Cloud-based ozone monitoring data collaborative management method and system

Abstract

The invention relates to the technical field of data collaborative management, in particular to a cloud-based ozone monitoring data collaborative management method and system, and the method comprises the following steps: obtaining the ozone concentration of each sampling point of a current task, calling a standard to obtain a difference value of each sampling point, and enabling the difference value to belong to an interval; and according to the mark, calibrating a fluctuation level aggregation area on the sampling task partition map, performing density correction on the area number, calculating the ozone concentration level of each partition, distributing a scheduling priority label, screening tasks which can be executed in a delayed manner, and generating scheduling cooperative control information. According to the invention, through dynamic comparison of the sampling point concentration values of the current batch and the previous batch, and combination of the fluctuation level and the space coordinate information, the fluctuation gathering area boundary is established, dynamic adaptation of partition reconstruction is completed, and delay of a static time partition structure to pollution fluctuation response is avoided. The organization density and the execution priority of high-risk area data in a scheduling round are improved, and the problem that the scheduling scale is not matched with task resources is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data collaborative management, and in particular to a cloud-based ozone monitoring data collaborative management method and system. Background Art

[0002] The field of data collaborative management technology specifically involves unified planning and coordination mechanisms for multi-source data collection, transmission, processing, sharing, and decision support. This area focuses on data compatibility, format unification, interface standardization, and permission control across systems, departments, and platforms. By establishing data resource directories, metadata management mechanisms, and standard communication protocols, it aims to improve data circulation efficiency and collaboratively utilize data value. Common applications include environmental monitoring, smart city management, and energy system scheduling. Its core lies in building an efficient and reliable data exchange and fusion framework to ensure the secure, efficient, and complete transmission and processing of data between different entities, avoiding data silos and redundant storage.

[0003] The collaborative ozone monitoring data management method aims to establish a data collection, processing, and sharing mechanism suitable for ozone environmental monitoring scenarios. This method addresses the issues of fragmented, redundant, and inconsistent monitoring data across different collection devices, analysis systems, or management terminals. It aims to achieve efficient data organization, sharing, and unified scheduling. This method can be used by environmental monitoring departments to centrally manage ozone concentration information, conduct joint analysis, and implement strategic responses, improving data utilization efficiency and governance collaboration. It is often used to build cloud-based pollutant monitoring systems or regional coordinated governance systems.

[0004] Traditional management methods rely on task units constructed based on a static time partition structure during task scheduling. The division of sampling tasks typically fails to fully consider the spatial heterogeneity of concentration fluctuations, resulting in a partition granularity that cannot adapt to dynamic changes in pollution. Sampling results exhibit limitations such as uneven spatial coverage or insufficient monitoring of highly polluted areas. Traditional methods use a single concentration threshold judgment method for task sorting and lack a mechanism for identifying intervals of multiple pollution levels. This results in similar task sorting weights for high- and medium-level pollution areas, interfering with task priority setting. The scheduling resource allocation process employs a quantitative allocation model and lacks the dynamic coupling judgment logic between tasks and resources. This results in both redundant and insufficient sampling resources during scheduling execution, especially during periods of high pollution fluctuations, causing scheduling delays and impacting pollution response efficiency. For example, in regional linkage monitoring tasks, the task volume is not differentiated between multiple sub-sites, resulting in wasted resources in low-fluctuation areas. At the same time, insufficient sampling density in pollution hotspots creates data gaps, limiting the comprehensive analysis and early warning accuracy of monitoring data. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a cloud-based ozone monitoring data collaborative management method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a cloud-based ozone monitoring data collaborative management method, comprising the following steps:

[0007] S1: Obtain the ozone concentration at each sampling point in the current task scheduling batch, combine it with the corresponding ozone concentration value of the previous batch sampling period, calculate multiple concentration difference grouping intervals, and generate the fluctuation difference grouping standard;

[0008] S2: Calling the fluctuation difference grouping standard, obtaining the numerical difference between the concentration of each sampling point in the current task and the concentration of the previous batch, determining the corresponding difference interval to which the difference of each sampling point belongs, combining and storing the sampling point number and the designated level, and generating sampling point fluctuation level mark information;

[0009] S3: Based on the sampling point fluctuation level mark information, the fluctuation level clustering area boundary is calibrated on the ozone sampling task partition map, the area number in the original task partition is corrected according to the spatial distribution density, and the adjusted division information is transmitted to the cloud task scheduling control center for synchronization coverage, thereby generating sampling task partition adjustment information;

[0010] S4: Call the sampling task partition adjustment information, calculate the ozone concentration level of each partition according to the real-time ozone concentration value of the sampling point corresponding to each partition, assign a scheduling priority level label to each partition in the current task batch, and generate task scheduling priority label information.

[0011] As a further solution of the present invention, the fluctuation difference grouping standard includes the concentration fluctuation threshold limit, the segmented interval number setting and the concentration difference level label; the sampling point fluctuation level marking information is specifically the sampling point number, the difference level classification label and the concentration fluctuation level serial number; the sampling task partition adjustment information includes the task block numbering rule, the spatial clustering division boundary and the fluctuation level sampling area mapping map; the task scheduling priority label information specifically refers to the partition scheduling level identifier, the concentration segment attribution table and the task scheduling priority sequence table.

[0012] As a further solution of the present invention, the steps for obtaining the fluctuation difference grouping standard are specifically as follows:

[0013] S101: Obtain the ozone concentration of each sampling point in the current task scheduling batch, and obtain the ozone concentration value of the same sampling point number in the previous batch, perform difference calculation based on the ozone concentration values ​​of the same sampling points in the two batches, call the difference calculation value under each sampling point number, and generate a difference set value;

[0014] S102: Based on the difference set values, extract the maximum difference value, the minimum difference value, and the median difference value, arrange the three values ​​horizontally and in parallel, calculate the value span between the maximum value and the minimum value, and generate a difference fluctuation interval parameter group;

[0015] S103: According to the difference fluctuation interval parameter group, the span is evenly divided into a set number of segments, and multiple difference interval segments are constructed in the form of equal-width intervals. Each interval segment is marked with a corresponding grade number, a grouping interval set is established, and a fluctuation difference grouping standard is generated.

