Intelligent land pollution assessment method and system based on water quality big data analysis

Abstract

The invention discloses an intelligent land pollution assessment method and system based on water quality big data analysis, relates to the technical field of environment monitoring and assessment, and is used for solving the problems of uneven integration and decision delay of multi-source water quality monitoring data. The method comprises the steps of analyzing time-space characteristics and spatial relevance of pollution, calculating pollution complexity and distributing treatment tasks, evaluating feasibility of a treatment target and calculating resource balance and action priority based on a task simulation result and budget data, and finally executing or adjusting the treatment tasks according to an evaluation result. And intelligent decision and dynamic optimization of water and soil pollution treatment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring and assessment, and more specifically, to a land pollution intelligent assessment method and system based on water quality big data analysis. Background Art

[0002] With the continuous development of environmental monitoring technology, water quality monitoring and land pollution assessment have gradually become important research directions in the field of environmental protection. Land pollution intelligent assessment methods and systems based on big data analysis are gradually becoming a research hotspot in this field due to their high efficiency and accuracy. However, the existing related technical solutions still have significant deficiencies in data collection, processing and application, and it is difficult to meet the needs of efficient and accurate land pollution assessment.

[0003] The prior art has the following deficiencies: At present, there are still obvious deficiencies in the comprehensive application of multi-source data collection, big data analysis capabilities and land pollution assessment. Especially in the face of complex environmental monitoring scenarios, existing technologies are difficult to achieve integrated analysis of multi-source water quality monitoring data, and it is also impossible to build a comprehensive and accurate land pollution assessment model. Therefore, a land pollution intelligent assessment method and system based on water quality big data analysis is proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a land pollution intelligent assessment method and system based on water quality big data analysis, which evaluates the pollution complexity by comprehensively considering the pollution diffusion coefficient and the regional correlation index to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a land pollution intelligent assessment system based on water quality big data analysis, comprising a data acquisition module, a complexity analysis module, a resource assessment module and a task optimization module, and the modules are signal connected; The data acquisition module is used to obtain water quality data and land pollution data in polluted areas; The complexity analysis module pre-processes the collected data, analyzes the spatiotemporal heterogeneity of land pollution and calculates the pollution diffusion coefficient. Combined with the spatial correlation of water quality, it uses the multidimensional weight allocation method to calculate the regional correlation index, based on which the pollution complexity is determined and the governance tasks are assigned; The resource assessment module simulates the assigned tasks, obtains the proposed governance duration and operational requirements, and calculates the cost difference, resource difference and resource balance coefficient based on the budget data in the database, and comprehensively analyzes the feasibility and priority of the governance objectives; The task optimization module determines whether to implement or adjust the task based on the evaluation results and executes the final governance plan.

[0007] In a preferred embodiment, the data acquisition module accesses the water resource monitoring platform to obtain the water pollutant concentration and flow rate of the implementation area, connects to the flow meter to detect the flow of the implementation area in real time, accesses the geological monitoring database to obtain the stability of the geological structure of the implementation area, counts the vegetation coverage rate of the implementation area and respectively counts the concentrations of heavy metals, organic matter and inorganic salt pollutants to calculate the soil pollutant ratio, and sets a period of time as the environmental change analysis time to collect ecological change data in the implementation area.

[0008] In a preferred embodiment, the complexity analysis module performs preprocessing on water quality data and land pollution data in the following specific steps: for water quality data, the result obtained by subtracting the maximum and minimum values ​​of chemical oxygen demand, total nitrogen and total phosphorus is used as the standardized result, which is marked as x, y and z respectively; for flow rate and flow, a plurality of historical time periods with the same sample time are selected as the analysis time with the time interval of collecting flow as the sample time, the maximum flow rate, minimum flow rate, maximum flow rate and minimum flow rate within the analysis time are obtained, and the Min-Max normalization method is used to process them, the result after flow rate normalization is used as the flow rate coefficient, the result after flow rate normalization is used as the flow rate coefficient, the water temperature is the ratio of the water temperature in the implementation area to the standard temperature, and its numerical range is limited to between 0 and 1, which is used as the water temperature coefficient; For land pollution data, the vegetation coverage rate is calculated based on the ratio of the vegetation coverage area to the total area of ​​the implementation area, and the concentrations of heavy metal, organic matter and inorganic salt pollutants are standardized to obtain the heavy metal pollutant ratio, organic matter pollutant ratio and inorganic salt pollutant ratio respectively.

