Urban green space carbon sink intelligent regulation system and method based on multi-source data fusion

Abstract

The present application relates to the technical field of urban carbon sink management, in particular to an urban green land carbon sink intelligent regulation and control system and method based on multi-source data fusion, which comprises a soil state identification module, a flux intervention identification module, a change triggering module, a green land structure merging module and a regulation and control path derivation module. In the present application, the vertical carbon response layer boundary is demarcated through the spatial alignment of the osmotic pressure trajectory and the vertical variation trend of the soil organic carbon content, the offset relationship between the fluctuation characteristics before and after the flux disturbance and the carbon input trend on the time axis is combined, the abnormal areas that respond synchronously under the influence of the disturbance are extracted, the disturbance frequency and the trend offset position are superimposed, the response area is identified, the spatial extension and the coherent and advancing characteristics green land area are integrated according to the boundary connection structure between the green land segments and the consistency of the carbon migration direction, the flux fluctuation response capability, the disturbance path identification capability and the structure advancing characteristic carbon sink path paragraph are constructed, and the intervention directivity and the structure linkage in the urban green land regulation and control are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban carbon sink management, and particularly relates to an urban green land carbon sink intelligent regulation system and method based on multi-source data fusion. BACKGROUND

[0002] The technical field of urban carbon sink management includes carbon emission control, carbon fixation capacity evaluation, carbon sink capacity improvement, carbon flux monitoring and regulation, and the like, and is an important technical direction for supporting climate governance and ecological restoration. The field takes ecological units such as green land, forest, and wetland as objects, and carries out management based on basic indexes such as land use change, biological productivity dynamics, and soil carbon storage change, and covers joint evaluation methods of multiple spatial scales and time sequence changes. With the acceleration of urbanization, the field technology gradually introduces remote sensing observation, environmental sensing monitoring, and urban meteorological data, develops carbon sink regulation means for urban areas, and emphasizes the accurate identification and intervention response of the regulation object in time and space. Through comprehensive analysis of carbon flux changes and human activity influences, the carbon sink contribution capacity of urban ecological space is improved.

[0003] Among them, the urban green land carbon sink intelligent regulation scheme based on multi-source data fusion refers to unified integration analysis of multiple types of data such as remote sensing images, meteorological records, and ground monitoring results, clear key contents such as carbon fixation capacity distribution in urban green land, soil carbon storage change, and human intervention time sequence characteristics, and generation of regulation basis for classified analysis of green land carbon sink conditions by using methods such as time series vegetation coverage extraction, carbon storage estimation model construction, and spatial interference evaluation quantification. Based on rule constraints and spatial discrimination results, classified management operations are performed, so as to complete the zoning regulation work of the dynamic characteristics of urban green land carbon sink.

[0004] The existing technology is based on spatial classification and data fusion evaluation of ecological units, and when facing urban green land scenes, it focuses on index integration and static zoning, ignores the time sequence shift correlation between carbon flux changes and disturbance events, and is difficult to accurately extract the evolution track of flux anomalies. Spatial segment aggregation is mainly based on the continuity of coverage types, and the dynamic disturbance path in the green land edge staggered area cannot be fully captured, and the carbon input abnormal change caused by high-frequency disturbance in the local green land junction is easily missed. In the response evaluation after the typical scene of short-term and sharp fluctuation of flux, there are problems such as fuzzy response node, scattered regional merging, and interrupted path recognition, which affect the effective extraction and intervention matching of the actual carbon sink migration structure in urban green land. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art, and an urban green land carbon sink intelligent regulation system and method based on multi-source data fusion are proposed.

[0006] In order to achieve the above object, the present application adopts the following technical scheme: the urban green land carbon sink intelligent regulation and control system based on multi-source data fusion comprises:

[0007] The soil state identification module is used for obtaining green land cover layer differentiated depth soil sample points, extracting osmotic pressure trajectory and locating turning points, comparing vertical variation trend of soil organic carbon content and position of turning points, identifying consistent horizon, and obtaining vertical carbon response area;

[0008] The flux intervention identification module is used for selecting carbon flux sensing points based on the vertical carbon response area, extracting flux segments before and after disturbance and judging whether it is sustained fluctuation, analyzing time axis offset state of the baseline segment before disturbance, and obtaining flux recovery abnormal block;

[0009] The change triggering module is used for extracting carbon input direction, flux fluctuation segment and disturbance frequency based on the flux recovery abnormal block, aligning trend offset position, analyzing change node and offset point overlap section, and obtaining change response area;

[0010] The green land structure merging module is used for extracting connected green land segments based on the change response area, identifying spatial connection relationship and boundary transition characteristics between segments, labeling interference trajectory intersection area, and obtaining continuous area set;

[0011] The regulation and control path derivation module is used for reading carbon input direction and migration trend line based on the continuous area set, identifying connection position extension relationship, analyzing consistent direction of advancing trajectory, and obtaining carbon sink regulation and control path paragraph.

[0012] As a further scheme of the present application, the vertical carbon response area comprises osmotic pressure turning point position, carbon concentration variation trend line and continuous consistent change horizon label, the flux recovery abnormal block comprises non-steady-state fluctuation segment, baseline flux segment offset position and abnormal duration, the change response area comprises carbon input trend line, disturbance frequency sequence and synchronous change area, the continuous area set comprises green land segment set, edge interference intersection area and surface cover change label, and the carbon sink regulation and control path paragraph comprises carbon input direction sequence, segment connection extension relationship and space advancing structure.

[0013] As a further scheme of the present application, the soil state identification module comprises:

[0014] The sample extraction sub-module is used for obtaining soil sample points with differentiated depth in urban green land partition surface cover layer, layer-by-layer acquisition and depth labeling, extracting pore water pressure change information corresponding to each layer, and obtaining multi-layer soil osmotic pressure information set;

[0015] The turning point recognition submodule is configured to recognize a water potential trajectory and draw a change path based on the set of water potential information of the multiple layers of soil, mark positions where a change in a water potential state occurs in each layer, extract a turning point corresponding region in the trajectory, and obtain a water potential turning point position identifier.

[0016] The horizon matching submodule is configured to extract a change trend of soil organic carbon content in the vertical direction of the corresponding depth layer based on the water potential turning point position identifier, carry out spatial alignment of the change trend of soil organic carbon content and the water potential turning point, mark a horizon region with consistent change directions, and obtain a vertical carbon response region.

[0017] As a further scheme of the present application, the flux intervention identification module comprises:

[0018] The sensing point selection submodule is configured to select a carbon flux sensing point arranged in a coverage range based on the vertical carbon response region, extract flux observation sequence and disturbance correlation time information recorded by the sensing point, correlate the sensing point and the region distribution position, and obtain a flux sensing point distribution map.

[0019] The flux fluctuation extraction submodule is configured to extract a flux stable segment before disturbance and a flux change segment after disturbance based on the flux sensing point distribution map, identify whether the flux change after disturbance is continuously in a non-steady state, and obtain a non-steady state flux segment sequence.

[0020] The abnormal block positioning submodule is configured to compare a position of the flux stable segment before disturbance on a time axis, judge a time offset relationship between the flux stable segment and the fluctuation segment, circumscribe a region with a continuous flux offset performance, and obtain a flux recovery abnormal block.

