[0231] The moisture, soil, biological, and meteorological data of the ecological station are classified by the inspection module against the preset standard range to distinguish normal data and abnormal data. The processing module determines the processing mode (parallel or asynchronous) and its priority of the data according to the priority processing strategy, and then allocates the data into parallel data and asynchronous data in combination with the preset processing speed. The adjustment module dynamically corrects the processing speed to ensure the data acquisition module for monitoring the data of the ecological station, including moisture data, soil data, biological data, and meteorological data;

[0232] An inspection module, which is connected to the data acquisition module and is used to perform screening processing according to the observation data and the corresponding preset standard range to determine normal data and abnormal data;

[0233] A processing module, which is connected to the inspection module and is used to sequentially determine the processing modes and processing priorities of the normal data and the abnormal data according to the preset priority processing strategy, and determine a number of parallel data and a number of asynchronous data according to the corresponding processing modes, the processing priorities and the processing speeds, wherein the processing modes include a parallel processing mode and an asynchronous processing mode, and the processing speeds include a preset parallel processing speed and a preset asynchronous processing speed;

[0234] An adjustment module, which is connected to the processing module and includes a speed adjustment unit for obtaining a number of corrected parallel data and a number of corrected asynchronous data based on a corrected parallel processing speed and a corrected asynchronous processing speed, and a priority strategy adjustment unit for adjusting the preset priority processing strategy according to a secondary response duration; wherein the speed adjustment unit determines the corrected parallel processing speed and the corrected asynchronous processing speed based on the corresponding first-level response durations obtained from each of the asynchronous data and each of the parallel data,

[0235] The secondary response duration is the total response duration of the corrected parallel data and the corrected asynchronous data within a preset adjustment duration by the priority strategy adjustment unit;

[0236] A marking output module, which is connected to the adjustment module and is used to mark the corrected parallel data and the corrected asynchronous data to form a quality control marked data set and an abnormal data set. A data acquisition module is used to monitor the data of the ecological station, including moisture data, soil data, biological data and meteorological data;

[0237] An inspection module, which is connected to the data acquisition module and is used to perform screening processing according to the observation data and the corresponding preset standard range to determine normal data and abnormal data;

[0238] A processing module, which is connected to the inspection module and is used to sequentially determine the processing modes and processing priorities of the normal data and the abnormal data according to the preset priority processing strategy, and determine a number of parallel data and a number of asynchronous data according to the corresponding processing modes, the processing priorities and the processing speeds, wherein the processing modes include a parallel processing mode and an asynchronous processing mode, and the processing speeds include a preset parallel processing speed and a preset asynchronous processing speed;

[0239] An adjustment module, which is connected to the processing module, includes a speed adjustment unit for obtaining a number of corrected parallel data and a number of corrected asynchronous data based on a corrected parallel processing speed and a corrected asynchronous processing speed, and a priority policy adjustment unit for adjusting the priority of the preset priority processing policy according to the secondary response duration; wherein, the speed adjustment unit determines the corrected parallel processing speed and the corrected asynchronous processing speed based on the corresponding primary response duration obtained according to each of the asynchronous data and each of the parallel data,

[0240] The secondary response duration is the total response duration of the corrected parallel data and the corrected asynchronous data within a preset adjustment duration by the priority policy adjustment unit;

[0241] A marking output module, connected to the adjustment module, for marking the corrected parallel data and the corrected asynchronous data to form a quality control marked data set and an abnormal data set. A data acquisition module for monitoring the data of the ecological station, including moisture data, soil data, biological data, and meteorological data;

[0242] An inspection module, connected to the data acquisition module, for performing screening processing according to the observed data and the corresponding preset standard range to determine normal data and abnormal data;

[0243] A processing module, connected to the inspection module, for sequentially determining the processing mode and processing priority of the normal data and the abnormal data according to a preset priority processing policy, and for determining a number of parallel data and a number of asynchronous data according to the corresponding processing mode, the processing priority, and the processing speed, wherein the processing mode includes a parallel processing mode and an asynchronous processing mode, and the processing speed includes a preset parallel processing speed and a preset asynchronous processing speed;

[0244] An adjustment module, which is connected to the processing module, includes a speed adjustment unit for obtaining a number of corrected parallel data and a number of corrected asynchronous data based on a corrected parallel processing speed and a corrected asynchronous processing speed, and a priority policy adjustment unit for adjusting the priority of the preset priority processing policy according to the secondary response duration; wherein, the speed adjustment unit determines the corrected parallel processing speed and the corrected asynchronous processing speed based on the corresponding primary response duration obtained according to each of the asynchronous data and each of the parallel data,

[0245] The secondary response duration is the total response duration of the corrected parallel data and the corrected asynchronous data within a preset adjustment duration by the priority policy adjustment unit;

[0246] A marking output module, connected to the adjustment module, is used to mark the corrected parallel data and the corrected asynchronous data to form a quality control marked data set and an abnormal data set. The secondary response duration meets the standard, and the processing strategy is optimized by the priority policy adjustment unit. Finally, the marking output module marks the adjusted data and forms a quality control marked data set and an abnormal data set for further analysis and decision-making.