[0016] As a further solution of the present invention, the step of obtaining the sampling point fluctuation level mark information is specifically as follows:

[0017] S201: calling the fluctuation difference grouping standard, obtaining the number of each sampling point in the current task and the ozone concentration value of the current period, collecting the ozone concentration value of the previous batch corresponding to the number, calculating the concentration value difference between the two periods, establishing a number and difference correspondence table, and generating a sampling point difference comparison table;

[0018] S202: Determine the difference interval according to the sampling point difference comparison table, match each difference value range with the interval segment, extract the level number of the interval, record the combination information of the sampling point number and the corresponding level number, and generate a sampling point level identifier set;

[0019] S203: calling the sampling point level identification set, establishing a data table with a unified structure, storing and managing each sampling point number and level mark information, configuring the partition attribute number field corresponding to the sampling point, and generating sampling point fluctuation level mark information.

[0020] As a further solution of the present invention, the step of obtaining the sampling task partition adjustment information is specifically as follows:

[0021] S301: Based on the sampling point fluctuation level tag information, according to the number, spatial coordinates and fluctuation level value of each sampling point, the positions of adjacent sampling points are retrieved on the ozone sampling task partition map, and based on the continuity of coordinates and consistency of fluctuation levels, clustered points of the same level are extracted. Based on the spatial position distribution of the same fluctuation level point set in the task partition map, the boundary coordinates are extracted to form a closed polygonal area, and a fluctuation level cluster boundary value group is generated;

[0022] S302: Calling the fluctuation level aggregation boundary value group, combining the area number distribution in the original task partition map, and obtaining the number and position distribution of sampling points in the original area number according to the spatial density distribution in each boundary value group, replacing the area numbers according to the distribution positions of the sampling points in the boundary value group while retaining the proportion of edge sampling points, to generate an area number edge correction value group;

[0023] S303: Call the area number edge correction value group, extract the numbers belonging to the same boundary value group according to the number replacement relationship, replace the target area number, re-assign a unified number identifier, merge them into independent sampling unit areas, and unify the replacement results into an area division mapping table and upload it to the cloud task scheduling control center to generate sampling task partition adjustment information.

[0024] As a further solution of the present invention, the step of obtaining the task scheduling priority tag information is specifically as follows:

[0025] S401: Calling the sampling task partition adjustment information, obtaining each sampling point number and the corresponding real-time ozone concentration value in each partition, aggregating all concentration values ​​of the partition according to the sampling point number, calculating the concentration mean of each partition, and generating the partition average concentration value;

[0026] S402: Calculating the ozone concentration level of each partition based on the average concentration value of each partition and the fluctuation of the ozone concentration in each partition, and generating partition concentration assessment information;

[0027] S403: Call the partition concentration assessment information, combine it with the task scheduling priority parameters, extract the scheduling priority tag bound to the ozone concentration level, establish a scheduling tag list that matches the task batch and priority level, and generate task scheduling priority tag information.

[0028] As a further solution of the present invention, the formula for calculating the ozone concentration level in each partition is:

[0029]

[0030] Among them, L i represents the concentration level of the ith partition, n i Represents the number of sampling points in the i-th partition, M i represents the average ozone concentration value of the ith partition, E represents the average concentration influence coefficient, O k,i represents the real-time ozone concentration value of the kth sampling point in the i-th partition, W i represents the fluctuation range of the ozone concentration difference sequence in the i-th partition, D i Represents the normalized value of the farthest spatial distance of the sampling points in the i-th partition.

[0031] As a further embodiment of the present invention, the method further comprises:

[0032] S5: Based on the task scheduling priority tag information, the marked partition scheduling level and the number of sampling point distribution, determine whether the task scale of each priority level matches the sampling resource capacity. For priority level tasks that exceed the scheduling capacity, screen the sampling point numbers that can be delayed. Combined with the sampling frequency control rules, organize the task set that needs to be executed synchronously in the current scheduling round, and generate sampling scheduling collaborative control information;

[0033] The sampling scheduling collaborative control information includes a synchronous execution task number set, a delayable sampling point index group, and a collaborative control scheduling priority configuration table.

[0034] As a further solution of the present invention, the step of acquiring the sampling scheduling coordinated control information is specifically:

[0035] S501: Based on the task scheduling priority tag information, according to the task partition number corresponding to each priority level, the total number of sampling point numbers in each partition is counted, the upper limit of the sampling resource configuration capacity in the current scheduling batch is extracted, and the deviation between the task sampling quantity corresponding to each priority level and the resource capacity is calculated to generate a task resource matching deviation value;

[0036] The formula for calculating the deviation between the number of task samples corresponding to each priority level and the resource capacity is:

[0037]

[0038] Among them, P d Represents the capacity deviation of the d-th priority task, C j,d represents the number of sampling points in the jth partition of the dth priority level, Q j,d represents the scheduling level weight coefficient of the jth partition in the dth priority level, S j,d represents the mean square error of the ozone concentration values ​​at the jth sampling point in the dth priority level, V j,d represents the spatial distribution density adjustment coefficient of the jth partition in the dth priority level, R d represents the upper limit of the sampling resource configuration capacity of the dth priority level, and m represents the total number of task partitions under the dth priority level;

[0039] S502: calling the task resource matching deviation value, identifying the priority number with resource overrun, extracting all sampling point numbers in the corresponding task partition, screening the sampling interval for each number according to the set sampling frequency control rule, retaining the number set that does not meet the frequency trigger condition, and generating a set of delayable sampling point numbers;

[0040] S503: Based on the set of delayable sampling point numbers, the delayed execution sampling point numbers are removed from the current scheduling task set, and the remaining sampling point numbers are sorted in combination with the partition numbers according to the priority order, a list of sampling points that need to be synchronously executed in the current scheduling round is established, and sampling scheduling collaborative control information is generated.

[0041] A cloud-based ozone monitoring data collaborative management system, the cloud-based ozone monitoring data collaborative management system is used to execute the above-mentioned cloud-based ozone monitoring data collaborative management method, the system comprising:

[0042] The difference grouping module obtains the ozone concentration of each sampling point in the current task scheduling batch, combines the corresponding ozone concentration values ​​of the previous batch sampling period, calculates multiple concentration difference grouping intervals, and generates the fluctuation difference grouping standard;

[0043] The fluctuation level partitioning module calls the fluctuation difference grouping standard, obtains the numerical difference between the concentration of each sampling point in the current task and the corresponding concentration of the previous batch, determines the corresponding difference interval to which the difference of each sampling point belongs, combines and stores the sampling point number and the demarcation level, and generates sampling point fluctuation level marking information;

[0044] The task partition adjustment module calibrates the boundaries of the fluctuation level clustering area on the ozone sampling task partition map based on the sampling point fluctuation level mark information, performs edge correction on the area number in the original task partition according to the spatial distribution density, transmits the adjusted division information to the cloud task scheduling control center for synchronous coverage, and generates sampling task partition adjustment information;