[0009] In a preferred embodiment, the complexity analysis module uses a nonlinear mapping method to analyze spatial correlation. The specific steps are as follows: first determine which pollution type the evaluation unit belongs to, and construct a nonlinear mapping model with the result of the normalization of the pollutant concentration of the corresponding pollution type and the corresponding flow parameter coefficient; When the nonlinear mapping calculation result exceeds the preset spatial correlation threshold, the evaluation unit is judged to have high spatial correlation; when the nonlinear mapping calculation result is lower than the preset spatial correlation threshold, the evaluation unit is judged to have low spatial correlation.

[0010] In a preferred embodiment, the complexity analysis module uses a multi-dimensional weight allocation method to calculate the regional correlation index. The specific steps are as follows: The implementation area is divided into N unit space areas, which are classified into chemical oxygen demand area, total nitrogen area and total phosphorus area according to pollution type. The nonlinear mapping calculation results are obtained and the product is summed with the corresponding pollutant concentration standardized results as the weight coefficient. The pollution weight is set using the hierarchical analysis method. The regional correlation index can be calculated by weighted summing the pollutant concentration standardized results and the pollution weight.

[0011] In a preferred embodiment, the complexity analysis module analyzes spatiotemporal heterogeneity and uses a dynamic clustering method to calculate the pollution diffusion coefficient. The specific steps are as follows: The environmental change analysis time is divided into two periods with the same time interval, marked as the early period and the late period respectively. The average ecological changes of the two periods are calculated and the absolute value of the difference is taken as the ecological change coefficient. The temporal and spatial heterogeneity of the implementation area is judged by comparing the ecological change coefficient with the preset ecological difference threshold. If the ecological change coefficient exceeds the ecological difference threshold, the implementation area is judged to have high spatiotemporal heterogeneity; otherwise, the implementation area is judged to have low spatiotemporal heterogeneity. A dynamic clustering model is constructed, and the corresponding variable regression coefficient is obtained by solving the equation group by taking the partial derivative of the residual sum of squares and setting the partial derivative equal to 0, and then the pollution diffusion coefficient is calculated.

[0012] In a preferred embodiment, when the pollution complexity is low, the complexity analysis module uses a dynamic weighted allocation method to optimize the governance tasks. The specific steps are as follows: Taking the regional correlation index and pollution diffusion coefficient as input and the governance priority coefficient as output, a dynamic weighted model is established. The distance weighted method is used to calculate the local weighted coefficient, and the weight function is set using the exponential kernel function. After calculating the governance priority coefficient, the allocation ratio is set, and the average value of the governance data set is calculated. The governance priority coefficient of each assessment unit is divided by the average value of the governance data set. The calculation result is used as the allocation ratio, and the task volume of each governance task is allocated according to the allocation ratio.

[0013] In a preferred embodiment, the resource evaluation module automatically calculates the proposed governance duration and the proposed operational resource requirements according to the governance tasks of each evaluation unit and accesses the database to obtain the proposed governance duration budget and the proposed operational resource configuration; The difference between the budgeted governance time and the proposed governance time is subjected to the same normalization treatment as the ecological change coefficient. The normalized result of the difference between the budgeted governance time and the proposed governance time is used as the cost difference coefficient. The resource balance coefficient is calculated using the multi-factor combination decision-making formula. The normalized result of the difference between the operating resource allocation and the proposed operating resource demand is used as the operating difference coefficient. The operating difference coefficient and the cost difference coefficient are summed and averaged to obtain the governance action coefficient, marked as r. The governance action coefficient is compared with the preset action threshold to analyze the feasibility of the target.

[0014] In a preferred embodiment, the resource assessment module randomly selects N assessment units in the implementation area and calculates the governance action coefficient and resource balance coefficient of each assessment unit respectively; The governance action coefficients and resource balance coefficients of N evaluation units are merged into the governance action data set and the resource balance data set respectively, and the execution priority is calculated using the multi-factor decision-making method. When the execution priority exceeds the preset execution threshold, the governance task is judged to be executable. When the execution priority is lower than the preset execution threshold, the governance task is judged to be unexecutable.

