Method and device for determining monitoring point of pollution source in drainage pipe network

Abstract

The invention provides a method and a device for determining a monitoring point of a pollution source in a drainage pipe network, belongs to the technical field of drainage treatment, and aims to solve the problems that an existing monitoring point tracking mode consumes a lot of manpower resources and time and is difficult to accurately position the pollution source. The method comprises the following steps: constructing a three-dimensional parameter inversion model of pollutant emission related data in the drainage pipe network; according to the three-dimensional parameter inversion model, pollutant emission monitoring points are determined; calculating an information entropy value of the pollutant emission monitoring point by taking the uncertainty minimization of pollution source tracking as a target; and determining an optimal monitoring point based on the information entropy value. According to the invention, the determination efficiency of the monitoring points can be effectively improved.

Description

Background Art

[0002] As a key component of modern urban infrastructure, the urban drainage system shoulders the heavy responsibility of rainwater discharge and domestic sewage and industrial wastewater treatment. With the rapid economic development and accelerated industrialization, it is facing increasingly severe challenges. At present, the cost of wastewater treatment remains high, causing some enterprises and individuals to resort to illegal discharge, stealth discharge and leakage in order to reduce costs, resulting in severe pollution of urban drainage pipe networks and threats to the safety of activated sludge in sewage treatment plants, which in turn has an adverse impact on water quality treatment and the ecological environment of the discharged water body.

[0003] In the traditional technical field, the tracking of pollution sources in drainage networks mainly relies on manual progressive searches upstream of the network. This method not only consumes a lot of time and manpower, but also makes it difficult to accurately locate the pollution source, which greatly increases the difficulty of evidence collection by regulatory authorities. In addition, the existing layout of monitoring points mostly relies on manual experience and simple judgment, lacking scientific and effective planning and technical support, which not only causes waste of resources, but also may cause monitoring management to fall into chaos and deviations in the direction of pollution source tracking, ultimately resulting in serious economic losses and environmental damage. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present invention provides a reasonable and efficient method and device for determining monitoring points of pollution sources in a drainage network.

[0005] According to a first aspect of an embodiment of the present invention, a method for determining monitoring points of pollution sources in a drainage network is provided, comprising: Construct a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; Determining pollutant emission monitoring points according to the three-dimensional parameter inversion model; Taking the uncertainty minimization of pollution source tracking as the goal, the information entropy value of the pollutant emission monitoring point is calculated; An optimal monitoring point is determined based on the information entropy value.

[0006] In some example embodiments of the present invention, based on the aforementioned scheme, the three-dimensional parameter inversion model is established based on a storm water management model; According to the three-dimensional parameter inversion model, determining the pollutant emission monitoring points includes: Controlling the three-dimensional parameter inversion model to be in a closed state; Calculating the three-dimensional parameter inversion model and generating a first format file and a second format file; Calling time series simulation values ​​to modify the simulation parameters of the first format file and the simulation parameters of the second format file at any time; The probability distribution in the Markov chain Monte Carlo method is set to Gaussian distribution to obtain a new Markov chain Monte Carlo method; Fitting the three-dimensional parameter inversion model, the simulation parameters of the first format file and the simulation parameters of the second format file, and using the new Markov chain Monte Carlo method to perform a centralized search for pollutant amounts to obtain search results; The pollutant emission monitoring point is determined according to the search result.

[0007] In some exemplary embodiments of the present invention, based on the above scheme, with the goal of minimizing the uncertainty of pollution source tracking, calculating the information entropy value of the pollutant emission monitoring point includes: With the goal of minimizing the uncertainty of pollution source tracking, the information entropy value of the pollutant emission monitoring point is calculated including: Taking the uncertainty minimization of pollution source tracking as the goal, an expected value calculation model of the information entropy of the pollutant emission monitoring point is established; Decomposing the expected value calculation model into a deterministic part and an uncertain part; Calculate the information entropy value of the deterministic part and the minimum information entropy value of the uncertain part; The information entropy value of the pollutant emission monitoring point is obtained according to the information entropy value of the deterministic part and the minimum value of the information entropy of the uncertain part.