[0045] The scheduling priority adjustment module calls the sampling task partition adjustment information, calculates the ozone concentration level of each partition based on the real-time ozone concentration value of the sampling point corresponding to each partition, assigns a scheduling priority level label to each partition in the current task batch, and generates task scheduling priority label information;

[0046] The scheduling collaborative control module screens the sampling point numbers that can be delayed according to the task scheduling priority tag information, organizes the task set that needs to be executed synchronously in the current scheduling round, and generates sampling scheduling collaborative control information.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In this paper, the maximum, minimum, and median differences are extracted through dynamic comparison of the concentration values ​​of sampling points in the current and previous batches to construct an interval classification standard. The fluctuation level and spatial coordinate information are combined to establish the fluctuation cluster boundary. With the help of edge number correction and partition merging operations, dynamic adaptation of partition reconstruction is completed, which strengthens the coupling relationship between sampling area delineation and actual pollution status, and avoids the delay of static time partition structure in responding to pollution fluctuations. At the same time, multi-level scheduling priority labels are set for each task partition based on real-time concentration. The dynamic difference value range of the current sampling task scale and resource capacity is used to screen delayed sampling points. The scheduling set composition is calibrated according to the sampling frequency control rule. The sampling point concentration response, spatiotemporal layout characteristics, and resource capacity are synchronously embedded in the scheduling control structure, which shifts the data acquisition instructions from a one-way passive configuration to a resource-sensitive allocation mechanism. Through the embedded concentration level quantization calculation method and the dynamic response at the sampling point number granularity level, the organization density and execution priority of high-risk area data in the scheduling round are improved, solving the problem of mismatch between scheduling scale and task resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0058] See also Figure 1 The present invention provides a technical solution: a cloud-based ozone monitoring data collaborative management method, comprising the following steps:

[0059] S1: Obtain the ozone concentration of each sampling point in the current task scheduling batch, combine it with the corresponding ozone concentration value of the previous batch sampling period, perform difference calculation on each sampling point according to the sampling point number, extract the maximum value, minimum value and median value in the difference result, calculate multiple concentration difference grouping intervals, and generate the fluctuation difference grouping standard;

[0060] S2: Call the fluctuation difference grouping standard, obtain the numerical difference between the concentration of each sampling point in the current task and the previous batch according to the boundary value of the concentration difference interval, determine the corresponding difference interval to which the difference value of each sampling point belongs, and combine and store the sampling point number and the designated level according to the level of the interval, and generate the sampling point fluctuation level mark information;

[0061] S3: Based on the sampling point fluctuation level marking information, the spatial coordinate information of the sampling points and the fluctuation level, the boundaries of the fluctuation level aggregation area are calibrated on the ozone sampling task partition map. The area numbers in the original task partition are corrected according to the spatial distribution density. The fluctuation value aggregation areas are renumbered and merged into independent sampling units. The adjusted division information is transmitted to the cloud task scheduling control center for synchronization coverage, and the sampling task partition adjustment information is generated.

[0062] S4: Call the sampling task partition adjustment information, calculate the ozone concentration level of each partition according to the real-time ozone concentration value of the sampling point corresponding to each partition, assign a scheduling priority label to each partition in the current task batch according to the task scheduling priority parameter, and generate task scheduling priority label information;

[0063] S5: Based on the task scheduling priority tag information, the marked partition scheduling level and the number of sampling point distribution, determine whether the task scale of each priority level matches the sampling resource capacity. For priority tasks that exceed the scheduling capacity, screen the sampling point numbers that can be delayed. Combined with the sampling frequency control rules, organize the task set that needs to be executed synchronously in the current scheduling round and generate sampling scheduling collaborative control information;

[0064] The fluctuation difference grouping standards include concentration fluctuation threshold limits, segmented interval number settings and concentration difference level labels. The sampling point fluctuation level marking information specifically includes the sampling point number, difference level classification label and concentration fluctuation level serial number. The sampling task partition adjustment information includes the task block numbering rules, spatial clustering division boundaries and fluctuation level sampling area mapping diagrams. The task scheduling priority label information specifically refers to the partition scheduling level identifier, concentration segment attribution table and task scheduling priority sequence table. The sampling scheduling collaborative control information includes the synchronous execution task number set, the delayable sampling point index group and the collaborative control scheduling priority configuration table.

[0065] See also Figure 2 The specific steps for obtaining the fluctuation difference grouping standard are as follows:

[0066] S101: Obtain the ozone concentration of each sampling point in the current task scheduling batch, and obtain the ozone concentration value of the same sampling point number in the previous batch, perform difference calculation based on the ozone concentration values ​​of the same sampling points in the two batches, call the difference calculation value under each sampling point number, and generate a difference set value;

[0067] If you need to obtain the ozone concentration of each sampling point in the current task scheduling batch, you can read the real-time data of all sampling points through sensor equipment or monitoring systems, set the current batch to contain three sampling points A, B, and C, and record their ozone concentration values ​​as O1A, O1B, and O1C respectively. Next, obtain the ozone concentration value under the same sampling point number in the previous batch. You can obtain the ozone concentration values ​​O2A, O2B, and O2C of sampling points A, B, and C in this batch by consulting the previous batch data. For example, the ozone concentration value of sampling point A in the previous batch is O2A=50ppb, the value of sampling point B is O2B=45ppb, and the value of sampling point C is O2C=60ppb. Then, perform the difference calculation. For each sampling point, calculate the difference between the ozone concentration of this batch and the ozone concentration of the previous batch. The calculation formula is: ΔO i =O 1i -O 2i , where ΔO irepresents the concentration difference at the i-th sampling point. For sampling point A, the difference is calculated as ΔOA = O1A - O2A = 70 ppb - 50 ppb = 20 ppb. For sampling point B, it's ΔOB = 60 ppb - 45 ppb = 15 ppb. For sampling point C, it's ΔOC = 80 ppb - 60 ppb = 20 ppb. The difference at each sampling point is stored in a difference set, for example, {20, 15, 20}.

[0068] S102: Based on the difference set values, extract the maximum difference value, the minimum difference value, and the median difference value, arrange the three values ​​horizontally and in parallel, calculate the value span between the maximum value and the minimum value, and generate a difference fluctuation interval parameter group;

[0069] Based on the difference set, we need to extract the maximum, minimum, and median difference values. First, sort the difference set to obtain {15, 20, 20}. The maximum difference is 20, the minimum is 15, and the median is 20. Next, calculate the span between the maximum and minimum values ​​using the formula: Span = Maximum Difference - Minimum Difference. Substituting the values, Span = 20 - 15 = 5 ppb. This process generates the difference fluctuation range parameter set {Maximum Difference: 20, Minimum Difference: 15, Median Difference: 20, Span: 5 ppb}. These values ​​will be used to delineate fluctuation ranges in subsequent analysis.