[0021] As a further scheme of the present application, the change triggering module comprises:

[0022] The trend extraction submodule is configured to read a carbon input change direction, a flux fluctuation segment, and a soil disturbance frequency sequence in the region based on the flux recovery abnormal block, track a dynamic process of the change content on a time axis, obtain time distribution characteristics of a carbon input line, a flux change line, and a disturbance sequence, and obtain a time series change trend combination map.

[0023] The node comparison submodule is configured to align times of a carbon input trend line and a flux change line based on the time series change trend combination map, identify a change node in the disturbance frequency sequence, mark an offset position in the trend line, calculate an occurrence frequency of the offset point in a time segment, screen a time segment synchronized with the disturbance node, and obtain a synchronized offset time segment set.

[0024] The slice marking submodule is configured to locate the corresponding spatial position in the time-coincident slice based on the set of synchronous offset time slices, filter the spatial range in which the carbon input, flux change and disturbance node continuously appear in the same area, and obtain the change response area.

[0025] As a further scheme of the present application, the calculation formula of the occurrence frequency of the offset point in the time slice is specifically:

[0026] ;

[0027] Wherein, represents the occurrence frequency of the offset point in the time slice, represents the total number of trend offset points identified in the current time slice, represents the time stamp of the first carbon input trend line offset point, represents the time stamp of the first flux change trend line offset point, represents the time stamp of the first carbon input trend line offset point, represents the time stamp of the first flux change trend line offset point, represents the difference between the carbon input values at the two time points before and after the first offset point in the carbon input trend line, represents the disturbance response weight coefficient, represents the slice disturbance background density correction term, represents the index number of the time slice in the time sequence distribution.

[0028] As a further scheme of the present application, the green space structure merging module comprises:

[0029] The slice extraction submodule is configured to extract the urban green space slice connected with the area based on the change response area, read the spatial connectivity width information and locate the arrangement position of the slice, identify the contact relationship between adjacent slices, and obtain the green space slice connection graph.

[0030] The boundary arrangement submodule is configured to read the arrangement order of the slice boundary line based on the green space slice connection graph, extract the corresponding land cover type sequence, mark the position where the land surface type changes in the boundary range, calculate the repetition ratio of the land cover type between adjacent boundary segments, identify the regional boundary of the continuity between slices, and obtain the spatial continuous cover structure.

[0031] The interference identification submodule is configured to locate the change slice in the edge area based on the spatial continuous cover structure, identify the staggered distribution of human interference tracks in the boundary area, filter the spatial range where the interference tracks interact with each other, and obtain the continuous slice set.

[0032] As a further scheme of the present application, the calculation formula of the repetition ratio of the land cover type between adjacent boundary segments is specifically:

[0033] ​ ;

[0034] representative segment between segments repetition ratio of land cover types, representative segment number of pixels under land cover type representative segment number of pixels under land cover type representative segment representative segment adjacent boundary segment shared length ratio, representative segment intersection area disturbance frequency of segment representative segment green cover area of segment representative segment green cover area of segment representative segment total length of boundary line segment of segment representative segment total length of boundary line segment of segment representative segment elevation value at boundary elevation sampling point representative segment elevation value at boundary elevation sampling point representative segment

[0035] As a further scheme of the present application, the control path derivation module comprises:

[0036] a direction extraction submodule, configured to, based on the continuous patch set, in accordance with a carbon input direction arrangement order in each green patch segment, and according to a spatial trend of a carbon migration trend line segment, continue a direction flow of a carbon input path to obtain a carbon input direction sequence;

[0037] a connection relationship construction submodule, configured to, based on the carbon input direction sequence, in accordance with a spatial position relationship between adjacent patch segments, track a direction extension manner between connection points, track a connection condition of carbon migration directions between patch segments, and obtain a patch extension structure diagram;

[0038] ​​​​​The path structure identification sub-module is used for following the direction consistency process of the space advancing track, extending the space structure of the advancing track in the continuous segment based on the segment extension structure diagram, locking the path track of the direction continuous feature, and obtaining the carbon sink regulation path paragraph.

[0039] The urban green land carbon sink intelligent regulation method based on multi-source data fusion comprises the following steps:

[0040] S1: Obtain soil sample points of differentiated depth of surface cover layer of urban green land partition, record the osmotic pressure change track of each layer of soil and locate the osmotic pressure change turning point, obtain the carbon concentration change trend at the corresponding depth, compare the vertical variation trend of soil organic carbon content with the spatial position of the turning point, and obtain the vertical carbon response area;

[0041] S2: Based on the vertical carbon response area, select the carbon flux sensing points arranged in the coverage range, extract the flux stable section before disturbance and the flux change section after disturbance, judge whether the flux after disturbance exists continuous non-steady-state fluctuation performance, and obtain the flux recovery abnormal block;

[0042] S3: Based on the flux recovery abnormal block, extract the carbon input change direction, flux fluctuation section and soil disturbance frequency sequence in the region, compare the carbon input trend line and the flux change line on the time axis, analyze whether the change nodes of the disturbance frequency and the offset points in the two trend lines overlap in time, mark the continuously occurring synchronous change area, and obtain the change response area;

[0043] S4: Based on the change response area, extract the connected urban green land segment, read the space connected width information and record the arrangement order of the green land boundary line, extract the surface cover type sequence corresponding to the segment and mark the change position in the space continuous segment, and obtain the continuous area set;

[0044] S5: Based on the continuous area set, read the arrangement order of the carbon input direction and the carbon migration trend line segment in each green land segment, extract the extension relationship of the connection position between the segments, analyze the consistency performance of the advancing track between adjacent segments in the space direction, and obtain the carbon sink regulation path paragraph.

[0045] Compared with the prior art, the advantages and positive effects of the present application are that:

[0046] 1. By spatially matching the osmotic pressure trajectory with the vertical variation trend of soil organic carbon content, the vertical carbon response layer boundary is delineated. Combined with the fluctuation characteristics before and after the disturbance of the flux and the offset relationship of the carbon input trend on the time axis, the abnormal area that synchronously responds under the influence of disturbance is extracted. The disturbance frequency and the trend offset position are superimposed to identify the response area. According to the consistency of the boundary connection structure between green space segments and the carbon migration direction, the spatial extension and the coherent advancing characteristics of the green space segment are integrated to construct a carbon sink path paragraph with flux fluctuation response ability, disturbance path identification ability and structural advancing characteristics, and to enhance the intervention directivity and structural linkage in the regulation of urban green space.

[0047] 2. The change triggering module analyzes the time synchronization of carbon input trend, flux fluctuation and disturbance frequency, and quantifies the disturbance influence intensity by combining the offset point frequency calculation formula. Compared with the traditional method which only relies on the static division of the continuity of the coverage type, this system can dynamically track the distribution of the disturbance trajectory in the interlaced area of the green space edge, avoiding the omission of carbon input abnormalities caused by high-frequency disturbance.