[0015] A land pollution intelligent assessment method based on water quality big data analysis includes S1: collecting water quality data and land pollution data of the polluted area and preprocessing them; S2: Analyze the spatiotemporal heterogeneity of land pollution and calculate the pollution diffusion coefficient. Combined with the spatial correlation of water quality, use the multidimensional weight allocation method to calculate the regional correlation index, based on which the pollution complexity is determined and the governance tasks are allocated; S3: Perform simulation based on the assigned tasks to obtain the proposed governance duration and operational requirements, and calculate the cost difference, resource difference and resource balance coefficient based on the budget data in the database to comprehensively analyze the feasibility and priority of the governance objectives; S4: Determine whether to implement or adjust the task based on the evaluation results, and implement the final governance plan Technical effects and advantages of the present invention: 1. The present invention collects multidimensional data related to water quality and land pollution, pre-processes the collected data, analyzes the spatiotemporal characteristics and spatial correlation of pollution, calculates the complexity of pollution and assigns governance tasks, and then evaluates the feasibility of governance goals and calculates resource balance and action priorities based on task simulation results and budget data. Finally, the governance tasks are executed or adjusted according to the evaluation results, thereby realizing intelligent decision-making and dynamic optimization of water and soil pollution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a module schematic diagram of a land pollution intelligent assessment system based on water quality big data analysis according to the present invention; Figure 2 This is a method flow chart of a land pollution intelligent assessment method based on water quality big data analysis of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0018] The present invention provides a land pollution intelligent assessment method and system based on water quality big data analysis. The core of the method is to integrate water quality big data with land pollution monitoring data, and through a series of complex calculation and analysis processes, to achieve intelligent assessment of land pollution and optimize the allocation of governance tasks. Figure 1 The module diagram in the figure describes in detail the specific implementation mode of the present invention; Attached Figure 1 The module structure of the system is demonstrated, including data collection module, complexity analysis module, resource assessment module and task optimization module. The signal connections between the modules are also shown. The whole process from data collection to governance task implementation and adjustment is further detailed through the flow chart: In the specific implementation, the data acquisition module first completes the collection of water quality data and land pollution data.

[0019] Water quality data mainly include water pollutant concentrations and flow parameters. The water pollutant concentrations include chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP), while the flow parameters include flow velocity, flow rate and water temperature.

[0020] In order to obtain data, the data acquisition module accesses the water resources monitoring platform to obtain the water pollutant concentration and flow rate information in the implementation area, while connecting to the flow meter to detect the flow data of the implementation area in real time, and accesses the geological monitoring database to obtain the geological structure stability information of the implementation area.

[0021] In addition, the data collection module also counts the vegetation coverage rate of the implementation area and calculates the concentration ratios of heavy metals, organic matter and inorganic salt pollutants respectively, thereby obtaining the soil pollutant ratio.

[0022] The data collection module will also set a period of time as the environmental change analysis time to collect ecological change data in the implementation area. After the data is sorted, it will be sent to the complexity analysis module for further processing.

[0023] After receiving the water quality data and land pollution data, the complexity analysis module first preprocesses the data.

[0024] For water quality data, the complexity analysis module uses the maximum and minimum normalization method to standardize the chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP), and takes the result obtained by subtracting the maximum and minimum values ​​as the standardized result, which are marked as x, y and z respectively.

[0025] For flow velocity and flow, the complexity analysis module uses the time interval for collecting flow as the sample time, selects multiple historical time periods with the same sample time as the analysis time, obtains the maximum flow velocity, minimum flow velocity, maximum flow rate and minimum flow rate within the analysis time, and uses the Min-Max normalization method to process them. The result after flow velocity normalization is used as the flow velocity coefficient, and the result after flow rate normalization is used as the flow coefficient.

[0026] The water temperature is calculated by the ratio of the regional water temperature to the standard temperature, and its value range is limited to between 0 and 1, which is used as the water temperature coefficient.