[0070] S103: Divide the span into a set number of segments according to the difference fluctuation interval parameter group, construct multiple difference interval segments in the form of equal-width intervals, label each interval segment with a corresponding grade number, establish a grouping interval set, and generate a fluctuation difference grouping standard;

[0071] According to the difference fluctuation interval parameter group, the numerical span is divided into the set number of segments, and the set number of segments is set to 3. First, the width of each interval segment is calculated, width = span / number of segments = 5ppb / 3 = 1.67ppb (rounded to 1.67ppb). Then, multiple difference interval segments are constructed in an equal width interval manner. Starting from the minimum difference, the interval width is increased in sequence. The constructed interval segments are: [15, 16.67), [16.67, 18.34),

[0072] Each interval segment corresponds to a level number, labeled as Interval 1, Interval 2, and Interval 3, forming a grouped interval set {[15,16.67), [16.67,18.34), [18.34,20)}. This also generates a fluctuation difference grouping standard. According to this standard, the differences at different sampling points can be classified for subsequent analysis and processing.

[0073] See also Figure 3 ,The steps for obtaining the sampling point fluctuation level mark information are as follows:

[0074] S201: Call the fluctuation difference grouping standard, obtain the number of each sampling point in the current task and the ozone concentration value of the current period, collect the previous batch of ozone concentration values ​​corresponding to the number, calculate the concentration value difference between the two periods, establish a number and difference correspondence table, and generate a sampling point difference comparison table;

[0075] After calling the fluctuation difference grouping standard, first extract the number information of all sampling points in the current task, and call the ozone concentration values ​​collected by the corresponding numbers in the current period one by one. For example, when the sampling points are numbered A, B, and C, the ozone concentration values ​​in the current period are 80ppb, 70ppb, and 75ppb, respectively. Then, for each sampling point number, extract the ozone concentration value in its historical record from the previous task scheduling batch. For example, the previous batch value of sampling point A is 60ppb, sampling point B is 65ppb, and sampling point C is 55ppb. Then calculate the difference in ozone concentration values ​​between the current period and the previous batch in sequence. The calculation formula is difference ΔO i =O 1i -O 2i , where O 1i is the ozone concentration value in the current period, O 2i For the concentration values ​​of the previous batch, the difference calculation is performed for the above example, and the difference value of sampling point A is 20ppb, sampling point B is 5ppb, and sampling point C is 20ppb. Then, based on the sampling point number and the calculated concentration difference, a one-to-one correspondence relationship set is constructed in the memory or data structure. Each record contains the sampling point number and its corresponding difference value. For example, number A corresponds to 20, number B corresponds to 5, and number C corresponds to 20. The difference statistics of each sampling point are completed by traversal and written uniformly into the data structure of the sampling point difference comparison table. Each record in the structure independently represents the mapping relationship between a sampling point number and its calculated difference value, forming a complete difference comparison table.

[0076] S202: Determine the difference interval to which the sampling point belongs based on the sampling point difference comparison table, match each difference value range with the interval segment, extract the level number of the interval, record the combination information of the sampling point number and the corresponding level number, and generate a sampling point level identifier set;

[0077] According to the sampling point difference comparison table, first for each sampling point number and its corresponding difference in the table, determine one by one which preset difference interval the difference is in. The difference interval needs to be divided according to the equal width given in the above-mentioned fluctuation difference grouping standard, for example, the interval is [15,16.67)

[0078] [16.67,18.34), [18.34,20), where the interval endpoints are floating-point values. The open and closed interval rules are used to determine the location of the difference. The difference between sampling points A and C is 20 ppb, which is in the interval [18.34,20), so its level number is marked as 3. The difference between sampling point B is 5 ppb, which does not belong to any interval. The lowest level number 0 can be further set to correspond to the difference value range outside all non-standard intervals. Therefore, sampling point B is marked as level number 0. By determining the specific interval segment to which each difference value range belongs, a one-to-one mapping between the difference value range and the standard level number is achieved. Then, based on the sampling point number and its corresponding level number, a combined information record is constructed. Each record consists of the sampling point number and its level number. All difference levels are identified and their annotation information is extracted in order of number. Finally, a sampling point level identification set is formed in the form of a sequence set, which is convenient for subsequent structured management.

[0079] S203: Calling the sampling point level identification set, establishing a data table with a unified structure, storing and managing each sampling point number and level tag information, configuring the partition attribute number field corresponding to the sampling point, and generating sampling point fluctuation level tag information;

[0080] After calling the sampling point grade identification set, the sampling point numbers and their grade identification information need to be systematically managed in a unified data format. First, a unified data structure field is set, including the sampling point number field, the grade number field, and the partition attribute number field. The sampling point number field is used to identify the unique monitoring point location, the grade number field is used to represent the fluctuation grade value mapped based on the difference interval, and the partition attribute number field is used to mark the spatial or functional area attribute to which the sampling point belongs, such as the number of urban area divisions, pollution characteristic areas, or areas near roads. The sampling point number, its corresponding grade value, and the corresponding partition attribute number are bound and written to the data table at the record granularity. For example, sampling point A has a grade number of 3 and a partition attribute number of Z1, sampling point B has a grade number of 0 and a partition attribute number of Z2, and sampling point C has a grade number of 3 and a partition attribute number of Z1. The complete structure record is filled in sequence according to the sampling point number. The grade identification and partition attribute of all sampling points in the current batch are jointly expressed by item by item configuration, and finally the sampling point fluctuation grade marking information is formed to support subsequent regional management, data classification, and other tasks.

[0081] See also Figure 4 ,The steps for obtaining the sampling task partition adjustment information are as follows:

[0082] S301: Based on the sampling point fluctuation level tag information, according to the number, spatial coordinates and fluctuation level value of each sampling point, the positions of adjacent sampling points are retrieved on the ozone sampling task partition map. Based on the continuity of coordinates and the consistency of fluctuation levels, clustered points of the same level are extracted. Based on the spatial position distribution of the same fluctuation level point set in the task partition map, the boundary coordinates are extracted to form a closed polygonal area, and a fluctuation level cluster boundary value group is generated.