[0247] Through automated data processing, the system significantly improves data quality control, reduces the need for manual intervention. The flexible processing mode and dynamic adjustment mechanism enable the system to adapt to data processing requirements in different scenarios, ensuring accurate data processing in complex environments. Especially the refined processing of abnormal data helps to improve data quality and the reliability of observation results. In addition, the built-in priority policy adjustment function in the system enables the system to automatically optimize resource allocation and processing order according to the actual operation situation, thereby enhancing the overall stability and response speed of the system, providing strong technical support for long-term positioning observation of the ecosystem, and effectively solving the problems of poor data quality and insufficient decision-making support caused by frequent manual intervention and insufficient abnormal detection ability.

[0248] Specifically, the processing module is used to obtain the type quantity of the normal data and the type quantity of the abnormal data, obtain the type quantity of the normal data and the type quantity of the abnormal data, and determine that the processing mode of the normal data corresponding to the type quantity of the normal data greater than the type quantity of the abnormal data is the parallel processing mode;

[0249] The processing module is also used to determine that the processing mode of the abnormal data corresponding to the type quantity of the abnormal data less than the type quantity of the normal data is the asynchronous processing mode.

[0250] After the processing module obtains moisture data, soil data, biological data, and meteorological data, it first classifies these data and determines which data belong to the normal range and which data belong to the abnormal range. The processing module then counts the type quantity of the normal data and the abnormal data. If the type quantity of the normal data is more than the type quantity of the abnormal data, the processing module sets these normal data to the parallel processing mode to quickly process a large amount of normal data; on the contrary, if the type quantity of the abnormal data is more than the type quantity of the normal data, the processing module will set these abnormal data to the asynchronous processing mode to give priority to processing fewer abnormal data.

[0251] By dynamically determining the data processing mode based on the type quantity of the normal data and the abnormal data, the system can flexibly adjust the data processing strategy in different situations. When the type quantity of the normal data is large, parallel processing is given priority to improve the data processing efficiency; when the type quantity of the abnormal data is large, asynchronous processing is given priority to ensure that the abnormal data is promptly concerned and processed.

[0252] Specifically, the processing module is further configured to determine that the processing mode for abnormal data corresponding to the number of abnormal data types greater than the number of normal data types is a parallel processing mode;

[0253] The processing module is further configured to determine that the processing mode for normal data corresponding to the number of normal data types less than the number of abnormal data types is an asynchronous processing mode.

[0254] After obtaining moisture data, soil data, biological data, and meteorological data, the processing module classifies these data and determines which belong to normal data and which belong to abnormal data. Subsequently, the processing module counts the number of types of normal data and abnormal data, and decides the processing mode based on the comparison of the number of data types. When the number of types of abnormal data is more than the number of types of normal data, the system sets these abnormal data to the parallel processing mode to quickly process a large amount of abnormal data; while when the number of types of normal data is more than the number of types of abnormal data, the system sets these normal data to the asynchronous processing mode to give priority to processing normal data.

[0255] By dynamically adjusting the processing mode according to the comparison of the number of types of normal data and abnormal data, the system can flexibly optimize the processing strategy under different data distributions. When there are more types of abnormal data, parallel processing is adopted to ensure quick response and processing of a large amount of abnormal data; when there are more types of normal data, asynchronous processing is adopted to effectively reduce resource occupancy and ensure processing efficiency.

[0256] Specifically, the processing module is further configured to determine that the processing priority of normal data with a processing mode of parallel processing is a high priority according to a preset priority processing strategy;

[0257] The processing module is further configured to determine that the processing priority of abnormal data with a processing mode of asynchronous processing is a low priority according to a preset priority processing strategy.

[0258] After obtaining the data, the processing module determines the processing mode and processing priority of each data according to the preset priority processing strategy. Specifically, the system first determines which data should adopt the parallel processing mode and which data should adopt the asynchronous processing mode. Then, according to the preset priority processing strategy, the processing module sets the normal data in the parallel processing mode to a high priority to ensure that these data can be quickly processed. At the same time, the processing module sets the abnormal data in the asynchronous processing mode to a low priority for processing when system resources permit.