[0048] 3. The system realizes the deep layer monitoring of soil carbon dynamics through the hierarchical identification of vertical carbon response area (combined with the soil osmotic pressure turning point and the variation trend of organic carbon content). At the same time, through the time axis offset analysis of the disturbance before and after by the flux intervention identification module, the timing characteristics of carbon flux anomaly are accurately captured, solving the problem of "heavy static zoning, light dynamic timing correlation" in traditional technology, which makes the spatial resolution of carbon sink monitoring refined to layer scale, and the time response accuracy improved to minute level of disturbance event correlation. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The system flowchart of the present application;

[0050] Figure 2 The system block diagram of the present application;

[0051] Figure 3 The method step flowchart of the present application;

[0052] Figure 4 The soil state identification module step flowchart of the present application;

[0053] Figure 5 The flux intervention identification module step flowchart of the present application;

[0054] Figure 6 The change triggering module step flowchart of the present application;

[0055] Figure 7 The green space structure merging module step flowchart of the present application;

[0056] Figure 8 A control path derivation module step flow chart of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0058] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0059] Please refer to Figure 1 The urban green land carbon sink intelligent control system based on multi-source data fusion comprises:

[0060] A soil state identification module is configured to obtain soil samples of different depths in the surface cover layer of the urban green land partition, extract the osmotic pressure change trajectory of each layer and locate the osmotic pressure turning point, extract the change trend of soil organic carbon content in the vertical direction and spatially align the osmotic pressure turning point, label the layer region with consistent continuous change direction, and obtain the vertical carbon response region.

[0061] A flux intervention identification module is configured to select carbon flux sensing points set in the coverage range based on the vertical carbon response region, extract the flux stable section before disturbance and the flux change section after disturbance, judge whether the flux after disturbance has a sustained non-steady state fluctuation, compare the offset state of the fluctuation section and the flux reference section on the time axis, and obtain the flux recovery abnormal block.

[0062] A change triggering module is configured to extract the carbon input change direction, flux fluctuation section and soil disturbance frequency sequence in the region based on the flux recovery abnormal block, compare the carbon input trend line and the flux change line on the time axis, analyze whether the change nodes of the disturbance frequency and the offset points in the two trend lines overlap in time, mark the continuously occurring synchronous change area, and obtain the change response region.

[0063] The green space structure merging module is configured to extract urban green space segments connected with the region based on the change response region, read spatial connectivity width information and record the arrangement order of the green space boundary line, extract a ground cover type sequence corresponding to the segment and mark a change position in the spatial continuous segment, identify an interlaced distribution range of human disturbance trajectories in the edge region, obtain a continuous segment set, and deliver the continuous segment set to the regulation path derivation module;

[0064] The regulation path derivation module is configured to read an arrangement order of carbon input directions and a carbon migration trend line segment in each green space segment based on the continuous segment set, extract an extension relationship of a connection position between segments, analyze a consistency performance of a pushing trajectory between adjacent segments in a spatial direction, mark a spatial pushing structure of a coherent direction feature, and obtain a carbon sink regulation path paragraph.

[0065] The vertical carbon response region includes a osmotic pressure turning point position, a carbon concentration change trend line, and a continuous consistent change horizon marker, the flux recovery abnormal block includes a non-steady-state fluctuation section, a baseline flux segment offset position, and an abnormal duration, the change response region includes a carbon input trend line, a disturbance frequency sequence, and a synchronous change segment, the continuous segment set includes a green space segment set, an edge disturbance interlaced region, and a ground cover change marker, and the carbon sink regulation path paragraph includes a carbon input direction sequence, a segment connection extension relationship, and a spatial pushing structure.

[0066] Please refer to Figure 1 、 Figure 2 and Figure 4 The soil state identification module includes:

[0067] The sample extraction sub-module is based on soil samples of different depths in the urban green space partition ground cover layer, obtains and labels depths in layers, extracts corresponding pore water pressure change information of each layer, and obtains a multi-layer soil osmotic pressure information set.

[0068] First, according to the different green space types in the geographical zoning map, the area is divided, and representative sampling points are selected in each area. According to the existing land use classification data and field topographic survey data, the green space boundary is manually calibrated, and combined with the elevation distribution information, the sample points are divided into three depth levels: shallow (0-10 cm), middle (10-30 cm), and deep (30-60 cm). Each layer of samples is labeled in a numbered management manner, recording the sample number, sampling depth, and geographic coordinates of the sample point. Then, a tensiometer or equivalent pressure sensing device is installed on each depth layer to collect continuous data on the change of pore water pressure over time with a time resolution of 30 minutes per group. At the same time, environmental disturbances such as human pressure, rainfall processes, etc. are marked and excluded from the interference section. After removing abnormal sections from the original data, a table of osmotic pressure change time series data for each layer of soil is established. For example, in a certain area, the pore water pressure of the middle layer sample point decreases from 9.6 kPa to 6.8 kPa in one day, and then rises to 11.2 kPa in 4 hours. Record it as three stages in this sample point, and store it in the form of sample number A12-2, middle layer identification, and pressure sequence P(t). In addition, the pressure sequence data is standardized, a uniform time step is set, and missing values are filled in using linear interpolation. The uniform output interval is every 30 minutes for equally spaced data sequences. For multiple point data in the same layer in the sampling area, the average value is combined to form a representative curve. For example, the pressure values of four points in the same layer in a certain area at the same time are 8.1, 8.4, 7.9, and 8.0 kPa, respectively. The average pressure at that time is 8.1 kPa. Finally, the data of each sampling area at different depths is output as a data matrix containing time stamp, layer, and pressure value. The output file is named uniformly with geographic number, such as GDL-01-PZ.csv, for subsequent comparison and processing. Finally, a multi-layer soil osmotic pressure information set is obtained.

[0069] The turning recognition submodule identifies the osmotic pressure trajectory and draws the change path based on the multi-layer soil osmotic pressure information set, marks the position where the osmotic pressure state changes direction in each layer, extracts the region corresponding to the turning point in the trajectory, and obtains the osmotic pressure turning position identifier.

[0070] First, the time series data of each depth layer is read layer by layer, and the pore water pressure value of each sampling point at the standard time interval is read one by one, and the curve is drawn with the sampling time as the horizontal axis and the pressure value as the vertical axis to form a continuous pressure trajectory graph. In the graph, observe the trend of the value change, for example, the pressure values of the shallow layer point in area A from the 1st day to the 3rd day are 7.8, 8.1, 9.3, 9.8, 9.7, 9.0, and 8.4 kPa, respectively. The pressure change curve of this point can be drawn, and the trend of the 4th day changes from rising to falling. Identify the period when the change direction changes from one-way to reverse, and through scanning the relationship between each data point and its adjacent two points in the time series, perform three-point recursive judgment. If the current data point value is greater than the previous day and less than the next day, it is judged as a turning point. If the difference between the change amounts before and after is less than 0.2 kPa, it is not listed as a turning point. The identification of the turning point needs to be continuously judged and the short-period repeated jitter situation needs to be excluded. When the turning point appears in the middle of the continuous fluctuation segment, the oscillation influence needs to be excluded and the first deviation node of the trend change needs to be retained. For example, in the middle layer sample point in area B, the daily sequence is 10.2, 10.4, 10.1, 9.9, 10.0, 10.3, and 10.6 kPa. 10.4 turns to 10.1, which is considered as a downward turning point. The subsequent rise to 10.6 is not counted as a turning point. Based on the first trend change point, a stable annotation path is formed. The extracted turning nodes are marked on the curve in the form of point position, and a polyline path graph is formed by connecting each continuous segment. For each depth layer sample point, repeat the process, mark the complete path on the graph, and record the turning time and corresponding depth. Finally, the pressure turning position identification is obtained.

[0071] The layer matching sub-module extracts the vertical variation trend of soil organic carbon content based on the pressure turning position identification, and carries out spatial alignment of the soil organic carbon content variation trend and the pressure turning point. The layer region with consistent continuous variation direction is marked to obtain the vertical carbon response region.