[0083] Based on the sampling point fluctuation level information, the number, spatial coordinates, and fluctuation level value of each sampling point must first be retrieved. Based on this information, the locations of adjacent sampling points on the ozone sampling task partition map are determined. For each sampling point number, its spatial coordinate data is first read. For example, sampling points A, B, and C are located at spatial coordinates (5, 10), (6, 10), and (7, 10), respectively. Then, combining the fluctuation level value of each sampling point (for example, the fluctuation levels of sampling points A, B, and C are 2, 2, and 2, respectively), adjacent sampling points of the same level are extracted and clustered into a point set based on the continuity of the spatial coordinates and the consistency of the fluctuation level. Assuming that sampling points A, B, and C all belong to fluctuation level 2, these three sampling points constitute a clustered point set of the same level. Next, based on the spatial distribution of these sampling points of the same level on the task partition map, their boundary coordinates are determined. A closed polygonal area, such as a triangle or rectangle, is defined by the coordinates of these sampling points. The boundary coordinates of this region are calculated and calibrated based on the specific sampling point locations and the relative positions of adjacent points, resulting in a set of fluctuation level cluster boundary values. For example, the boundary values ​​of the clustered region are (5, 10), (7, 10), and (6, 12), ultimately resulting in a set of fluctuation level cluster boundary coordinates. The polygon formed by these boundary coordinates is the fluctuation level cluster region.

[0084] S302: Calling the fluctuation level aggregation boundary value group, combining the area number distribution in the original task partition map, and obtaining the number and location distribution of sampling points in the original area number according to the spatial density distribution within each boundary value group, replacing the area numbers involved according to the distribution positions of the sampling points in the boundary value group while retaining the proportion of edge sampling points, to generate an area number edge correction value group;

[0085] After obtaining the clustered boundary value groups for the fluctuation level, they need to be processed in conjunction with the area number distribution in the original task partition map. First, based on the spatial density distribution within each boundary value group, the number and location distribution of sampling points within that area are counted and analyzed. For example, within a given area, multiple sampling points may be distributed within the clustered area formed by the boundary value group, including sampling points A, B, and C, located at coordinates (5, 10), (6, 10), and (7, 10), respectively. Next, based on the spatial distribution and boundary coordinates of these sampling points, the distribution density of the sampling points within the area can be determined. Combined with the area number, this analysis can be used to determine whether the sampling points within that area are highly clustered. If the sampling point density is high, number replacement may be necessary. In this case, the original numbers of the areas containing these sampling points are replaced based on the spatial density distribution, and the area numbers are reassigned according to the new numbering system with the adjusted spatial boundaries. Furthermore, when replacing numbers, the proportion of edge sampling points must be taken into account. For example, within a given area, sampling point A may occupy 30%, sampling point B 20%, and sampling point C 50%. At this time, the ratio of edge sampling points is calculated, the information is saved and associated with the new region number. In this way, the generated region number edge correction value group contains the mapping relationship between the edge sampling point ratio and the corresponding region number.

[0086] S303: Calling the region number edge correction value group, extracting the numbers belonging to the same boundary value group based on the number replacement relationship, replacing the target region number, re-assigning a unified number identifier, and merging them into independent sampling unit regions. The replacement results are unified into a region division mapping table and uploaded to the cloud task scheduling control center to generate sampling task partition adjustment information;

[0087] After invoking the region number edge correction value group, the sampling point numbers belonging to the same boundary value group are first extracted based on the number replacement relationship within each boundary value group. These numbers are then replaced based on their spatial coordinates and fluctuation levels. For example, within a region number, the original region numbered Z1 contains sampling points A, B, and C. After calculation, the boundary-corrected sampling point number is replaced with Z3, and sampling points A, B, and C are reassigned a unified number. After the number replacement is complete, the numbers of all sampling points are uniformly calibrated according to the new rules, and the original region is merged into an independent sampling unit area. This merge result forms a new region division mapping table, which is uploaded to the cloud-based task scheduling control center for further task scheduling and analysis to ensure the accurate execution of sampling tasks. Ultimately, the generated sampling task partition adjustment information will include relevant data on the new region division and sampling unit area, ensuring that the adjusted task partition map accurately reflects the spatial distribution of sampling points and their level changes.

[0088] See also Figure 5 ,The specific steps for obtaining task scheduling priority label information are:

[0089] S401: Call the sampling task partition adjustment information, obtain each sampling point number in each partition and the corresponding real-time ozone concentration value, aggregate all concentration values ​​of the partition according to the sampling point number, calculate the concentration mean of each partition, and generate the average concentration value of the partition;

[0090] After calling the sampling task partition adjustment information, it is necessary to obtain the numbers of all sampling points in each partition and the corresponding real-time ozone concentration values. First, retrieve each sampling point number and query its ozone concentration in the current period. For example, in partition 1, the real-time concentration of sampling point A is 70ppb, sampling point B is 65ppb, and sampling point C is 80ppb. Next, aggregate the concentration values ​​of all sampling points in the partition according to the sampling point number. For example, the concentration values ​​of all sampling points in partition 1 are summarized as {70, 65, 80}. Then, calculate the mean concentration of each partition by adding the concentration values ​​of all sampling points in the partition and dividing it by the number of sampling points. The formula is:

[0091]

[0092] Where n is the number of sampling points in the partition, O i is the concentration value of the i-th sampling point. In this example, the number of sampling points in partition 1 is 3, and the concentration values ​​are {70, 65, 80}. Therefore, the mean concentration of partition 1 is calculated as:

[0093]

[0094] This calculation yields an average concentration value of 71.67 ppb for partition 1.

[0095] S402: Calculate the ozone concentration level of each zone based on the average concentration value of each zone and the fluctuation of the ozone concentration in each zone, and generate zone concentration assessment information;

[0096] The formula for calculating the ozone concentration level in each zone is:

[0097]

[0098] Among them, L i represents the concentration level of the ith partition, n i Represents the number of sampling points in the i-th partition, M i represents the average ozone concentration value of the ith partition, E represents the average concentration influence coefficient, O k,i represents the real-time ozone concentration value of the kth sampling point in the i-th partition, W irepresents the fluctuation range of the ozone concentration difference sequence in the i-th partition, D i Represents the normalized value of the farthest spatial distance of the sampling points in the i-th partition;

[0099] Based on the average concentration value of each partition and the fluctuation of ozone concentration in each partition, it is necessary to further calculate the ozone concentration level of each partition. First, the fluctuation of ozone concentration in each partition is obtained. Usually, it is judged based on the concentration fluctuation range of each sampling point in the partition. For example, the concentration fluctuation range of partition 1 is {70, 65, 80}, and its fluctuation range W i is the difference between the maximum and minimum values, that is