[0008] In some example embodiments of the present invention, based on the foregoing solution, determining the optimal monitoring point based on the information entropy value includes: Monitoring point screening step: Screen out the monitoring point with the smallest information entropy value among all monitoring points to obtain the target monitoring point and the current remaining monitoring points; Judgment step: judge whether the number of target detection points reaches the preset value. If so, output all target monitoring points as the optimal monitoring points; otherwise, take the current remaining monitoring points as all monitoring points and return to the monitoring point screening step, and the number of target detection points increases by 1.

[0009] In some exemplary embodiments of the present invention, based on the above scheme, after determining the optimal monitoring point based on the information entropy value, the method for determining the monitoring point of the pollution source in the drainage network further includes: The monitoring of the optimal monitoring point is optimized and verified.

[0010] In some exemplary embodiments of the present invention, based on the above-mentioned solution, optimizing and verifying the monitoring of the optimal monitoring point includes: Randomly and evenly extract the information entropy value of the optimal monitoring point to obtain the extracted information entropy value; Use three-dimensional parameter inversion model simulation to generate concentration monitoring values; Setting the length of the Markov chain and inverting the pollutant parameters using the Markov chain Monte Carlo algorithm to obtain a number of posterior samples equal to the length of the Markov chain; Performing posterior statistics on the posterior samples to obtain a posterior mean; Calculating a posterior mean error between the posterior mean and the extracted information entropy value; Determine whether the posterior mean error is within a preset error range, and if so, output the optimal monitoring point; otherwise, redetermine the optimal monitoring point.

[0011] In some example embodiments of the present invention, based on the foregoing scheme, calculating the posterior mean error between the posterior mean and the extracted information entropy value includes: in, is the posterior mean And the extracted information entropy value The posterior mean error between is the number of assumed pollution parameter groups, Indicates the number of information entropy values ​​extracted, For the Group information entropy value, express No. types.

[0012] According to a second aspect of an embodiment of the present invention, there is provided a device for determining a monitoring point of a pollution source in a drainage network based on the above-mentioned method, comprising: Model building module, used to build a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; A monitoring point determination module, used to determine pollutant emission monitoring points according to the three-dimensional parameter inversion model; A calculation module, used to calculate the information entropy value of the pollutant emission monitoring point with the goal of minimizing the uncertainty of pollution source tracking; The optimal monitoring point determination module is used to determine the optimal monitoring point based on the information entropy value.

[0013] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for determining monitoring points of pollution sources in a drainage network in the first aspect is implemented.

[0014] According to a fourth aspect of an embodiment of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for determining monitoring points of pollution sources in a drainage network in the first aspect is implemented.

[0015] The technical solution provided by the embodiments of the present invention may have the following beneficial effects: In the embodiment of the present invention, by constructing a three-dimensional parameter inversion model of pollutant emission related data in the drainage network, the distribution and propagation of pollutants in the network can be comprehensively and three-dimensionally analyzed, providing a solid foundation for accurately determining monitoring points, thereby greatly improving the accuracy of pollutant emission monitoring; the information entropy value of the pollutant emission monitoring point is calculated with the goal of minimizing the uncertainty of pollution source tracking, and the optimal monitoring point is determined based on this, which can effectively reduce the monitoring error caused by uncertainty and further improve the accuracy of the monitoring results; therefore, the present invention can scientifically determine the pollutant emission monitoring points, avoid the blindness and arbitrariness of the traditional monitoring point setting, and make the layout of the monitoring points more reasonable and efficient.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] Figure 1 A schematic diagram showing a system architecture of an exemplary application environment of a method and device for determining monitoring points of pollution sources in a drainage network to which an embodiment of the present invention can be applied; Figure 2 A schematic diagram schematically shows a flow chart of a method for determining monitoring points of pollution sources in a drainage network according to some embodiments of the present invention; Figure 3 A flowchart of monitoring point optimization coupling calculation according to some embodiments of the present invention is schematically shown; Figure 4 A schematic diagram schematically shows a device for determining monitoring points of pollution sources in a drainage network according to some embodiments of the present invention; Figure 5 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present invention is shown; Figure 6 A schematic diagram of a computer-readable storage medium according to some embodiments of the present invention is schematically shown. DETAILED DESCRIPTION

[0019] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0020] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0022] Figure 1 A schematic diagram of a system architecture of an exemplary application environment of a method and apparatus for determining monitoring points of pollution sources in a drainage network to which an embodiment of the present invention can be applied is shown.