[0072] First, the pore pressure monitoring data at every 10 cm interval in the profile needs to be extracted, and the pore pressure variation trend is analyzed layer by layer downward. The pore pressure difference between each layer and the next layer is calculated in sequence according to the depth order. The position of the pressure gradient mutation is determined by calculating the interlayer difference of the pore pressure value. For example, if the pore pressure is 38 kPa at 4.5 m and 35 kPa at 4.6 m, the interlayer difference is -3 kPa. When the pore pressure difference between two consecutive layers changes from positive to negative or from negative to positive, and the mutation amplitude is greater than 1.5 kPa, the point is marked as the breakthrough transition position. The reference value for identifying the mutation is set for sandy soil and clay soil respectively. In sandy soil, the difference value exceeding 3 kPa can be set as the basis for judgment, while in clay soil, the value can be set to 1 kPa. Subsequently, taking the depth of the transition point as the center, the soil organic carbon content data in the depth interval is extracted, which is listed in the order of depth. For example, the organic carbon content at 4.3 m, 4.4 m, 4.5 m, 4.6 m and 4.7 m is 7.8, 8.1, 8.6, 9.0 and 9.3 g per kg respectively. The change direction can be compared in sequence, and the increase or decrease of the content between each layer and the next layer is recorded as positive or negative value. When the change direction of three or more consecutive layers is the same, such as all increasing values, i.e. the carbon content of each layer is higher than that of the previous layer, it indicates that the region has a continuous and consistent carbon rising trend. Conversely, if all the values are decreasing, it indicates a downward trend. Then, the spatial position of these continuous trend regions and the section where the breakthrough mutation point is located is compared. When they overlap or are adjacent in space, for example, the mutation point is located at 4.5 m, and the consistent change trend appears between 4.3 m and 4.7 m, it indicates that the mutation point and the change of soil carbon content have a consistent response relationship. Finally, the vertical carbon response region is obtained.

[0073] Please refer to Figure 1 , Figure 2 and Figure 5 , the flux intervention identification module comprises:

[0074] The induction point selection submodule selects the carbon flux induction points set in the coverage range based on the vertical carbon response region, extracts the flux observation sequence and interference association time information recorded by the induction points, and associates the induction points with the regional distribution position to obtain the flux induction point distribution map.

[0075] Firstly, the boundary coordinate range of the area in the geographical space is determined, the coverage grid number of the response area in the urban green space system is extracted from the land vector diagram, and this is used as the retrieval condition to retrieve the carbon flux sensing points that have been set in the range one by one. The sensing points are recorded in the form of numbers and correspond to their physical coordinate positions. For example, the coordinate of the point with the number QZ-014 is (113.2845, 22.6392), the installation depth is 0.5 meters below the ground surface, the recording interval is once every hour, and the covered flux data is the CO2 flux change rate. After all the point positions are extracted, the point position distribution map is overlaid with the spatial boundary of the vertical carbon response area, and the points completely within the boundary are retained. If the point position is located at the boundary (i.e., there is an intersection between the 5-meter buffer range of the point position and the boundary of the area), it is listed as an edge sensing point. By reading the original recording data of the sensing point, the flux observation value and the corresponding recording time in each time period are extracted. For example, a point records 48 groups of flux data from 10:00 on October 12 to 10:00 on October 13, and the data structure is (time, flux value). Then, the disturbance correlation time information is compared through the event log. That is, the artificial disturbance records recorded in the city operation records, such as pruning, cleaning, and crowd activities, are combined. If a record shows that area A was pruned at 14:00 on October 12, it is determined that the disturbance event has a time correspondence relationship with the time period in the sensing point recording data. The flux change before and after the disturbance is selected as the boundary basis for subsequent extraction sections. Finally, according to the number, area number, spatial coordinates, sampling depth, and flux data effective start and end time of each sensing point, a spatial positioning map of all flux points in the area is constructed, and the disturbance event time label is overlaid to obtain the flux sensing point distribution map.

[0076] The flux fluctuation extraction submodule extracts the flux stable section before the disturbance occurs and the flux change section after the disturbance based on the flux sensing point distribution map, identifies whether the flux change after the disturbance is continuously in a non-steady state, and obtains a sequence of non-steady state flux sections.

[0077] First, take a single sensing point as the smallest unit, corresponding to the time label of its interference event, intercept the complete flux observation data within the range of 24 hours before the disturbance to 48 hours after the disturbance, the flux data structure is a sequence of (time, flux value), for example, the record of point QZ-014 is the CO2 flux value from 10:00 on October 12 to 10:00 on October 14, and the disturbance time occurs at 08:00 on October 13, then segment the time series according to the disturbance, the front segment is defined as "steady segment", and the rear segment is defined as "change segment", in the steady segment, filter the segment with a fluctuation range less than ±0.8 within 6 hours, and further confirm that the standard deviation of the flux value in the segment does not exceed 0.6, the segment that meets the above conditions is classified as a flux reference segment, for example, the record values from 16:00 to 22:00 on October 12 are 8.2, 8.3, 8.1, 8.0, 8.1, 8.3, the difference interval is 0.3, and the standard deviation is 0.11, so this segment is a steady segment, then monitor the flux change segment in the region after the disturbance occurs hour by hour, judge whether the flux value exists in a state of continuous rise or fall within 12 hours, and evaluate whether the fluctuation value in the same segment exceeds the maximum and minimum boundary range of the previous steady segment, if the flux value deviates from the range by more than ±1.2 and lasts for more than 3 hours, it is determined as a non-steady fluctuation segment, the judgment standard can be set as "deviation threshold" 1.2, the average flux fluctuation tolerance interval from the green land sample, if the flux value at point QZ-014 reaches 9.8, 10.3, 10.6, 10.9, 11.1 continuously within 12 hours after the disturbance, the segment is marked as a non-steady segment, in addition, short-term disturbances caused by climate factors need to be excluded, and the rainfall and wind speed changes of the day need to be compared, if the rainfall exceeds 5mm or the wind speed increases by more than 2.5m / s, the segment data is temporarily excluded from the identification, finally, all flux non-steady change segments that pass the judgment are numbered according to the time sequence and archived to obtain the non-steady flux segment sequence.

[0078] The abnormal block positioning sub-module is based on the non-steady flux segment sequence, compares the position of the flux steady segment before the disturbance on the time axis, judges the time offset relationship between the fluctuation segment, circulates the region with continuous flux offset performance, and obtains the flux recovery abnormal block;

[0079] First, extract each identified flux non-steady segment, corresponding to its induction point number, occurrence time, flux value offset direction and amplitude, and locate its time period on the unified time axis, for example, point QZ-014 identifies non-steady segment time as October 13, 09:00 to 16:00, the flux change value increases from 8.1 to 11.5, and then falls to 9.3, which is set as the non-steady identification segment, and the stable segment time period is extracted from the pre-recorded record of the same induction point, for example, the point 10 / 12 16:00 to 22:00 is the reference stable segment, compare the start and end time points of the two time periods, record the offset start point, duration and end time, compare the offset start time with the disturbance start time in the disturbance event record, if the offset segment starts within 1 hour after the disturbance time, it is marked as a response to the event behavior, and it is confirmed whether the non-steady flux segment appears continuously after the disturbance time and lasts more than 6 hours and does not return to the reference segment fluctuation range, if the flux value deviates from the interval formed by the maximum and minimum values of the reference segment more than ±1.2 and lasts more than 4 hours, for example, the reference interval is 7.9 to 8.4, and the flux value after disturbance is 9.6, 10.2, 10.9, 11.3, all of which are in the offset state and the time is continuous, the segment is judged as an abnormal area, combined with the intersection of the spatial coordinates of the induction point and the vertical carbon response area, all segments that meet the above conditions are superimposed in space to draw the boundary line of the offset range, and the geographical coordinate point set is output in a polygon closed manner in space, finally, the induction point number, abnormal segment start and end time, maximum flux offset, and time interval with disturbance point information are summarized to form a structured annotation list, and a flux recovery abnormal block is obtained.