[0100] W i =80-65=15ppb. Next, calculate the concentration level for each partition using the formula:

[0101]

[0102] Among them, L i is the concentration level of the ith partition, n i is the number of sampling points in the i-th partition, M i is the average concentration value of the i-th partition, E is the average concentration influence coefficient, O k,i is the concentration value of the kth sampling point in the i-th partition, W i is the fluctuation range value, D i is the normalized value of the farthest spatial distance. Assuming E = 0.1 (i.e., concentration influence coefficient), the normalized value of the farthest spatial distance of partition 1 is D i =0.5, then the concentration level calculation process is:

[0103]

[0104] First calculate each difference:

[0105] |71.67-70|=1.67,|71.67-65|=6.67,|71.67-80|=8.33;

[0106] Then sum and calculate the average:

[0107]

[0108] Then calculate the remainder:

[0109]

[0110] Finally, the concentration levels are:

[0111] L1=5.56+7.17+3.16=15.89;

[0112] This calculation yields a concentration evaluation grade of 15.89 for partition 1.

[0113] S403: Calling the zone concentration assessment information, combining it with the task scheduling priority parameters, extracting the scheduling priority tag bound to the ozone concentration level, establishing a scheduling tag list that matches the task batches and priority levels, and generating task scheduling priority tag information;

[0114] After obtaining the partition concentration assessment information, it is necessary to combine the task scheduling priority parameters to extract the scheduling priority label bound to the ozone concentration level. First, according to the concentration level value, the concentration level of each partition is matched with the preset scheduling priority level, and the scheduling priority level is set as follows: 0-10 is low priority; 1-20 is medium priority; 21 and above is high priority; according to the calculation results, the concentration level of partition 1 is 15.89, which falls within the medium priority range. Therefore, the scheduling priority label of partition 1 is "medium priority". Next, based on the concentration level and scheduling priority label of each partition, a scheduling label list is established to match the task batch with the priority level. It is assumed that there are other partitions 2 and 3, corresponding to different concentration levels and scheduling labels respectively. In this way, the generated task scheduling priority label list will include the number of each partition and its corresponding priority label. Finally, these scheduling label information are uploaded to the cloud task scheduling control center to ensure that the task scheduling system can schedule sampling tasks in a timely manner according to the priority level.

[0115] See also Figure 6 ,The steps for acquiring the sampling scheduling collaborative control information are as follows:

[0116] S501: Based on the task scheduling priority tag information and the task partition number corresponding to each priority level, the total number of sampling point numbers in each partition is counted, the upper limit of the sampling resource configuration capacity in the current scheduling batch is extracted, and the deviation between the task sampling quantity corresponding to each priority level and the resource capacity is calculated to generate a task resource matching deviation value;

[0117] The formula for calculating the deviation between the number of task samples and resource capacity corresponding to each priority level is:

[0118]

[0119] Among them, P d Represents the capacity deviation of the d-th priority task, C j,d represents the number of sampling points in the jth partition of the dth priority level, Q j,d represents the scheduling level weight coefficient of the jth partition in the dth priority level, S j,d represents the mean square error of the ozone concentration values ​​at the jth sampling point in the dth priority level, V j,drepresents the spatial distribution density adjustment coefficient of the jth partition in the dth priority level, R d represents the upper limit of the sampling resource configuration capacity of the dth priority level, and m represents the total number of task partitions under the dth priority level;

[0120] According to the task scheduling priority label information, first count the total number of sampling point numbers in each partition according to the task partition number corresponding to each priority level. For example, assume there are three partitions under priority level 1, partition A has 10 sampling points, partition B has 8 sampling points, and partition C has 12 sampling points. Then the total number of sampling points in partitions A, B, and C is counted as 10, 8, and 12, respectively. Next, extract the upper limit of the sampling resource configuration capacity in the current scheduling batch. For example, the upper limit of the sampling resource capacity of the current task configuration is 100 sampling points. Next, calculate the deviation between the number of task samples corresponding to each priority level and the resource capacity. This calculation needs to be performed using the following formula:

[0121]

[0122] Among them, P d Represents the capacity deviation of the d-th priority task, C j,d represents the number of sampling points in the jth partition of the dth priority level, Q j,d represents the scheduling level weight coefficient of the jth partition in the dth priority level, S j,d represents the mean square error of the ozone concentration values ​​at the jth sampling point in the dth priority level, V j,d represents the spatial distribution density adjustment coefficient of the jth partition in the dth priority level, R d Represents the upper limit of the sampling resource configuration capacity of the dth priority level, and m represents the total number of task partitions under the dth priority level. Assume that there are 3 partitions under the first priority level. The number of sampling points in partition A is 10, the weight coefficient is 1, the mean square error of ozone concentration is 5, and the spatial distribution density coefficient is 0.8. The number of sampling points in partition B is 8, the weight coefficient is 0.9, the mean square error of ozone concentration is 4, and the spatial distribution density coefficient is 0.7. The number of sampling points in partition C is 12, the weight coefficient is 1.1, the mean square error of ozone concentration is 6, the spatial distribution density coefficient is 0.9, and the upper limit of the sampling resource configuration capacity is 100. Calculate the deviation contribution value of each partition. First, calculate the contribution of each partition:

[0123]

[0124] Then sum:

[0125]

[0126] Finally, calculate the task capacity deviation:

[0127]

[0128] Through this calculation, the capacity deviation of the task with priority level 1 is generated to be 6.17.

[0129] S502: Calling the task resource matching deviation value to identify the priority number with resource overrun, extracting all sampling point numbers in the corresponding task partition, and filtering the sampling interval for each number according to the set sampling frequency control rule, retaining the number set that does not meet the frequency trigger condition, and generating a set of delayable sampling point numbers;

[0130] After calling the task resource matching deviation value, it is necessary to identify the priority level numbers with resource overruns. First, based on the calculated deviation value, determine which priority level tasks exceed the resource allocation capacity. For example, if the deviation value of 6.17 for priority level 1 exceeds the set resource capacity limit (set to 5), priority level 1 can be considered overrun. Next, all sampling point numbers in the task partition corresponding to that priority level are extracted and screened. According to the set sampling frequency control rules (for example, each sampling point must meet the condition of at least two samples per day), the sampling interval of each sampling point number is screened to filter out the set of sampling point numbers that do not meet the frequency trigger conditions. For example, if sampling point A only samples once a day, it is screened as not meeting the frequency trigger condition, and the set of delayed sampling point numbers is eliminated, generating a set of delayed sampling point numbers. It is assumed that three sampling points that do not meet the frequency requirements are finally screened out, forming a number set.