[0023] like Figure 1 As shown, the system architecture 100 may include one or more terminal devices such as a desktop computer 101, a portable computer 102, a smart phone 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables, etc. The terminal device may be any electronic device with data processing capabilities, which has a display screen for displaying the monitoring status of the pollution source monitoring point to the user, including but not limited to the above-mentioned desktop computers, portable computers, smart phones, etc. It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a sub-server cluster composed of multiple sub-servers.

[0024] The method for determining the monitoring point of the pollution source in the drainage network provided in the embodiment of the present invention can generally be executed by the terminal device, and accordingly, the device for determining the monitoring point of the pollution source in the drainage network is generally set in the terminal device. However, it is easy for those skilled in the art to understand that the method for determining the monitoring point of the pollution source in the drainage network provided in the embodiment of the present invention can also be executed by the server 105, and accordingly, the device for determining the monitoring point of the pollution source in the drainage network can also be set in the server 105, which is not particularly limited in this exemplary embodiment.

[0025] In addition, it should be understood that the monitoring point determination method of the pollution source in the drainage network of the embodiment of the present invention can be configured as a software module. In some implementation scenarios, the monitoring point determination scheme of the pollution source in the drainage network of the present invention can be deployed separately to generate the optimal monitoring point. In other implementation scenarios, the monitoring point determination scheme of the pollution source in the drainage network of the present invention can be deployed in other software as a functional module of the software, such as deployed in the analysis software of the underground pipeline. The present invention does not place any special restrictions on the application of the monitoring point determination method of the pollution source in the drainage network.

[0026] Next, the embodiments of the present invention are described in detail.

[0027] like Figure 2 As shown, Figure 2 The present invention is a flowchart of a method for determining a monitoring point of a pollution source in a drainage network according to an exemplary embodiment of the present invention, comprising the following steps: S210: Construct a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; S220: Determine pollutant emission monitoring points according to the three-dimensional parameter inversion model; S230: Calculating the information entropy value of the pollutant emission monitoring point with the goal of minimizing the uncertainty of pollution source tracking; S240: Determine an optimal monitoring point based on the information entropy value.

[0028] In S210, a three-dimensional parameter inversion model of pollutant emission related data in the drainage network is constructed.

[0029] The drainage network is a complex system used to collect and discharge urban domestic sewage, industrial wastewater, etc. In the drainage network, there are various pollutants discharged and flow.

[0030] Pollutant emission related data may include pollutant types and concentrations, water flow velocity and flow, pipe network geometry (including pipe diameter, length, slope and other information, these factors will affect the movement path of water flow and pollutants.) and time series data, etc. The present invention is not limited.

[0031] In the embodiment of the present invention, the pollutant emission related data includes pollutant emission nodes, emission amount and emission time in the drainage network. On this basis, the three-dimensional parameter inversion model of the pollutant emission related data is a three-dimensional parameter inversion model of pollutant emission nodes, emission amount and emission time in the drainage network.

[0032] In S220, pollutant emission monitoring points are determined according to the three-dimensional parameter inversion model.

[0033] It should be noted that those skilled in the art can construct a three-dimensional parameter inversion model of pollutant emission related data in any manner. For example, in some embodiments, the pollutant emission related data can be used to train some models in the machine learning algorithm to obtain a three-dimensional parameter inversion model.

[0034] In the embodiment of the present invention, the three-dimensional parameter inversion model is established based on the coupling of Storm Water Management Model (SWMM) and Markov chain Monte Carlo (MCMC).

[0035] The three-dimensional parameter inversion model of the present invention is constructed based on the SWMM model. On this basis, according to the three-dimensional parameter inversion model, the pollutant emission monitoring points are determined to include: Controlling the three-dimensional parameter inversion model to be in a closed state; Calculating the three-dimensional parameter inversion model and generating a first format file and a second format file; Calling time series simulation values ​​to modify the simulation parameters of the first format file and the simulation parameters of the second format file at any time; The probability distribution in the Markov chain Monte Carlo method is set to Gaussian distribution to obtain a new Markov chain Monte Carlo method; Fitting the three-dimensional parameter inversion model, the simulation parameters of the first format file and the simulation parameters of the second format file, and using the new Markov chain Monte Carlo method to perform a centralized search for pollutant amounts to obtain search results; The pollutant emission monitoring point is determined according to the search result.