[0259] By allocating processing modes and priorities based on a preset priority processing strategy, the system can effectively optimize resource utilization, ensure that important normal data is processed in a timely manner, and thus maintain the normal operation of the system. At the same time, abnormal data is set to a low priority when resources are scarce, avoiding excessive resource consumption and system performance degradation.

[0260] Specifically, the processing module is also used to determine that the processing priority of abnormal data with a parallel processing mode according to the preset priority processing strategy is a high priority;

[0261] The processing module is also used to determine that the processing priority of normal data with an asynchronous processing mode according to the preset priority processing strategy is a low priority.

[0262] After obtaining the data, the processing module decides the processing mode and priority of each data according to the preset priority processing strategy. Specifically, the system first determines which data should adopt a parallel processing mode or an asynchronous processing mode. For abnormal data in the parallel processing mode, the processing module will set it to a high priority to ensure that these key data can be processed first. For normal data in the asynchronous processing mode, the processing module will set it to a low priority so that it can be processed when the system resources are sufficient.

[0263] By preferentially processing abnormal data in the parallel mode, the system can identify and process potential data anomalies in a timely manner. At the same time, setting normal data to a low priority and adopting an asynchronous processing mode reduces the immediate resource consumption of the system and improves the flexibility and efficiency of overall resource scheduling.

[0264] Specifically, the processing module is also used to process the normal data and the abnormal data according to the parallel processing mode and the preset parallel processing speed to obtain a number of parallel data.

[0265] The processing module simultaneously processes normal data and abnormal data from the ecological station according to the parallel processing mode set in the system and the preset parallel processing speed. In this way, the system can generate multiple parallel data in a relatively short time, ensuring the efficiency and timeliness of data processing.

[0266] By adopting a parallel processing mode and a preset processing speed, the system can maximize the processing efficiency when processing multiple data types simultaneously.

[0267] Specifically, the processing module is also used to process the normal data and the abnormal data according to the asynchronous processing mode and the asynchronous processing speed to obtain a number of asynchronous data.

[0268] The processing module processes the normal data and abnormal data of the ecological station step by step according to the asynchronous processing mode and the preset asynchronous processing speed. This process does not require all data to be processed simultaneously, but generates asynchronous data sequentially according to their respective processing requirements and speeds, so as to flexibly meet the processing requirements of different data.

[0269] By adopting the asynchronous processing mode, the system can be flexibly adjusted according to the specific processing requirements of each data type, effectively avoiding the situation of excessive resource consumption and system overload.

[0270] Please continue to refer to Figure 3 shown, which is the decision logic diagram for the speed adjustment unit of this embodiment to determine the adjusted processing speed;

[0271] Specifically, the speed adjustment unit is used to determine that the secondary response duration greater than the maximum value of the standard response duration range is too long, and increase the preset parallel processing speed or the preset asynchronous processing speed according to the secondary response duration, the preset standard response duration range, and the preset adjustment coefficient to form a corrected parallel processing speed and a corrected asynchronous processing speed, where V1’ = V1×[1 + k×(T - Tmax) / Tmax], V2’ = V2×[1 + k×(T - Tmax) / Tmax], V1’ is the corrected parallel processing speed, V1 is the preset parallel processing speed, k is the preset adjustment coefficient, Tmax is the maximum value of the preset standard response duration range, T is the secondary response duration, V2’ is the corrected asynchronous processing speed, and V2 is the preset asynchronous processing speed;

[0272] The speed adjustment unit is also used to determine that the secondary response duration less than the minimum value of the standard response duration range is too short, and decrease the preset parallel processing speed or the preset asynchronous processing speed according to the secondary response duration, the preset standard response duration range, and the preset adjustment coefficient to form a corrected parallel processing speed and a corrected asynchronous processing speed, where V1’ = V1×[1 - k×(Tmin - T) / Tmin], V2’ = V2×[1 - k×(Tmin - T) / Tmin], and Tmin is the minimum value of the preset standard response duration range;

[0273] The preset adjustment coefficient is a dimensionless parameter used to adjust between the secondary response duration and the processing speed. It optimizes the system performance by correcting the processing speed. Depending on the sensitivity of the system to the secondary response duration and the processing speed, the desired system stability, and the complexity of the processing task, it is usually set to a relatively small value, such as between 0.01 and 0.1. In this embodiment, the preset adjustment coefficient is set to 0.05. This value can flexibly adjust the processing speed to a certain extent to cope with different response durations without causing excessive speed fluctuations;

[0274] The speed adjustment unit is further configured to process the normal data and the abnormal data according to the parallel processing mode and the corrected parallel processing speed, so as to obtain a plurality of corrected parallel data;

[0275] The speed adjustment unit is further configured to process the normal data and the abnormal data according to the asynchronous processing mode and the corrected asynchronous processing speed, so as to obtain a plurality of corrected asynchronous data.