[0131] S503: Based on the set of deferrable sampling point numbers, the delayed sampling point numbers are removed from the current scheduling task set, and the remaining sampling point numbers are sorted in combination with the partition numbers according to the priority order, to establish a list of sampling points that need to be synchronously executed in the current scheduling round, and generate sampling scheduling collaborative control information;

[0132] Based on the set of delayed sampling point numbers, the sampling point numbers that are delayed are removed from the current scheduled task set. First, after excluding all the delayed sampling point numbers, the remaining sampling point numbers are further sorted in combination with the partition number in the order of priority. For example, the sampling points of priority level 1 are sorted as A, B, C, and the sampling points of priority level 2 are sorted as D, E, F, and so on. These sampling points are combined in the sorted order to form a new sampling point list. Finally, the sampling scheduling collaborative control information is generated, which contains the detailed information of the sampling point numbers and task partitions that need to be executed synchronously in the current scheduling round, and uploaded to the scheduling center for execution.

[0133] See also Figure 7A cloud-based ozone monitoring data collaborative management system is provided. The cloud-based ozone monitoring data collaborative management system is used to execute the above-mentioned cloud-based ozone monitoring data collaborative management method. The system includes:

[0134] The difference grouping module obtains the ozone concentration of each sampling point in the current task scheduling batch, combines the corresponding ozone concentration values ​​of the previous batch sampling period, calculates multiple concentration difference grouping intervals, and generates the fluctuation difference grouping standard;

[0135] The fluctuation level partitioning module calls the fluctuation difference grouping standard to obtain the numerical difference between each sampling point in the current task and the corresponding concentration of the previous batch, determines the corresponding difference interval to which the difference value of each sampling point belongs, combines and stores the sampling point number and the designated level, and generates the sampling point fluctuation level mark information;

[0136] The task partition adjustment module calibrates the boundaries of the fluctuation level clustering area on the ozone sampling task partition map based on the sampling point fluctuation level marking information, and makes edge corrections to the area numbers in the original task partition according to the spatial distribution density. The adjusted division information is transmitted to the cloud task scheduling control center for synchronous coverage, and the sampling task partition adjustment information is generated.

[0137] The scheduling priority adjustment module calls the sampling task partition adjustment information, calculates the ozone concentration level of each partition based on the real-time ozone concentration value of the sampling point corresponding to each partition, assigns a scheduling priority level label to each partition in the current task batch, and generates task scheduling priority label information;

[0138] The scheduling collaborative control module determines whether the task scale of each priority level matches the sampling resource capacity based on the task scheduling priority tag information. For priority tasks that exceed the scheduling capacity, it screens the sampling point numbers that can be delayed. Combined with the sampling frequency control rules, it organizes the task set that needs to be executed synchronously in the current scheduling round and generates sampling scheduling collaborative control information.

[0139] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A cloud-based ozone monitoring data collaborative management method, characterized in that: The following steps are involved: S1: Obtain the ozone concentration at each sampling point in the current task scheduling batch, combine it with the corresponding ozone concentration value of the previous batch sampling period, calculate multiple concentration difference grouping intervals, and generate the fluctuation difference grouping standard; S2: Calling the fluctuation difference grouping standard, obtaining the numerical difference between the concentration of each sampling point in the current task and the concentration of the previous batch, determining the corresponding difference interval to which the difference of each sampling point belongs, combining and storing the sampling point number and the designated level, and generating sampling point fluctuation level mark information; S3: Based on the sampling point fluctuation level mark information, the fluctuation level clustering area boundary is calibrated on the ozone sampling task partition map, the area number in the original task partition is corrected according to the spatial distribution density, and the adjusted division information is transmitted to the cloud task scheduling control center for synchronization coverage, thereby generating sampling task partition adjustment information; S4: Call the sampling task partition adjustment information, calculate the ozone concentration level of each partition according to the real-time ozone concentration value of the sampling point corresponding to each partition, assign a scheduling priority level label to each partition in the current task batch, and generate task scheduling priority label information.

2. The cloud-based ozone monitoring data collaborative management method according to claim 1, characterized in that: The fluctuation difference grouping standard includes the concentration fluctuation threshold limit, the segmented interval number setting and the concentration difference level label. The sampling point fluctuation level marking information specifically includes the sampling point number, the difference level classification label and the concentration fluctuation level grade serial number. The sampling task partition adjustment information includes the task block numbering rule, the spatial clustering division boundary and the fluctuation level sampling area mapping diagram. The task scheduling priority label information specifically refers to the partition scheduling level identifier, the concentration segment attribution table and the task scheduling priority sequence table.

3. The cloud-based ozone monitoring data collaborative management method according to claim 2, characterized in that: The steps for obtaining the fluctuation difference grouping standard are specifically as follows: S101: Obtain the ozone concentration of each sampling point in the current task scheduling batch, and obtain the ozone concentration value of the same sampling point number in the previous batch, perform difference calculation based on the ozone concentration values ​​of the same sampling points in the two batches, call the difference calculation value under each sampling point number, and generate a difference set value; S102: Based on the difference set values, extract the maximum difference value, the minimum difference value, and the median difference value, arrange the three values ​​horizontally and in parallel, calculate the value span between the maximum value and the minimum value, and generate a difference fluctuation interval parameter group; S103: According to the difference fluctuation interval parameter group, the span is evenly divided into a set number of segments, and multiple difference interval segments are constructed in the form of equal-width intervals. Each interval segment is marked with a corresponding grade number, a grouping interval set is established, and a fluctuation difference grouping standard is generated.

4. The cloud-based ozone monitoring data collaborative management method according to claim 3, characterized in that: The steps for obtaining the sampling point fluctuation level mark information are specifically as follows: S201: calling the fluctuation difference grouping standard, obtaining the number of each sampling point in the current task and the ozone concentration value of the current period, collecting the ozone concentration value of the previous batch corresponding to the number, calculating the concentration value difference between the two periods, establishing a number and difference correspondence table, and generating a sampling point difference comparison table; S202: Determine the difference interval according to the sampling point difference comparison table, match each difference value range with the interval segment, extract the level number of the interval, record the combination information of the sampling point number and the corresponding level number, and generate a sampling point level identifier set; S203: calling the sampling point level identification set, establishing a data table with a unified structure, storing and managing each sampling point number and level mark information, configuring the partition attribute number field corresponding to the sampling point, and generating sampling point fluctuation level mark information.