[0036] In some embodiments, the first format file and the second format file may be format files suitable for programming language software. In the present invention, Python is selected as the programming language, and the SWMM model is calculated using Python. One of the generated first format file and the generated second format file is a *.rpt file and the other is a *.out file.

[0037] Manual simulation of SWMM software will increase the workload, make random parameter modification difficult, and fail to achieve centralized search of pollutant emissions. The process of calculating the SWMM model using Python does not require opening the SWMM model software, which can solve the problem of increased workload caused by manual simulation of SWMM software.

[0038] The present invention utilizes Python related library functions to call time series simulation values, and can modify input file simulation parameters at any time, thereby solving the problem of difficulty in randomly modifying parameters.

[0039] Finally, the probability distribution in the MCMC method is set to Gaussian distribution. After model coupling and parameter modification, the SWMM-MCMC coupled inversion model is obtained. The coupled inversion model is used to realize the centralized search of pollutant quantity in the simulation process. After simulation calculation, possible pollutant emission monitoring points are finally selected.

[0040] In S230, the information entropy value of the pollutant emission monitoring point is calculated with the goal of minimizing the uncertainty of pollution source tracking.

[0041] Entropy quantifies the uncertainty of random variables through the probability distribution of random variables. When the system becomes more orderly, the degree of uncertainty decreases and the information entropy decreases. Conversely, when the system is disordered, the uncertainty increases and the information entropy increases accordingly. Information entropy can effectively quantify the size of uncertainty. The mathematical expression of information entropy is as follows: in: represents a random variable; Represents the probability density function of a random variable with a priori range of [a,b].

[0042] The specific coupling calculation process of the monitoring point optimization model based on information entropy is as follows: Figure 3 As shown in the figure, it includes five steps: generating unknown parameters, modifying *.inp files, extracting and storing time series, calculating conditional probability functions, and calculating information entropy values. The automatic modification of the SWMM model and the extraction of time series are directly implemented in the Python program without opening the SWMM software. The optimization model is actually a coupling calculation of the information entropy algorithm and the SWMM model. The time series extracted by the SWMM model is directly used for the likelihood function. and Calculation.

[0043] The specific calculation principle of the information entropy value of the pollutant emission monitoring point is as follows: Use Python to directly calculate the SWMM model and extract pollutant concentration monitoring values , considering pollutant emission monitoring points as unknowns , use the inversion model to invert unknown parameters ,in The posterior probability density function is , For monitoring scheme, parameters The information entropy of the posterior distribution is similarly defined as follows: The left-hand item contains the monitored value However, due to the influence of actual conditions, the actual monitoring value cannot be obtained during the monitoring point optimization node stage. , so it is assumed that the monitoring value is a random variable, and the corresponding probability density function is Therefore, in order to more accurately screen the monitoring scheme, the information entropy calculation process is only associated with the monitoring scheme. Multiply both sides by , through Integrate to find the information entropy The expected value of is as follows: From this formula, we can see that Only monitored programs The influence of By understanding the relevant principles of information entropy, we know that the smaller the uncertainty, the smaller the information entropy value. Therefore, in order to reduce the uncertainty of pollution source tracking as much as possible, various monitoring schemes are solved here. The minimum value of information entropy under After continuous calculation, the optimal monitoring solution for the drainage network monitoring nodes is obtained. According to the concept of information root entropy, the optimal monitoring solution is used Layout the entire drainage network area and measure the pollutant concentration monitoring values , using the pollution source tracking model to invert unknown parameters , since the information entropy value of x is the minimum, the pollution source tracking effect is best at this time.

[0044] As can be seen from the above formula, it includes actual monitoring data and monitoring schemes. The entire information entropy solution process is relatively complicated and it is difficult to express a clear calculation result. Therefore, the present invention uses the Monte Carlo method to approximate the solution. Rewrite the above formula as: The prediction uncertainty of the expected value of information entropy can be divided into two parts: in, represents the total uncertainty in the model predictions, is the parameter uncertainty derived from within the model, It is the conceptual model uncertainty derived from the model.