[0027] For land pollution data, the complexity analysis module calculates the vegetation coverage rate based on the ratio of the vegetation coverage area to the total area of ​​the implementation area, and standardizes the concentrations of heavy metal, organic and inorganic salt pollutants to obtain the heavy metal pollutant ratio, organic pollutant ratio and inorganic salt pollutant ratio respectively.

[0028] After completing data preprocessing, the complexity analysis module uses the dynamic clustering method to calculate the pollution diffusion coefficient for analyzing spatiotemporal heterogeneity.

[0029] The specific steps are as follows: The complexity analysis module divides the environmental change analysis time into two periods of equal time interval, marked as the early period and the late period, and calculates the average ecological changes in the two periods and takes the absolute value of the difference, which is used as the ecological change coefficient.

[0030] By comparing the ecological change coefficient with the preset ecological difference threshold, the spatiotemporal heterogeneity of the implementation area can be judged.

[0031] If the ecological change coefficient exceeds the ecological difference threshold, the spatiotemporal heterogeneity of the implementation area is judged to be high; otherwise, the spatiotemporal heterogeneity of the implementation area is judged to be low.

[0032] Subsequently, the complexity analysis module constructs a dynamic clustering model to calculate the pollution diffusion coefficient.

[0033] The model expression is: y = h0 - h1x1 + h2x2 + h3x3 + h4x4 + d; Among them, y is the pollution diffusion coefficient, x1 is the vegetation coverage rate, x2 is the heavy metal pollutant ratio, x3 is the organic pollutant ratio, x4 is the inorganic salt pollutant ratio, d is the random error term, and h1, h2, h3 and h4 are the regression coefficients of the corresponding variables respectively.

[0034] When solving the regression coefficient, the complexity analysis module obtains a system of five linear equations by taking the partial derivative of the residual sum of squares and setting the partial derivative equal to 0. Solving the system of equations can obtain the regression coefficient of the corresponding variable, and then calculate the pollution diffusion coefficient. At the same time, the complexity analysis module also uses nonlinear mapping methods to analyze spatial correlation.

[0035] The specific steps are as follows: The complexity analysis module first determines which pollution type the evaluation unit belongs to. After confirmation, a nonlinear mapping model is constructed based on the results of the standardized pollutant concentration of the corresponding pollution type and the corresponding flow parameter coefficient. The model expression is: M = f(P,Q); Among them, M is the result of nonlinear mapping calculation, f is the nonlinear function, P is the result of normalization of pollutant concentration of corresponding pollution type, and Q is the corresponding flow parameter coefficient.

[0036] The complexity analysis module obtains the three-dimensional coordinate range of the evaluation unit, obtains the proportion of pollution types in the evaluation unit, and classifies the pollution types of the evaluation unit, and takes the type with the largest proportion of pollution types as the reference type.

[0037] For example, if the chemical oxygen demand accounts for the largest proportion in the assessment unit, the corresponding pollution type is chemical oxygen demand, and the corresponding flow parameter is the flow rate coefficient, that is, P is the standardized result of chemical oxygen demand, and Q is the flow rate coefficient.

[0038] When the nonlinear mapping calculation result exceeds the preset spatial correlation threshold, the evaluation unit spatial correlation is judged to be high; when the nonlinear mapping calculation result is lower than the preset spatial correlation threshold, the evaluation unit spatial correlation is judged to be low; After completing the analysis of spatiotemporal heterogeneity and spatial correlation, the complexity analysis module combines the results of the two to evaluate the complexity of pollution.

[0039] When the spatial correlation of the assessment units is high and the spatiotemporal heterogeneity of the implementation area is low, the pollution complexity is judged to be low; otherwise, the pollution complexity is judged to be high.

[0040] When the pollution complexity is low, the complexity analysis module uses a dynamic weighted allocation method to optimize the governance tasks.

[0041] The specific steps are as follows: first determine the input and output, taking the regional correlation index and pollution diffusion coefficient as input, and the governance priority coefficient as output.

[0042] Subsequently, spatial data are prepared to collect spatial location information of each assessment unit in the implementation area, such as longitude and latitude coordinates.