[0080] Please refer to Figure 1 , Figure 2 and Figure 6 , the change triggering module comprises:

[0081] The trend extraction submodule reads the carbon input change direction, flux fluctuation segment and soil disturbance frequency sequence in the area based on the flux recovery abnormal block, traces the dynamic process of the change content on the time axis, obtains the time distribution characteristics of the carbon input line, flux change line and disturbance sequence, and obtains a time sequence change trend combination diagram;

[0082] First, the delineated abnormal space region is taken as the extraction range, and the carbon input change direction record data of each sample or monitoring unit in it is read in sequence, which needs to come from the vegetation biomass estimation result and the output of the vegetation photosynthetic carbon absorption model. The carbon input direction of each monitoring point on a daily scale is summarized. If the carbon input values of a point from October 10 to October 14 are 3.2, 3.4, 3.5, 3.1, and 2.9 gC / m² / d, respectively, the direction can be identified as first rising and then falling, and the carbon input direction turns on October 12, and a carbon input trend line is drawn. Then, the flux change segment data in the region is extracted, and the continuous record sequence is obtained at each flux sensing point. The rising and falling characteristics and slope trend of the flux change curve in the same interval are compared. For example, the flux record is 7.8, 8.1, 8.4, 9.0, and 9.2, and the change direction is continuously rising. The trend line is formed by the positive slope change line segment from the first day to the fifth day. Then, the soil disturbance frequency record of each monitoring point is associated according to the spatial position, and the record form is the number of human disturbance actions per unit time. For example, the records from October 10 to 14 are 0, 1, 3, 2, and 1 times, respectively. The disturbance frequency curve can be drawn and the disturbance intensive point time node is marked. The three types of data are projected on the same time axis, the horizontal axis is the date, and the vertical axis is the dimensionless classification. The carbon input change is marked by an arrow, the flux change is drawn by a broken line graph, and the disturbance frequency is represented by a column height. Finally, the carbon input direction trend line, the flux change line, and the disturbance column distribution are constructed on the graph Figure Three The time trajectory relationship diagram of the group data is completed, and the unified distribution and cross recognition of the change content are obtained.

[0083] The node comparison submodule aligns the time of the carbon input trend line and the flux change line based on the time sequence change trend combination graph, identifies the change nodes in the disturbance frequency sequence, marks the offset position in the trend line, calculates the occurrence frequency of the offset points in the time segment, filters the time segments synchronized with the disturbance nodes, and obtains the synchronized offset time segment set.

[0084] The calculation formula of the occurrence frequency of the offset points in the time segment is as follows:

[0085] ;

[0086] Wherein, represents the occurrence frequency of the offset points in the time segment, represents the total number of trend offset points identified in the current time segment, represents the timestamp of the i-th carbon input trend line offset point, represents the timestamp of the i-th flux change trend line offset point, represents the timestamp of the i-th carbon input trend line offset point, represents the timestamp of the i-th flux change trend line offset point, represents the timestamp of the i-th carbon input trend line offset point, the difference between the carbon input values of the two time points before and after the offset point, representing the disturbance response weight coefficient, representing the slice disturbance background density correction term, representing the index number of the time slice in the time sequence distribution;

[0087] Assumptions:

[0088] Select the time slice from 00:00 on October 10, 2020 to 24:00 on October 12, and identify 5 trend offset points in this time period, i.e. ;

[0089] The first offset point occurs in the carbon input trend line on October 10th at 14:00, corresponding to 14 hours;

[0090] The corresponding offset point in the flux change trend line is 13 hours, and the offset time difference is 1 hour;

[0091] The region is identified as a mechanical tillage behavior, and the disturbance weight is ;

[0092] The disturbance intensity density of this slice is 0.15 times per square meter per hour, denoted as ;

[0093] At the same time, the carbon input values of the two nodes before and after the offset point in the trend line are 4.7 and 6.3 gC / m², respectively;

[0094] The carbon input change is 1.6 gC / m²;

[0095] denoted as , then the first term is:

[0096] ;

[0097] The second offset point appears in the carbon input trend line at 21 hours:

[0098] The corresponding flux offset point is 20 hours, with an offset difference of 1 hour:

[0099] The disturbance behavior is spraying operation:

[0100] The corresponding disturbance weight is 1.1, the disturbance density is 0.17, and the carbon input change is 2.1 gC / m²;

[0101] Substituting we get:

[0102] ;

[0103] The third offset point appears at 33 hours;

[0104] The flux offset point is 31 hours, with an offset of 2 hours;

[0105] The human activity is lawn mowing:

[0106] The disturbance weight is 0.9, the density is 0.12, and the carbon input difference is 1.3 gC / m²;

[0107] Substituting, we get:

[0108] ;

[0109] The fourth offset point appears at 44 hours;

[0110] The corresponding flux offset point is 45 hours, with an offset of -1 hour;

[0111] The disturbance category is patrol and tread:

[0112] The disturbance weight is 1.0, the density is 0.18, and the carbon input difference is 0.9 gC / m²;

[0113] Substituting, we get:

[0114] ;

[0115] The fifth offset point appears at 56 hours;

[0116] The corresponding flux offset point is 53 hours, with an offset of 3 hours;

[0117] The disturbance behavior is hedge pruning:

[0118] The disturbance weight is 1.5, the density is 0.20, and the carbon input difference is 1.9 gC / m²;

[0119] Substituting, we get:

[0120] ;

[0121] Adding the above five values: 0.851 + 0.679 + 1.429 + 0.885 + 2.848 = 6.692;

[0122] Dividing by the number of offset points, 5, we get the final frequency index:

[0123] ;

[0124] The result shows that the offset response frequency between the carbon input trend line and the flux change line in the set time segment is 1.3384, indicating that the disturbance events in this segment have stable correlation characteristics with carbon flux fluctuations.

[0125] The slice marking sub-module is based on the set of synchronous offset time slices, locates the corresponding spatial position within the time-coincident slice, screens the spatial range in which the carbon input, flux change and disturbance node appear continuously within the same area, and obtains the change response area;

[0126] First, according to the start and end time range of each slice, the carbon input data, flux change data and soil disturbance frequency sequence recorded in each monitoring point are compared one by one, the recorded items overlapping the time period of the slice are extracted, for example, the start and end time of a slice is October 13, 08:00 to October 13, 20:00, then extract the record group in which the carbon input value is greater than the daily average change 0.2gC / m², the flux variation amplitude is more than ±1.2 and the disturbance frequency is greater than 1 in all sensing points within the time period, in the extraction process, it is necessary to check whether the three types of data come from the same spatial unit, that is, the plot or monitoring area number of the sensing point needs to be consistent, when the three types of data cover the same geographic unit and have records in the corresponding time period, it is determined as a coincidence event, the sensing point positions corresponding to these events are superimposed into the GIS spatial layer according to the coordinates, the point positions appearing three coincidences are marked in space, the point positions with a distance less than 10 meters are buffered and merged, the merging method is to take the midpoint as the center, set a 5-meter buffer, form a continuous slice area contour, the merged slices are managed in a continuous numbering manner, for example, ZB-01, ZB-02, etc., then compare whether the start and end time of each slice are continuous through the time sequence axis, if the time interval is not more than 2 hours, merge them into a unified event group, finally mark the boundary lines of all slice areas meeting the time coincidence and spatial overlap conditions on the map, output the boundary coordinate set, start and end time, point position number and event number to the data table, and obtain the change response area.