[0045] After Transformation, parameters inside the model The prior distribution information entropy of is: From the above formula, we can see that The larger the prior range, the greater the parameter The larger the information entropy calculation value is, the greater the parameter The greater the uncertainty of the inversion result, the greater the uncertainty of the inversion result. When the information entropy value remains unchanged, remains unchanged, so to obtain The minimum value of , extract the second half, as shown in the following formula, only need to calculate The minimum value can be obtained by taking the minimum value of the information entropy of the corresponding monitoring scheme: pass It can be seen that the final value of information entropy is composed of two parts: the uncertainty of the prior parameters and the uncertainty under the influence of the monitoring scheme. Among them, the prior distribution has nothing to do with the entire model calculation process and is obtained from relevant data and experience. Moreover, when adding monitoring nodes, it can be clearly seen that the uncertainty of the entire model is reduced. Among them, This is further verified. The calculated result is less than zero, which shows that the use of monitoring points in the Bayesian formula reduces the inversion parameters. The Monte Carlo method is used to solve the following equation: First, from the unknown parameters Prior distribution Randomly select samples, denoted as ( i =1, 2, …, ); for each i ∈ , use python programming to modify the unknown parameter random samples of the SWMM model, and implement simulations to extract monitoring plans from them The conditional probability density function under the condition, and then from the function Randomly select 1 sample from , a total of Each group and Substituting into the above formula, we can calculate the conditional probability density function . According to the formula It can be seen that in the above formula , still using the Monte Carlo method to find this integral: Using the formula , and The approximate information entropy of each node in the pipeline network model of different monitoring schemes can be calculated.

[0046] That is, with the goal of minimizing the uncertainty of pollution source tracking, calculating the information entropy value of the pollutant emission monitoring point includes: in, represents the parameter uncertainty derived from within the 3D parameter inversion model, represents the posterior probability density function, represents the prior probability distribution of pollutant emission monitoring points, Indicates the pollutant emission monitoring point, Indicates the pollutant concentration monitoring value at the pollutant emission monitoring point, Indicates the monitoring plan, Represents the information entropy value of the pollutant emission monitoring point; Represents the conceptual model uncertainty derived from the 3D parameter inversion model; from the prior distribution Randomly selected from samples, denoted as ( i =1, 2, …, ); from the function Randomly select a sample from ; Each group and Substitute and calculate the conditional probability density function , Represents the natural logarithm of the likelihood function.

[0047] In S240, an optimal monitoring point is determined based on the information entropy value.

[0048] The pipe network in the study area of ​​this invention has a total of 32 nodes. If different numbers of monitoring points are optimized and combined, Solving the information entropy of all alternative monitoring schemes will generate a huge workload, and the monitoring cost will increase exponentially as the number of pipe network monitoring points increases.

[0049] As can be seen from the above, if a monitoring node is added, the possible results of the monitoring scheme will increase exponentially, and the information entropy calculation will also increase greatly. Therefore, in order to reduce the calculation time, the present invention uses the method of progressively adding nodes, that is, in the case of one node monitoring, the node with the smallest information entropy value is calculated and screened, and the node is used as one of the monitoring points in the case of two node monitoring, and then the other monitoring node is calculated and screened under this condition. And so on, the optimal multi-monitoring point combined monitoring scheme is finally determined.

[0050] That is, determining the optimal monitoring point based on the information entropy value includes: Monitoring point screening step: Screen out the monitoring point with the smallest information entropy value among all monitoring points to obtain the target monitoring point and the current remaining monitoring points; Judgment step: judge whether the number of target detection points reaches the preset value. If so, output all target monitoring points as the optimal monitoring points; otherwise, take the current remaining monitoring points as all monitoring points and return to the monitoring point screening step, and the number of target detection points increases by 1.

[0051] Since the amount of calculation is too large, the monitoring cost will increase with the increase of nodes. In the present invention, the monitoring nodes of the drainage network model are finally set within 5, and the generalized calculation formula of the node information entropy is as follows: Different monitoring schemes have different information entropies. The above formula is used to calculate the information entropy values ​​of nodes under different monitoring schemes. However, calculating only the information entropy value lacks credibility. In order to verify the monitoring point optimization model based on information entropy, the present invention introduces the relative error mean of the posterior results of pollution source tracking as a reference object, and linearly fits the two to obtain a linear relationship between the two.