[0043] Then a dynamic weighted model is established, and the model expression is: Z = β0(ui,vi)-β1(ui,vi)α1 + β2(ui,vi)α2 + ε; Among them, Z is the governance priority coefficient, β0, β1 and β2 are local weighting coefficients, (ui, vi) are the coordinates of the ith evaluation unit, α1 is the regional correlation index, α2 is the pollution diffusion coefficient, and ε is the random error term.

[0044] The complexity analysis module uses the distance weighted method to calculate the local weighted coefficient, and its weight function is set using the exponential kernel function.

[0045] When calculating the governance priority coefficient, the calculated local weighted coefficient is used to calculate the governance priority coefficient Z of all evaluation units through a dynamic weighted model and merge them into a governance data set.

[0046] When setting the allocation ratio, the average value of the governance data set is calculated, and the governance priority coefficient of each assessment unit is divided by the average value of the governance data set to obtain the calculated result as the allocation ratio.

[0047] Finally, the task volume of each governance task is allocated according to the allocation ratio. The formula is T = α × T, where α is the allocation ratio, T is the governance task averagely allocated to each assessment unit in the entire implementation area, and T is the governance task of each assessment unit.

[0048] The resource assessment module is responsible for collecting budget data and simulation operation data, and analyzing the feasibility of governance objectives through budget data and simulation operation data.

[0049] Budget data includes governance time budget and operational resource allocation, and simulation operation data includes proposed governance time budget and proposed operational resource allocation.

[0050] The resource assessment module simulates the governance tasks assigned by the complexity analysis module and automatically calculates the proposed governance time budget and proposed operational resource allocation.

[0051] Subsequently, the resource assessment module accesses the database to obtain the proposed governance duration budget and the proposed operating resource allocation, and performs the same standardized processing as the ecological change coefficient after subtracting the governance duration budget from the proposed governance duration. The standardized result of the difference between the governance duration budget and the proposed governance duration is used as the cost difference coefficient.

[0052] The resource evaluation module uses a multi-factor combination decision formula to calculate the resource balance coefficient. The formula is: f = u × 1 + v × 2; Among them, f is the resource balance coefficient, u is the cost difference coefficient, and v is the result of the standardized ecological change coefficient.

[0053] When the cost difference coefficient is larger or the ecological change coefficient is larger, the task implementation time is more relaxed and the resource balance coefficient is larger.

[0054] At the same time, the resource assessment module uses the standardized result of the difference between the operating resource configuration and the planned operating resource demand as the operating difference coefficient, and sums and averages the operating difference coefficient and the cost difference coefficient to obtain the governance action coefficient, which is marked as r.

[0055] Compare the governance action coefficient with the preset action threshold to analyze the feasibility of the target. When the governance action coefficient exceeds the action threshold, the feasibility of the target is judged to be high; otherwise, the feasibility of the target is judged to be low.

[0056] The resource assessment module randomly selects N assessment units in the implementation area and calculates the governance action coefficient and resource balance coefficient of each assessment unit.

[0057] It should be noted that the duration budget data and operation feedback data of each unit are formulated based on the average budget data of each evaluation unit and the allocation ratio of each evaluation unit's governance tasks. For example, if the allocation ratio of a unit is 1.2, if the average budget duration of each evaluation unit is 2000 seconds, and the average operation configuration is 5 monitoring devices, then the corresponding unit budget expenditure is 2400 seconds, and the corresponding unit budget operation configuration is 6 monitoring devices; when the operation configuration is calculated according to the ratio, the calculation result will be rounded up.

[0058] It should be noted that the limitation of the operation configuration may be operation configurations such as monitoring equipment or drone monitoring, and the specific operation configuration type is not limited and will not be described in detail here; The governance action coefficients and resource balance coefficients of N evaluation units are merged into the governance action dataset and resource balance dataset, respectively.

[0059] The comprehensive governance action dataset and resource balance dataset use the multi-factor decision-making method to calculate the execution priority. The specific steps are as follows: First, a linear combination of the execution priority is constructed, and the formula is: s = w1r + w2f; Among them, s is the execution priority, r is the average value of the governance action data set, f is the average value of the resource balance data set, w1 is the optimal coefficient of r, and w2 is the optimal coefficient of f.