5. The cloud-based ozone monitoring data collaborative management method according to claim 4, characterized in that: The steps for obtaining the sampling task partition adjustment information are specifically as follows: S301: Based on the sampling point fluctuation level tag information, according to the number, spatial coordinates and fluctuation level value of each sampling point, the positions of adjacent sampling points are retrieved on the ozone sampling task partition map, and based on the continuity of coordinates and consistency of fluctuation levels, clustered points of the same level are extracted. Based on the spatial position distribution of the same fluctuation level point set in the task partition map, the boundary coordinates are extracted to form a closed polygonal area, and a fluctuation level cluster boundary value group is generated; S302: Calling the fluctuation level aggregation boundary value group, combining the area number distribution in the original task partition map, and obtaining the number and position distribution of sampling points in the original area number according to the spatial density distribution in each boundary value group, replacing the area numbers according to the distribution positions of the sampling points in the boundary value group while retaining the proportion of edge sampling points, to generate an area number edge correction value group; S303: Call the area number edge correction value group, extract the numbers belonging to the same boundary value group according to the number replacement relationship, replace the target area number, re-assign a unified number identifier, merge them into independent sampling unit areas, and unify the replacement results into an area division mapping table and upload it to the cloud task scheduling control center to generate sampling task partition adjustment information.

6. The cloud-based ozone monitoring data collaborative management method according to claim 5, characterized in that: The steps for obtaining the task scheduling priority tag information are specifically as follows: S401: Calling the sampling task partition adjustment information, obtaining each sampling point number and the corresponding real-time ozone concentration value in each partition, aggregating all concentration values ​​of the partition according to the sampling point number, calculating the concentration mean of each partition, and generating the partition average concentration value; S402: Calculating the ozone concentration level of each partition based on the average concentration value of each partition and the fluctuation of the ozone concentration in each partition, and generating partition concentration assessment information; S403: Call the partition concentration assessment information, combine it with the task scheduling priority parameters, extract the scheduling priority tag bound to the ozone concentration level, establish a scheduling tag list that matches the task batch and priority level, and generate task scheduling priority tag information.

7. The cloud-based ozone monitoring data collaborative management method according to claim 6, characterized in that: The formula for calculating the ozone concentration level in each zone is: Among them, L i represents the concentration level of the ith partition, n i Represents the number of sampling points in the i-th partition, M i represents the average ozone concentration value of the ith partition, E represents the average concentration influence coefficient, O k,i represents the real-time ozone concentration value of the kth sampling point in the i-th partition, W i represents the fluctuation range of the ozone concentration difference sequence in the i-th partition, D i Represents the normalized value of the farthest spatial distance of the sampling points in the i-th partition.

8. The cloud-based ozone monitoring data collaborative management method according to claim 7, characterized in that: The method further comprises: S5: Based on the task scheduling priority tag information, the marked partition scheduling level and the number of sampling point distribution, determine whether the task scale of each priority level matches the sampling resource capacity. For priority level tasks that exceed the scheduling capacity, screen the sampling point numbers that can be delayed. Combined with the sampling frequency control rules, organize the task set that needs to be executed synchronously in the current scheduling round, and generate sampling scheduling collaborative control information; The sampling scheduling collaborative control information includes a synchronous execution task number set, a delayable sampling point index group, and a collaborative control scheduling priority configuration table.

9. The cloud-based ozone monitoring data collaborative management method according to claim 8, characterized in that: The steps for acquiring the sampling scheduling coordinated control information are specifically as follows: S501: Based on the task scheduling priority tag information, according to the task partition number corresponding to each priority level, the total number of sampling point numbers in each partition is counted, the upper limit of the sampling resource configuration capacity in the current scheduling batch is extracted, and the deviation between the task sampling quantity corresponding to each priority level and the resource capacity is calculated to generate a task resource matching deviation value; The formula for calculating the deviation between the number of task samples corresponding to each priority level and the resource capacity is: Among them, P d Represents the capacity deviation of the d-th priority task, C j,d represents the number of sampling points in the jth partition of the dth priority level, Q j,d represents the scheduling level weight coefficient of the jth partition in the dth priority level, S j,d represents the mean square error of the ozone concentration values ​​at the jth sampling point in the dth priority level, V j,d represents the spatial distribution density adjustment coefficient of the jth partition in the dth priority level, R d represents the upper limit of the sampling resource configuration capacity of the dth priority level, and m represents the total number of task partitions under the dth priority level; S502: calling the task resource matching deviation value, identifying the priority number with resource overrun, extracting all sampling point numbers in the corresponding task partition, screening the sampling interval for each number according to the set sampling frequency control rule, retaining the number set that does not meet the frequency trigger condition, and generating a set of delayable sampling point numbers; S503: Based on the set of delayable sampling point numbers, the delayed execution sampling point numbers are removed from the current scheduling task set, and the remaining sampling point numbers are sorted in combination with the partition numbers according to the priority order, a list of sampling points that need to be synchronously executed in the current scheduling round is established, and sampling scheduling collaborative control information is generated.

10. A cloud-based ozone monitoring data collaborative management system, characterized in that: The cloud-based ozone monitoring data collaborative management method according to any one of claims 1 to 9, wherein the system comprises: The difference grouping module obtains the ozone concentration of each sampling point in the current task scheduling batch, combines the corresponding ozone concentration values ​​of the previous batch sampling period, calculates multiple concentration difference grouping intervals, and generates the fluctuation difference grouping standard; The fluctuation level partitioning module calls the fluctuation difference grouping standard, obtains the numerical difference between the concentration of each sampling point in the current task and the corresponding concentration of the previous batch, determines the corresponding difference interval to which the difference of each sampling point belongs, combines and stores the sampling point number and the demarcation level, and generates sampling point fluctuation level marking information; The task partition adjustment module calibrates the boundaries of the fluctuation level clustering area on the ozone sampling task partition map based on the sampling point fluctuation level mark information, performs edge correction on the area number in the original task partition according to the spatial distribution density, transmits the adjusted division information to the cloud task scheduling control center for synchronous coverage, and generates sampling task partition adjustment information; The scheduling priority adjustment module calls the sampling task partition adjustment information, calculates the ozone concentration level of each partition based on the real-time ozone concentration value of the sampling point corresponding to each partition, assigns a scheduling priority level label to each partition in the current task batch, and generates task scheduling priority label information; The scheduling collaborative control module screens the sampling point numbers that can be delayed according to the task scheduling priority tag information, organizes the task set that needs to be executed synchronously in the current scheduling round, and generates sampling scheduling collaborative control information.

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