[0127] Please refer to Figure 1 , Figure 2 and Figure 7 , the green space structure merging module includes:

[0128] The slice extraction sub-module extracts the urban green space slice connected with the area based on the change response area, reads the spatial connectivity width information and locates the arrangement position of the slice, identifies the contact relationship of adjacent slices, and obtains the green space slice connection diagram.

[0129] First, adjacent plots are matched in the urban green space database based on the regional boundary coordinates. Then, the numbered green space fragment layers in the urban park land dataset are retrieved. All green space patches within a 10-meter buffer zone at the edge of overlapping areas are searched. Green space units with an area greater than 50 square meters and covered by grassland, shrubs, or trees are selected as extractable fragments. Their plot numbers, center point coordinates, area values, and shape parameters are recorded. Next, the boundary lines and adjacency data of each fragment are read. Based on the shortest boundary distance and contact boundary length between each patch, it is determined whether they constitute a spatial connectivity relationship. A connectivity threshold is set as a boundary contact length greater than 5 meters or a distance of... A distance of less than 3 meters is considered a connectivity criterion. If the contact boundary length between segment A and segment B is 5.3 meters, they are considered connected segments. The arrangement order is then calculated based on the distance between the center points of adjacent segment pairs. The relative positions are numbered in a north-first, top-down manner to construct an arrangement matrix. The segment numbers connected to each segment are identified in the numbering structure. For example, if segment G1 connects G2 and G3, the arrangement order is G1→G2→G3. Finally, all segment pairs involved in the connectivity determination, their spatial positions, boundary connection states, and numbering relationships are exported in a three-column matrix and a graphical view layer is generated. The output content includes a connection relationship table and spatial position information, resulting in a green space segment connection map.

[0130] The boundary arrangement submodule is based on the green space segment connection map. It reads the arrangement order of the segment boundary lines, extracts the corresponding land cover type sequence, marks the location where the land cover type changes within the boundary range, calculates the repetition ratio of land cover type between adjacent boundary segments, identifies the continuous regional boundaries between segments, and obtains the spatial continuous cover structure.

[0131] The specific formula for calculating the overlap ratio of land cover types between adjacent boundary segments is as follows:

[0132] ;

[0133] Representative excerpt With fragments The percentage of overlap between land cover types Representative excerpt In land cover type The number of pixels below Representative excerpt In land cover type The number of pixels below Represents the number of all land cover types. Representative excerpt With fragments The percentage of shared length between adjacent boundary segments Representative excerpt With fragments The frequency of disturbances in the boundary area Representative excerpt Green space coverage area Representative excerpt Green space coverage area Representative excerpt The total length of the boundary line segment, Representative excerpt The total length of the boundary line segment, Representative excerpt At the boundary elevation sampling point The elevation value at that location, Representative excerpt At the boundary elevation sampling point The elevation value at that location, This represents the number of points used to sample the boundary elevation values ​​of the segment.

[0134] Assumption:

[0135] Number of land surface types These are grassland, woodland, and hard cover, respectively.

[0136] Number of pixels:

[0137] ;

[0138] (The length of the common boundary of the fragment accounts for 60% of the total length).

[0139] (A total of 5 instances of artificial interference occurred at the boundaries of fragments in the historical record).

[0140] (Area unit: square meters);

[0141] (Boundary line length unit: meters);

[0142] Number of elevation sampling points The elevations are as follows:

[0143] ;

[0144] ;

[0145] ;

[0146] Calculate the sum of coverage type differences:

[0147] ;

[0148] Calculate the disturbance area term:

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] Calculate the boundary length difference:

[0154] ;

[0155] Calculate the total elevation difference:

[0156] ;

[0157] Substitute the formula:

[0158] ;

[0159] The results show that the fragment The degree of repetition of the surface cover type distribution with the fragment is 2.043, which is used as the basis for determining whether the two constitute a continuous fragment boundary in the subsequent. If the proportion exceeds the cover threshold of regional continuity determination, it indicates that the two have associated continuity and can be used for subsequent merging judgment.

[0160] The interference identification submodule is based on the spatial continuous coverage structure, locates the change fragments of the edge region, identifies the interlaced distribution of human interference trajectories in the boundary region, filters the spatial range of interference trajectory interaction concentration, and obtains a continuous area set;

[0161] First, the edge extraction process is performed on the constructed green space fragment connection graph, all fragment numbers located at the outer edge of the spatial structure are identified, and the fragment set within a 10-meter wide contour is determined as the edge fragment candidate area through the boundary extraction algorithm. Then, the ground surface type change of each fragment in the candidate area is monitored, and the temporal remote sensing image data is used to determine whether the ground cover in the boundary area has changed within a month. If fragment A is a tree at T1 and bare land at T2, the change state is recorded as "tree bare land", and it is counted as an explicit interference event. The changed fragment is recorded in the change fragment table. Then, the human activity trajectory data in the city management record is called. The trajectory data is stored in the form of trajectory point sequence, recording personnel number, timestamp, positioning coordinates, activity label, etc. All trajectories that pass through the above change fragment buffer zone are selected, the number of trajectory intersections and the direction are counted, for example, trajectory T001 enters fragment G5 at 14:20 on October 15, and leaves at 14:26, the crossing direction is east to west, which is recorded as 1 time and the direction is E→W. After repeating the processing of all trajectory points, the trajectory crossing frequency map is established. If a fragment boundary area has more than 3 trajectory intersections within 3 consecutive days, it is determined to be an interference intersection area. The fragment boundary lines that meet the condition are extracted and a buffer zone is constructed, and the buffer width is set to 5 meters. After superimposition, the continuous interference area layer is formed. The fragments corresponding to these continuous interference ranges are merged into groups, and the attribution numbers are divided according to the spatial proximity. The fragment list, boundary coordinate set and time label of the interference space set are output, and the continuous fragment area set is obtained.

[0162] Please refer to Figure 1 , Figure 2 and Figure 8 , the control path derivation module comprises:

[0163] The direction extraction submodule is based on the continuous fragment area set, and in accordance with the arrangement order of carbon input direction in each green space fragment, the spatial trend of carbon migration trend line segment is obtained, the direction flow of carbon input path is continued, and the carbon input direction sequence is obtained.

[0164] Firstly, the recorded carbon input direction data in each green space segment is called, which is composed of time series carbon input curve generated by vegetation coverage change rate, aboveground biomass increment and meteorological driving factors. The data is read in order according to the photo segment number, and the time interval is set in days. For example, the carbon input direction of segment G01 from October 1 to October 5 is north, northeast, east, southeast, which shows that the path tends to eastward deviation. Then, the arrangement of each segment in the two-dimensional plane is determined according to the physical boundary and coordinates of each segment, and the spatial trend of the carbon migration trend line segment is calculated by combining the start and end coordinates. The angle difference between the migration directions of multiple consecutive segments is taken as the judgment condition. If the included angle between the migration line segments of adjacent segments is less than 45°, it is considered that the direction continuity is good. The segment sequence is arranged in order with the starting segment as the first number to generate a complete path number sequence, for example, G01→G03→G06→G08 constitutes a main path line. Then, the arrow information of carbon input direction is combined with the spatial path to draw the input direction distribution on each migration path in a graphical way. If the direction of a segment is reversed in the path, for example, the overall path is southeast, and the direction of a segment is northwest, it is marked as a direction mutation point and recorded for subsequent investigation. Finally, a multi-field data set composed of segment number, spatial sequence, input direction and trend continuity state is obtained to get the carbon input direction sequence.