[0060] The governance action coefficient and resource balance coefficient calculated for each evaluation unit are summed, S = r + f, and w1 and w2 are calculated using the multi-factor decision formula, which is min(1 / 2N × (S -(w1r + w2f))2 + γ × (|w1| + |w2|)), where γ is the regularization parameter.

[0061] When w1 and w2 minimize the calculated results using the multi-factor decision formula, w1 and w2 are the optimal coefficients of r and f respectively.

[0062] The calculated execution priority is compared with the preset execution threshold. When the execution priority exceeds the preset execution threshold, the governance task is judged to be executable, and the resource assessment module sends a governance task confirmation signal to the task optimization module; When the execution priority is lower than the preset execution threshold, the governance task is judged to be unexecutable, and the resource assessment module sends the calculated execution priority and the governance task adjustment signal to the task optimization module; The task optimization module is responsible for receiving the signals sent by the resource assessment module and implementing the governance task content or adjusting the governance task content before implementation.

[0063] When the task optimization module receives a signal to confirm the governance task, it implements the evaluation unit governance task given by the complexity analysis module; when it receives a signal to adjust the governance task content, it adjusts the allocation ratio according to the execution priority, and the product of the allocation ratio and the execution priority can be used as the new allocation ratio. The evaluation unit governance tasks are allocated and implemented according to the new allocation ratio.

[0064] Specifically, the following water pollution source detection methods can be used before the assessment: When collecting acoustic disturbance data, water environment data are collected simultaneously, including four indicators: water temperature, pH value, dissolved oxygen concentration and flow rate.

[0065] The first group of water environment data is four types of indicators at the bottom mud (water temperature, pH value, dissolved oxygen concentration and flow rate); the second group of water environment data is four types of indicators 5 cm vertically upward from the bottom mud; the third group of water environment data is four types of indicators 15 cm vertically upward from the bottom mud; the fourth group of water environment data is four types of indicators 50 cm vertically upward from the bottom mud.

[0066] This setting is because when the sediment is disturbed, only the composition of the sediment is often considered, which cannot timely reflect the diffusion of the sediment and the actual nonlinear pollution behavior. By setting the water environment data of the gradient height, the changes in the close range after the sediment disturbance can be reflected first, and as the diffusion range increases, the set gradient gradually increases, which effectively prevents the collection of invalid or unclear data and facilitates the accuracy of subsequent analysis.

[0067] Calculate water environment gradient data based on water environment data; suppose water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; The water environment gradient data also consists of four groups; each group is a three-dimensional vector; the collection of water environment gradient data is to improve the sensitivity to abnormal pollution release events; traditional sediment collection and analysis only exist in the laboratory, which is far from enough if the timely response capability of the environmental protection department needs to be improved. By obtaining water environment gradient data and amplifying the gradient impact of sediment disturbance, it is conducive to timely and real-time detection and early warning of water pollution. The vector method can better reflect the gradient trend than single data.

[0068] The water environment gradient data and acoustic disturbance data are introduced into the time synchronization mechanism, which gives the water environment gradient data and acoustic disturbance data a unified timestamp, accurate to seconds. The timestamp information includes the acquisition date, hour, minute, second and quarterly identification, which is used to subsequently construct the time series input feature matrix. The quarterly information is automatically determined by the system's built-in calendar module. The introduction of time features can enhance the model's ability to identify periodic disturbance events and seasonal pollution release trends.

[0069] In summary, the present invention realizes intelligent assessment of land pollution and optimal allocation of governance tasks by integrating water quality big data and land pollution monitoring data, combining dynamic clustering method, nonlinear mapping method, dynamic weighted allocation method and multi-factor decision-making method and other algorithms. Through the above specific implementation methods, it can effectively reduce the problems of unreasonable allocation of governance tasks and overspending of governance time, which has important practical application value. Example 2