[0052] After determining the optimal monitoring point based on the information entropy value, the method for determining the monitoring point of the pollution source in the drainage network further includes: The monitoring of the optimal monitoring point is optimized and verified.

[0053] Specifically, optimizing and verifying the monitoring of the optimal monitoring point includes: Randomly and evenly extract the information entropy value of the optimal monitoring point to obtain the extracted information entropy value; Use three-dimensional parameter inversion model simulation to generate concentration monitoring values; Setting the length of the Markov chain and inverting the pollutant parameters using the Markov chain Monte Carlo algorithm to obtain a number of posterior samples equal to the length of the Markov chain; Performing posterior statistics on the posterior samples to obtain a posterior mean; Calculating a posterior mean error between the posterior mean and the extracted information entropy value; Determine whether the posterior mean error is within a preset error range, and if so, output the optimal monitoring point; otherwise, redetermine the optimal monitoring point.

[0054] In the present invention, MH sampling method is used to randomly and uniformly obtain 20 sets of parameters α, and SWMM model simulation is used to generate 20×32 sets of concentration monitoring values. The length of each Markov chain is set to 10000, and the parameter α is inverted using MCMC algorithm. The posterior statistics of these 10000 sets of samples are performed to obtain the parameter posterior mean estimate MS. Finally, the true parameter α is substituted into expression: in, is the posterior mean And the extracted information entropy value The posterior mean error between is the number of assumed pollution parameter groups, Indicates the number of information entropy values ​​extracted, For the Group information entropy value, express No. types.

[0055] According to a second aspect of the embodiment of the present invention, referring to Figure 4 As shown, a monitoring point determination device 400 for a pollution source in a drainage network is also provided, comprising: A model building module 410 is used to build a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; A monitoring point determination module 420, for determining pollutant emission monitoring points according to the three-dimensional parameter inversion model; A calculation module 430 is used to calculate the information entropy value of the pollutant emission monitoring point with the goal of minimizing the uncertainty of pollution source tracking; The optimal monitoring point determination module 440 is used to determine the optimal monitoring point based on the information entropy value.

[0056] It should be noted that, although several modules and submodules of the monitoring point determination device for pollution sources in the drainage network are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or submodules described above can be concretized in one module or unit. Conversely, the features and functions of one module or submodule described above can be further divided into multiple modules or submodules to be concretized.

[0057] In addition, in an exemplary embodiment of the present invention, an electronic device capable of implementing the above-mentioned method for determining monitoring points of pollution sources in a drainage network is also provided.

[0058] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0059] Refer to the following Figure 5 An electronic device 500 according to such an embodiment of the present invention is described. Figure 5 The electronic device 500 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0060] like Figure 5 As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), and a display unit 540.

[0061] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps of various exemplary embodiments of the present invention described in the above “Exemplary Method” section. For example, the processing unit 510 can perform the following steps: Figure 2 S210 shown in: constructing a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; S220: determining pollutant emission monitoring points based on the three-dimensional parameter inversion model; S230: calculating the information entropy value of the pollutant emission monitoring point with the goal of minimizing the uncertainty of pollution source tracking; S240: determining the optimal monitoring point based on the information entropy value.

[0062] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522 , and may further include a read-only memory unit (ROM) 523 .

[0063] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0064] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0065] The electronic device 500 may also communicate with one or more external devices 570 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 550. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 560. Figure 5 As shown, the network adapter 560 communicates with other modules of the electronic device 500 via the bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0066] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present invention.

[0067] In an exemplary embodiment of the present invention, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present invention is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps of various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present invention.

[0068] refer to Figure 6, a program product 600 for implementing the above-mentioned method for determining monitoring points of pollution sources in a drainage network according to an embodiment of the present invention is described, which may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in the present invention, a readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, an apparatus, or a device.

[0069] The program product may use any combination of one or more readable storage media. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0070] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0071] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0072] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.

[0073] Other embodiments of the invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. The invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0074] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for determining monitoring points of pollution sources in a drainage network, characterized in that: include: Construct a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; Determining pollutant emission monitoring points according to the three-dimensional parameter inversion model; Taking the uncertainty minimization of pollution source tracking as the goal, the information entropy value of the pollutant emission monitoring point is calculated; An optimal monitoring point is determined based on the information entropy value.