[0276] The speed adjustment unit determines whether the response is too long or too short by monitoring the comparison between the actual response duration and the standard response duration range during the processing. If the response duration is greater than the standard response duration range, the parallel or asynchronous processing speed is increased according to the response duration, the standard response duration range, and a preset adjustment coefficient to improve the processing efficiency. On the contrary, if the response duration is less than or equal to the standard value, the processing speed is decreased according to the same parameters to avoid resource waste or overprocessing, thereby optimizing the system performance. Finally, the corrected parallel data and the corrected asynchronous data are determined according to the formed corrected parallel processing speed and corrected asynchronous processing speed.

[0277] The dynamic adjustment mechanism ensures that the processing speed of the system matches the actual demand, which can not only increase the speed to accelerate the processing progress when the processing time is too long, but also reduce the speed to avoid resource waste when the processing time is too short.

[0278] Please continue to refer to Figure 4 as shown, which is the decision logic diagram for the priority policy adjustment unit in this embodiment to determine and adjust the priority processing policy;

[0279] Specifically, the priority policy adjustment unit is configured to calculate the standard deviation of all the secondary response durations of the corrected parallel data and the corrected asynchronous data within a preset adjustment duration to obtain the response duration standard deviation, and determine that the response duration standard deviation greater than the preset response duration fluctuation value is too large and the priority processing policy needs to be adjusted.

[0280] This adjustment includes: originally giving priority to processing the normal data in the parallel processing mode, now giving priority to processing the abnormal data in the asynchronous processing mode; originally giving priority to processing the abnormal data in the asynchronous processing mode, now giving priority to processing the normal data in the parallel processing mode. The priority policy adjustment unit monitors the fluctuation of the secondary response duration by calculating the standard deviation of all the secondary response durations within a preset adjustment duration. If the calculated response duration standard deviation is greater than the preset response duration fluctuation value, it is determined that the fluctuation of the response duration is too large and the priority processing policy needs to be adjusted.

[0281] By monitoring the standard deviation of the response duration and comparing it with the preset fluctuation value, this mechanism can timely identify and correct the problem of abnormal response fluctuation, thereby maintaining the stability and processing consistency of the system.

[0282] Specifically, the inspection module is used to determine that the moisture data not within the preset standard range corresponding to the moisture data is abnormal data;

[0283] The inspection module is also used to determine that the soil data not within the preset standard range corresponding to the soil data is abnormal data;

[0284] The inspection module is also used to determine that the biological data not within the preset standard range corresponding to the biological data is abnormal data;

[0285] The inspection module is also used to determine that the meteorological data not within the preset standard range corresponding to the meteorological data is abnormal data.

[0286] The inspection module performs quality assessment on the collected data. It first checks the moisture data. If the data is not within the preset standard range, it is marked as abnormal data. Then, the inspection module performs the same check on the soil data. If the data exceeds the preset standard range, it is also marked as abnormal data. Next, the inspection module evaluates the biological data. If the data does not meet the preset standard range, it is also determined to be abnormal data. Finally, the inspection module performs abnormal assessment on the meteorological data. Through such an inspection process, the system can systematically identify and isolate the abnormal values in the data, providing an accurate basis for subsequent data processing.

[0287] By strictly inspecting the moisture, soil, biological, and meteorological data, abnormal situations in the data can be quickly identified, which helps to improve the data quality and the credibility of the analysis results.

[0288] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0289] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An automated quality control system for ecosystem long-term positioning observation data, characterized in that: include: Data collection module, used to obtain observation data of the ecological station, including water data, soil data, biological data and meteorological data; An inspection module, connected to the data acquisition module, for performing screening processing according to the observed data and a corresponding preset standard range to determine normal data and abnormal data; a processing module connected to the inspection module, for determining the processing modes and processing priorities of the normal data and the abnormal data in sequence according to a preset priority processing strategy, and determining a number of parallel data and a number of asynchronous data according to the corresponding processing modes, processing priorities and processing speeds, wherein the processing modes include a parallel processing mode and an asynchronous processing mode, and the processing speeds include a preset parallel processing speed and a preset asynchronous processing speed; an adjustment module connected to the processing module, comprising a speed adjustment unit for obtaining a plurality of corrected parallel data and a plurality of corrected asynchronous data based on the corrected parallel processing speed and the corrected asynchronous processing speed, and a priority strategy adjustment unit for adjusting the preset priority processing strategy according to the secondary response time; wherein the speed adjustment unit determines the corrected parallel processing speed and the corrected asynchronous processing speed based on the corresponding primary response time obtained according to each of the asynchronous data and each of the parallel data, The secondary response duration is the total response duration of the priority policy adjustment unit based on the corrected parallel data and the corrected asynchronous data within the preset adjustment duration; A marking output module is connected to the adjustment module and is used to mark the corrected parallel data and the corrected asynchronous data to form a quality control marking data set and an abnormal data set.