[0070] See also Figure 2 , an intelligent land pollution assessment method based on water quality big data analysis, including: S1: Collect water quality data and land pollution data of polluted areas and perform preprocessing; S2: Analyze the spatiotemporal heterogeneity of land pollution and calculate the pollution diffusion coefficient. Combined with the spatial correlation of water quality, use the multidimensional weight allocation method to calculate the regional correlation index, based on which the pollution complexity is determined and the governance tasks are allocated; S3: Perform simulation based on the assigned tasks to obtain the proposed governance duration and operational requirements, and calculate the cost difference, resource difference and resource balance coefficient based on the budget data in the database to comprehensively analyze the feasibility and priority of the governance objectives; S4: Determine whether to implement or adjust the task based on the evaluation results, and implement the final governance plan; The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0071] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0072] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0074] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A land pollution intelligent assessment system based on water quality big data analysis, characterized by: include: Data acquisition module, complexity analysis module, resource assessment module and task optimization module, and signal connections between modules; The data acquisition module is used to obtain water quality data and land pollution data in polluted areas; The complexity analysis module pre-processes the collected data, analyzes the spatiotemporal heterogeneity of land pollution and calculates the pollution diffusion coefficient. Combined with the spatial correlation of water quality, it uses the multidimensional weight allocation method to calculate the regional correlation index, based on which the pollution complexity is determined and the governance tasks are assigned; The resource assessment module simulates the assigned tasks, obtains the proposed governance duration and operational requirements, and calculates the cost difference, resource difference and resource balance coefficient based on the budget data in the database, and comprehensively analyzes the feasibility and priority of the governance objectives; The task optimization module determines whether to implement or adjust the task based on the evaluation results and executes the final governance plan.

2. According to claim 1, a land pollution intelligent assessment system based on water quality big data analysis is characterized by: The data acquisition module accesses the water resources monitoring platform to obtain the water pollutant concentration and flow rate in the implementation area, connects to the flow meter to detect the flow in the implementation area in real time, accesses the geological monitoring database to obtain the stability of the geological structure in the implementation area, calculates the vegetation coverage rate of the implementation area and separately calculates the concentration of heavy metals, organic matter and inorganic salt pollutants to calculate the soil pollutant ratio, and sets a period of time as the environmental change analysis time to collect ecological change data in the implementation area.

3. The land pollution intelligent assessment system based on water quality big data analysis according to claim 2 is characterized by: The specific steps of the complexity analysis module for preprocessing water quality data and land pollution data are as follows: For water quality data, the results obtained by subtracting the maximum and minimum values ​​of chemical oxygen demand, total nitrogen and total phosphorus are used as standardized results, marked as x, y and z respectively. For flow velocity and flow, the time interval of flow collection is used as the sample time, and multiple historical time periods with the same sample time are selected as the analysis time. The maximum flow velocity, minimum flow velocity, maximum flow rate and minimum flow rate within the analysis time are obtained, and the Min-Max normalization method is used to process them. The results after flow velocity normalization are used as flow velocity coefficients, and the results after flow rate normalization are used as flow rate coefficients. The water temperature is the ratio of the water temperature in the implementation area to the standard temperature, and its value range is limited to between 0 and 1, which is used as the water temperature coefficient; For land pollution data, the vegetation coverage rate is calculated based on the ratio of the vegetation coverage area to the total area of ​​the implementation area, and the concentrations of heavy metal, organic matter and inorganic salt pollutants are standardized to obtain the heavy metal pollutant ratio, organic matter pollutant ratio and inorganic salt pollutant ratio respectively.

4. The land pollution intelligent assessment system based on water quality big data analysis according to claim 3 is characterized by: The complexity analysis module uses the nonlinear mapping method to analyze spatial correlation. The specific steps are as follows: first determine which pollution type the assessment unit belongs to, and construct a nonlinear mapping model with the results of the standardized pollutant concentration of the corresponding pollution type and the corresponding flow parameter coefficient; When the nonlinear mapping calculation result exceeds the preset spatial correlation threshold, the evaluation unit is judged to have high spatial correlation; when the nonlinear mapping calculation result is lower than the preset spatial correlation threshold, the evaluation unit is judged to have low spatial correlation.

5. The land pollution intelligent assessment system based on water quality big data analysis according to claim 1 is characterized by: The complexity analysis module uses the multi-dimensional weight distribution method to calculate the regional correlation index. The specific steps are as follows: The implementation area is divided into N unit space areas, which are classified into chemical oxygen demand area, total nitrogen area and total phosphorus area according to pollution type. The nonlinear mapping calculation results are obtained and the product is summed with the corresponding pollutant concentration standardized results as the weight coefficient. The pollution weight is set using the hierarchical analysis method. The regional correlation index can be calculated by weighted summing the pollutant concentration standardized results and the pollution weight.