2. The method for determining monitoring points of pollution sources in a drainage network according to claim 1, characterized in that: The three-dimensional parameter inversion model is established based on a storm water management model; According to the three-dimensional parameter inversion model, determining the pollutant emission monitoring points includes: Controlling the three-dimensional parameter inversion model to be in a closed state; Calculating the three-dimensional parameter inversion model and generating a first format file and a second format file; Calling time series simulation values ​​to modify the simulation parameters of the first format file and the simulation parameters of the second format file at any time; The probability distribution in the Markov chain Monte Carlo method is set to Gaussian distribution to obtain a new Markov chain Monte Carlo method; Fitting the three-dimensional parameter inversion model, the simulation parameters of the first format file and the simulation parameters of the second format file, and using the new Markov chain Monte Carlo method to perform a centralized search for pollutant amounts to obtain search results; The pollutant emission monitoring point is determined according to the search result.

3. The method for determining monitoring points of pollution sources in a drainage network according to claim 1, characterized in that: With the goal of minimizing the uncertainty of pollution source tracking, the information entropy value of the pollutant emission monitoring point is calculated including: Taking the uncertainty minimization of pollution source tracking as the goal, an expected value calculation model of the information entropy of the pollutant emission monitoring point is established; Decomposing the expected value calculation model into a deterministic part and an uncertain part; Calculate the information entropy value of the deterministic part and the minimum information entropy value of the uncertain part; The information entropy value of the pollutant emission monitoring point is obtained according to the information entropy value of the deterministic part and the minimum value of the information entropy of the uncertain part.

4. The method for determining monitoring points of pollution sources in a drainage network according to claim 1, characterized in that: Determining the optimal monitoring point based on the information entropy value includes: Monitoring point screening step: Screen out the monitoring point with the smallest information entropy value among all monitoring points to obtain the target monitoring point and the current remaining monitoring points; Judgment step: judge whether the number of target detection points reaches the preset value. If so, output all target monitoring points as the optimal monitoring points; otherwise, take the current remaining monitoring points as all monitoring points and return to the monitoring point screening step, and the number of target detection points increases by 1.

5. The method for determining monitoring points of pollution sources in a drainage network according to any one of claims 1 to 4, characterized in that: After determining the optimal monitoring point based on the information entropy value, the method for determining the monitoring point of the pollution source in the drainage network further includes: The monitoring of the optimal monitoring point is optimized and verified.

6. The method for determining monitoring points of pollution sources in a drainage network according to claim 5, characterized in that: Optimizing and verifying the monitoring of the optimal monitoring point includes: Randomly and evenly extract the information entropy value of the optimal monitoring point to obtain the extracted information entropy value; Use three-dimensional parameter inversion model simulation to generate concentration monitoring values; Setting the length of the Markov chain and inverting the pollutant parameters using the Markov chain Monte Carlo algorithm to obtain a number of posterior samples equal to the length of the Markov chain; Performing posterior statistics on the posterior samples to obtain a posterior mean; Calculating a posterior mean error between the posterior mean and the extracted information entropy value; Determine whether the posterior mean error is within a preset error range, and if so, output the optimal monitoring point; otherwise, redetermine the optimal monitoring point.

7. The method for determining monitoring points of pollution sources in a drainage network according to claim 6, characterized in that: Calculating the posterior mean error between the posterior mean and the extracted information entropy value includes: in, is the posterior mean And the extracted information entropy value The posterior mean error between is the number of assumed pollution parameter groups, Indicates the number of information entropy values ​​extracted, For the Group information entropy value, express No. types.

8. A device for determining monitoring points of pollution sources in a drainage network based on the method according to any one of claims 1 to 7, characterized in that: include: Model building module, used to build a three-dimensional parameter inversion model for pollutant emission related data in the drainage network; A monitoring point determination module, used to determine pollutant emission monitoring points according to the three-dimensional parameter inversion model; A calculation module, used to calculate the information entropy value of the pollutant emission monitoring point with the goal of minimizing the uncertainty of pollution source tracking; The optimal monitoring point determination module is used to determine the optimal monitoring point based on the information entropy value.

9. An electronic device, characterized in that: include: processor; as well as A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method for determining monitoring points of pollution sources in a drainage network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method for determining monitoring points of pollution sources in a drainage network as described in any one of claims 1 to 7 is implemented.

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