2. The automated quality control system for ecosystem long-term positioning observation data according to claim 1 is characterized in that: The processing module is used to obtain the number of types of the normal data and the number of types of the abnormal data, obtain the number of normal data types and the number of abnormal data types, and determine that the processing mode of the normal data corresponding to the number of normal data types greater than the number of abnormal data types is a parallel processing mode; The processing module is further used to determine that the processing mode of abnormal data corresponding to the number of abnormal data types that is smaller than the number of normal data types is an asynchronous processing mode.

3. The automated quality control system for ecosystem long-term positioning observation data according to claim 2 is characterized in that: The processing module is also used to determine that the processing mode of abnormal data corresponding to the number of abnormal data types greater than the number of normal data types is a parallel processing mode; The processing module is further used to determine that the processing mode of normal data corresponding to the number of normal data types that is smaller than the number of abnormal data types is an asynchronous processing mode.

4. The automated quality control system for ecosystem long-term positioning observation data according to claim 3 is characterized in that: The processing module is also used to determine that the processing priority of normal data in the parallel processing mode is a high priority according to a preset priority processing strategy; The processing module is also used to determine that the processing priority of abnormal data in the asynchronous processing mode is a low priority according to a preset priority processing strategy.

5. The automated quality control system for ecosystem long-term positioning observation data according to claim 4 is characterized in that: The processing module is also used to determine that the processing priority of abnormal data in the parallel processing mode is a high priority according to a preset priority processing strategy; The processing module is also used to determine that the processing priority of normal data in the asynchronous processing mode is a low priority according to a preset priority processing strategy.

6. The automated quality control system for ecosystem long-term positioning observation data according to claim 5, characterized in that: The processing module is further used to process the normal data and the abnormal data according to the parallel processing mode and the preset parallel processing speed to obtain a plurality of parallel data.

7. The automated quality control system for ecosystem long-term positioning observation data according to claim 6, characterized in that: The processing module is also used to process the normal data and the abnormal data according to the asynchronous processing mode and the asynchronous processing speed to obtain a plurality of asynchronous data.

8. The automated quality control system for ecosystem long-term positioning observation data according to claim 7, characterized in that: The speed adjustment unit is used to determine that the secondary response time that is greater than the maximum value of the standard response time range is too long, and to increase the preset parallel processing speed or the preset asynchronous processing speed according to the secondary response time, the preset standard response time range and the preset adjustment coefficient to form a modified parallel processing speed and a modified asynchronous processing speed; The speed adjustment unit is further used to determine that the secondary response time that is less than the minimum value of the standard response time range is too short, and to reduce the preset parallel processing speed or the preset asynchronous processing speed according to the secondary response time, the preset standard response time range, and the preset adjustment coefficient to form a modified parallel processing speed and a modified asynchronous processing speed; The speed adjustment unit is further used to process the normal data and the abnormal data according to the parallel processing mode and the modified parallel processing speed to obtain a number of modified parallel data; The speed adjustment unit is further used to process the normal data and the abnormal data according to the asynchronous processing mode and the modified asynchronous processing speed to obtain a plurality of modified asynchronous data.

9. The automated quality control system for ecosystem long-term positioning observation data according to claim 8, characterized in that: The priority strategy adjustment unit is used to calculate the standard deviation of all the secondary response times of the corrected asynchronous data and the corrected parallel data within the preset adjustment time, obtain the response time standard deviation, and determine that the response time standard deviation greater than the preset response time fluctuation value is too large, and the priority processing strategy needs to be adjusted.

10. The automated quality control system for ecosystem long-term positioning observation data according to claim 9, characterized in that: The checking module is used to determine that the moisture data that is not within the preset standard range corresponding to the moisture data is abnormal data; The checking module is also used to determine that the soil data that is not within the preset standard range corresponding to the soil data is abnormal data; The checking module is also used to determine that the biological data that is not within the preset standard range corresponding to the biological data is abnormal data; The checking module is also used to determine that the meteorological data that is not within a preset standard range corresponding to the meteorological data is abnormal data.

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