6. The land pollution intelligent assessment system based on water quality big data analysis according to claim 5 is characterized by: Complexity analysis module, analyze spatiotemporal heterogeneity and use dynamic clustering method to calculate pollution diffusion coefficient. The specific steps are as follows: The environmental change analysis time is divided into two periods with the same time interval, marked as the early period and the late period respectively. The average ecological changes of the two periods are calculated and the absolute value of the difference is taken as the ecological change coefficient. The temporal and spatial heterogeneity of the implementation area is judged by comparing the ecological change coefficient with the preset ecological difference threshold. If the ecological change coefficient exceeds the ecological difference threshold, the implementation area is judged to have high spatiotemporal heterogeneity; otherwise, the implementation area is judged to have low spatiotemporal heterogeneity. A dynamic clustering model is constructed, and the corresponding variable regression coefficient is obtained by solving the equation group by taking the partial derivative of the residual sum of squares and setting the partial derivative equal to 0, and then the pollution diffusion coefficient is calculated.

7. The land pollution intelligent assessment system based on water quality big data analysis according to claim 1 is characterized by: When the pollution complexity is low, the complexity analysis module uses a dynamic weighted allocation method to optimize the governance tasks. The specific steps are as follows: Taking the regional correlation index and pollution diffusion coefficient as input and the governance priority coefficient as output, a dynamic weighted model is established. The distance weighted method is used to calculate the local weighted coefficient, and the weight function is set using the exponential kernel function. After calculating the governance priority coefficient, the allocation ratio is set, and the average value of the governance data set is calculated. The governance priority coefficient of each assessment unit is divided by the average value of the governance data set. The calculation result is used as the allocation ratio, and the task volume of each governance task is allocated according to the allocation ratio.

8. The land pollution intelligent assessment system based on water quality big data analysis according to claim 7 is characterized by: The resource assessment module automatically calculates the proposed governance duration and proposed operational resource requirements based on the governance tasks of each assessment unit and accesses the database to obtain the proposed governance duration budget and proposed operational resource configuration; The difference between the budgeted governance time and the proposed governance time is subjected to the same normalization treatment as the ecological change coefficient. The normalized result of the difference between the budgeted governance time and the proposed governance time is used as the cost difference coefficient. The resource balance coefficient is calculated using the multi-factor combination decision-making formula. The normalized result of the difference between the operating resource allocation and the proposed operating resource demand is used as the operating difference coefficient. The operating difference coefficient and the cost difference coefficient are summed and averaged to obtain the governance action coefficient, marked as r. The governance action coefficient is compared with the preset action threshold to analyze the feasibility of the target.

9. The land pollution intelligent assessment system based on water quality big data analysis according to claim 8 is characterized by: The resource assessment module randomly selects N assessment units in the implementation area and calculates the governance action coefficient and resource balance coefficient of each assessment unit; The governance action coefficients and resource balance coefficients of N evaluation units are merged into the governance action data set and the resource balance data set respectively, and the execution priority is calculated using the multi-factor decision-making method. When the execution priority exceeds the preset execution threshold, the governance task is judged to be executable. When the execution priority is lower than the preset execution threshold, the governance task is judged to be unexecutable.

10. A land pollution intelligent assessment method based on water quality big data analysis, used to implement a land pollution intelligent assessment system based on water quality big data analysis as claimed in any one of claims 1 to 9, characterized in that: include: S1: Collect water quality data and land pollution data of polluted areas and perform preprocessing; S2: Analyze the spatiotemporal heterogeneity of land pollution and calculate the pollution diffusion coefficient. Combined with the spatial correlation of water quality, use the multidimensional weight allocation method to calculate the regional correlation index, based on which the pollution complexity is determined and the governance tasks are allocated; S3: Perform simulation based on the assigned tasks to obtain the proposed governance duration and operational requirements, and calculate the cost difference, resource difference and resource balance coefficient based on the budget data in the database to comprehensively analyze the feasibility and priority of the governance objectives; S4; Determine whether to implement or adjust the task based on the evaluation results, and execute the final governance plan.

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