[0165] The connection relationship construction submodule is based on the carbon input direction sequence, and tracks the direction extension mode between the connection points according to the spatial position relationship between adjacent segments, and tracks the connection of carbon migration direction between segments to obtain a segment extension structure diagram.

[0166] Firstly, the geometry information of each greenfield fragment in the spatial layer is called, the center point coordinates and the start and end coordinates of the boundary line segment are extracted, the center points of adjacent fragments are connected according to the numbering order, the basic connection line segment set is constructed, for each pair of adjacent fragments, the azimuth angle of the line between the two center points is calculated as the reference of the spatial position relationship between the fragments, for example, the center point of fragment A is (114.5023, 30.6132), the center point of fragment B is (114.5041, 30.6146), and the azimuth angle is northeast by east, then according to the output direction of A and the input direction of B in the carbon input direction sequence, they are marked as east and northeast respectively, by comparing the included angle of the two direction vectors, if the included angle is less than 30 degrees, it is judged that the direction is consistent, and marked as direction continuity, otherwise, it is marked as direction jump, at the same time, whether there is actual connection data of carbon migration trend line between the connection points is tracked, if there is continuous carbon concentration migration trajectory line between the two fragments and the start and end points coincide with the above connection points, the line segment is judged as direction continuity, the fragment pairs meeting the spatial position, direction consistency and migration path continuity are classified as effective connection group, the process is repeated for all adjacent fragments, and the connection pair information between all fragments is recorded in matrix form, including start fragment number, target fragment number, connection state mark, direction included angle value, trend line number and other field contents, finally the matrix data is imported into the visual layer, the direction flow and connection trajectory are identified by arrows and line segments, and the fragment extension structure diagram is obtained.

[0167] The path structure recognition submodule is based on the fragment extension structure diagram, follows the direction consistency process of the spatial propulsion trajectory, extends the spatial structure of the propulsion direction in the continuous fragments, locks the path direction of the direction continuity feature, and obtains the carbon sink regulation path paragraph;

[0168] First, the direction continuity of the established connection line segments in the figure is determined, the carbon input direction, spatial trend and connection angle parameters of the starting segment and target segment contained in each connection path are extracted, these segment pairs are sorted from high to low according to the direction similarity value, and the segment sequence with a direction included angle not exceeding 30 degrees is retrieved as the potential propulsion path starting point. After determining the first segment, the direction vector of the subsequent segment in the connection relationship is sequentially called back, and the direction consistency of each segment is compared one by one. If the included angle fluctuation between three consecutive segments is within 20 degrees, the segment is judged as a direction stable segment, which is included in the propulsion path. Otherwise, if the included angle between the direction of a certain segment and the direction of the previous segment is greater than 45 degrees, the propulsion chain is interrupted, and a new path branch is started. When there are multiple path branches in the same area, record the total segment number and total length of each branch path. If the total length of a path exceeds 200 meters and the number of segments is not less than 4, it is identified as the main propulsion path. Other paths are marked as secondary propulsion segments. Finally, all path lines are superimposed on the map to identify the main path and secondary path numbers, length, connection node number and direction sequence information, and output in the form of a topological structure diagram to obtain the carbon sink control path paragraph.

[0169] Please refer to Figure 3 , the intelligent control method of urban green land carbon sink based on multi-source data fusion, comprising the following steps:

[0170] S1: Obtain soil samples of the differentiated depth of the surface cover layer of the urban green land partition, record the osmotic pressure change trajectory of each layer of soil and locate the osmotic pressure change turning point, and obtain the carbon concentration change trend at the corresponding depth. Compare the vertical carbon content change trend with the spatial position of the turning point to obtain the vertical carbon response area.

[0171] S2: Based on the vertical carbon response area, select the carbon flux sensing points set in the coverage range, extract the flux stable segment before disturbance and the flux change segment after disturbance, and judge whether the flux after disturbance exists continuous non-steady fluctuation. Obtain the flux recovery abnormal block.

[0172] S3: Based on the flux recovery abnormal block, extract the carbon input change direction, flux fluctuation segment and soil disturbance frequency sequence in the region, compare the carbon input trend line and the flux change line on the time axis, analyze whether the change nodes of the disturbance frequency and the offset points in the two trend lines overlap in time, mark the continuously changed synchronous area, and obtain the change response area.

[0173] S4: Based on the change response area, extract the connected urban green land segments, read the spatial connected width information and record the arrangement order of the green land boundary line, extract the surface cover type sequence corresponding to the segments and mark the change position in the spatial continuous segment, and obtain the continuous area set.

[0174] S5: Based on the set of continuous patches, reading the arrangement order of carbon input direction and carbon migration trend line segment in each green patch segment, extracting the extension relationship of the connection position between the segments, analyzing the consistency performance of the advancing track between the adjacent segments in the spatial direction, and obtaining the carbon sink regulation path paragraph.

[0175] The above merely describes the preferred embodiments of the present application, but is not intended to limit the present application in other forms. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. An urban green space carbon sink intelligent regulation system based on multi-source data fusion, characterized in that: The system comprises: The soil state recognition module is used for obtaining differentiated depth soil samples of green land cover layer, extracting osmotic pressure trajectory and locating turning points, comparing vertical variation trend of soil organic carbon content and turning point position, identifying consistent direction horizon, and obtaining vertical carbon response area; The flux intervention identification module is used for selecting carbon flux sensing points based on the vertical carbon response area, extracting flux segments before and after disturbance and judging whether there is continuous fluctuation, analyzing time axis offset state of the baseline segment before disturbance, and obtaining flux recovery abnormal block; The change triggering module is used for extracting carbon input direction, flux fluctuation segment and disturbance frequency based on the flux recovery abnormal block, aligning trend offset position, analyzing change node and offset point overlap section, and obtaining change response area; The green land structure merging module is used for extracting connected green land segments based on the change response area, identifying spatial connection relationship and boundary transition characteristics between segments, labeling disturbance trajectory intersection area, and obtaining continuous area set; The regulation path derivation module is used for reading carbon input direction and migration trend line based on the continuous area set, identifying connection position extension relationship, analyzing consistent direction of advancing trajectory, and obtaining carbon sink regulation path paragraph. The vertical carbon response area comprises osmotic pressure turning point position, carbon concentration variation trend line and continuous consistent change horizon label, the flux recovery abnormal block comprises non-steady-state fluctuation segment, baseline flux segment offset position and abnormal duration, the change response area comprises carbon input trend line, disturbance frequency sequence and synchronous change area, the continuous area set comprises green land segment set, edge disturbance intersection area and surface cover change label, and the carbon sink regulation path paragraph comprises carbon input direction sequence, segment connection extension relationship and spatial advancing structure. 2.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion according to claim 1, characterized in that, The soil state recognition module comprises: The sample extraction submodule is used for obtaining differentiated depth soil samples in urban green land partition surface cover layer, layered acquisition and depth labeling, extracting pore water pressure change information corresponding to each layer, and obtaining multi-layer soil osmotic pressure information set; The turning identification submodule is used for identifying osmotic pressure trajectory and drawing change path based on the multi-layer soil osmotic pressure information set, labeling position of osmotic pressure state direction change in each layer, extracting turning corresponding area in the trajectory, and obtaining osmotic pressure turning position label; The horizon matching submodule is used for extracting vertical variation trend of soil organic carbon content corresponding to the depth layer based on the osmotic pressure turning position label, carrying out spatial alignment of soil organic carbon content variation trend and osmotic pressure turning point, labeling horizon area with continuous change consistent direction, and obtaining vertical carbon response area. 3.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion of claim 1, characterized in that, The flux intervention identification module comprises: The sensing point selection submodule is used for selecting carbon flux sensing points arranged in the coverage range based on the vertical carbon response area, extracting flux observation sequence and disturbance correlation time information recorded by the sensing points, correlating the sensing points and area distribution position, and obtaining flux sensing point distribution map; The flux fluctuation extraction submodule is configured to extract a flux stable section before disturbance and a flux change section after disturbance based on the flux sensing point distribution map, identify whether the flux change after disturbance is continuously in a non-steady state, and obtain a non-steady flux section sequence. The abnormal block positioning submodule is configured to compare a position of the flux stable section on a time axis before disturbance with a time offset relationship between the disturbance and the fluctuation section, circumscribe a region where the flux offset performance is continuously present, and obtain a flux recovery abnormal block. 4.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion of claim 1, characterized in that, The change triggering module comprises: The trend extraction submodule is configured to read a carbon input change direction, a flux fluctuation section, and a soil disturbance frequency sequence in the region based on the flux recovery abnormal block, track a dynamic process of the change content on a time axis, obtain time distribution characteristics of a carbon input line, a flux change line, and a disturbance sequence, and obtain a time sequence change trend combination map. The node comparison submodule is configured to align times of the carbon input trend line and the flux change line based on the time sequence change trend combination map, identify change nodes in the disturbance frequency sequence, mark offset positions in the trend line, calculate an occurrence frequency of offset points in a time section, filter time sections that are synchronized with the disturbance nodes, and obtain a synchronized offset time section set. The patch area marking submodule is configured to position corresponding spatial positions in the time-coincident sections based on the synchronized offset time section set, filter a spatial range in which the carbon input, the flux change, and the disturbance nodes continuously appear in the same region, and obtain a change response region. 5.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion according to claim 4, characterized in that, The calculation formula of the occurrence frequency of the offset points in the time section is specifically as follows: ； wherein, representing the frequency of occurrence of the offset point within the time segment, representing the total number of trend offset points identified within the current time segment, representing the timestamp of the first carbon input trend line offset point, representing the timestamp of the first carbon input trend line offset point, representing the timestamp of the first flux change trend line offset point, representing the timestamp of the first flux change trend line offset point, representing the difference between the carbon input values at the two time instants before and after the first offset point in the carbon input trend line, representing the difference between the carbon input values at the two time instants before and after the first offset point in the carbon input trend line, representing the disturbance response weight coefficient, representing the patch disturbance background density correction term, representing the index number of the time segment in the time series distribution. 6.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion of claim 1, characterized in that, The green space structure merging module comprises: The section extraction submodule is configured to extract urban green space sections connected with the region based on the change response region, read spatially connected width information and position arrangement positions of the sections, identify contact relationships of adjacent sections, and obtain a green space section connection map. The boundary arrangement submodule is configured to read arrangement orders of section boundary lines based on the green space section connection map, extract corresponding surface cover type sequences, mark positions where surface types in the boundary range change, calculate a repetition proportion of surface cover types between adjacent boundary sections, identify regional boundaries of continuity between the sections, and obtain a spatially continuous cover structure. The disturbance identification submodule is configured to position change sections of edge regions based on the spatially continuous cover structure, identify an interlaced distribution situation of human disturbance trajectories in the boundary regions, filter a spatial range in which the disturbance trajectories interact, and obtain a continuous patch area set. 7.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion according to claim 6, characterized in that, The calculation formula of the repetition proportion of the surface cover types between the adjacent boundary sections is specifically as follows: ； Representative excerpt With fragments The percentage of overlap between land cover types Representative excerpt In land cover type The number of pixels below Representative excerpt In land cover type The number of pixels below Represents the number of all land cover types. Representative excerpt With fragments The percentage of shared length between adjacent boundary segments Representative excerpt With fragments The frequency of disturbances in the boundary area Representative excerpt Green space coverage area Representative excerpt Green space coverage area Representative excerpt The total length of the boundary line segment, Representative excerpt The total length of the boundary line segment, Representative excerpt At the boundary elevation sampling point The elevation value at that location, Representative excerpt At the boundary elevation sampling point The elevation value at that location, This represents the number of points used to sample the boundary elevation values ​​of the segment. 8.The urban green space carbon sink intelligent regulation and control system based on multi-source data fusion of claim 1, characterized in that, The regulation path derivation module comprises: The direction extraction submodule is configured to arrange orders of carbon input directions in each green space section based on the continuous patch area set, continue a direction process of the carbon input path according to a spatial trend of a carbon migration trend line section, and obtain a carbon input direction sequence. The connection relationship construction submodule is configured to track the direction extension mode between the connection points based on the carbon input direction sequence, track the continuity of the carbon migration direction between the segments, and obtain a segment extension structure diagram according to the spatial position relationship between adjacent segments; The path structure identification submodule is configured to follow the direction consistency process of the spatial advancing track, extend the spatial structure of the advancing direction in the continuous segments, lock the path direction of the direction continuity feature, and obtain a carbon sink regulation path paragraph based on the segment extension structure diagram.

9. The urban green space carbon sink intelligent regulation method based on multi-source data fusion, characterized in that, The urban green land carbon sink intelligent regulation system based on multi-source data fusion according to any one of claims 1-8 comprises the following steps: S1: Obtain soil samples of the differentiated depth of the surface cover layer of the urban green land partition, record the osmotic pressure change trajectory of each layer of soil and locate the osmotic pressure change turning point, obtain the carbon concentration change trend at the corresponding depth, compare the vertical variation trend of the soil organic carbon content with the spatial position of the turning point, and obtain a vertical carbon response area; S2: Based on the vertical carbon response area, select the carbon flux sensing points arranged in the coverage range, extract the flux stable segment before disturbance and the flux change segment after disturbance, determine whether the flux after disturbance has a sustained non-steady-state fluctuation, and obtain a flux recovery abnormal block; S3: Based on the flux recovery abnormal block, extract the carbon input change direction, the flux fluctuation segment, and the soil disturbance frequency sequence in the region, compare the carbon input trend line and the flux change line on the time axis, analyze whether the change nodes of the disturbance frequency and the offset points in the two trend lines overlap in time, mark the continuously occurring synchronous change patches, and obtain a change response area; S4: Based on the change response area, extract the connected urban green land segments, read the spatial connected width information and record the arrangement order of the green land boundary lines, extract the surface cover type sequence corresponding to the segments and mark the change positions in the spatial continuous segments, and obtain a continuous patch set; S5: Based on the continuous patch set, read the arrangement order of the carbon input direction and the carbon migration trend line segment in each green land segment, extract the extension relationship of the connection positions between the segments, analyze the consistency of the advancing track between adjacent segments in the spatial direction, and obtain a carbon sink regulation path paragraph.

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