CN116681562A - River pollution tracing method and device - Google Patents
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Abstract
The embodiment of the disclosure provides a river pollution tracing method and device; the method is applied to the field of river water quality monitoring and management. The method comprises the steps of calling a space-time database to obtain river water quality monitoring point data to be detected; judging whether the monitoring point data is abnormal or not; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range; if the pollution enterprise is one, the pollution enterprise is used as a pollution source of the monitoring point; if the number of the polluted enterprises is multiple, acquiring water quality monitoring point data of other rivers in the upstream rivers of the monitoring points at the same time; judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained; and sequencing the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points and displaying sequencing results so as to position the pollution enterprises. In this way, a contaminated business can be accurately located and alerted.
Description
Technical Field
The disclosure relates to the field of river water quality monitoring and management, in particular to a river pollution tracing method and device.
Background
River pollution is a common form of water pollution and is more damaging. The most effective way to reduce river pollution is to reduce pollution sources, so the accuracy and real-time of the river pollution tracing method become particularly important. The existing pollution tracing algorithm is single, the condition that a plurality of pollution sources appear at the same time is not considered, the influence of a plurality of pollution factors is not considered, and real-time warning can not be carried out on a pollution enterprise.
Disclosure of Invention
The disclosure provides a river pollution tracing method, a river pollution tracing device, river pollution tracing equipment and a storage medium.
According to a first aspect of the present disclosure, a river pollution tracing method is provided. The method comprises the following steps:
invoking a space-time database to acquire river water quality monitoring point data to be detected;
judging whether the monitoring point data is abnormal or not; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range;
if the pollution enterprise is one, the pollution enterprise is used as a pollution source of the monitoring point; if the number of the polluted enterprises is multiple, acquiring water quality monitoring point data of other rivers in the upstream rivers of the monitoring points at the same time;
judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained;
and sequencing the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points and displaying sequencing results so as to position the pollution enterprises.
In some implementations of the first aspect, the generating of the spatio-temporal database includes:
coding river data, pollution enterprise data and river water quality monitoring point data through Beidou grid codes, and storing the river data, the pollution enterprise data and the river water quality monitoring point data into a space-time database; wherein,,
the river water quality monitoring point data comprises the position and detection data of the river water quality monitoring point.
In some implementations of the first aspect, obtaining pollution enterprise data for a river upstream of the monitoring point within a user-defined pollution range includes:
according to the user-defined pollution range size and an upstream river calculation algorithm, a Beidou grid range of the upstream river of the monitoring point is obtained, and according to the pollution enterprise data in the range and the space-time database, pollution enterprise data of the upstream river in the user-defined pollution range is obtained; wherein,,
depending on the user-defined contamination range size, including but not limited to:
the value of the monitoring point anomaly data is inversely proportional to the user-defined pollution range.
In some implementations of the first aspect, ranking the possibility that the pollution enterprise existing at the same time becomes a pollution source of the monitoring point and displaying the ranking result includes:
according to a pollution weight calculation algorithm, each pollution enterprise is associated with a plurality of monitoring points corresponding to the pollution enterprise;
traversing each pollution enterprise, accumulating calculation results of each index weight of the associated monitoring point corresponding to each pollution enterprise, and taking the results as pollution indexes of each pollution enterprise;
ranking the possibility that each pollution enterprise becomes a pollution source of the monitoring point according to the pollution index, wherein the greater the pollution index is, the earlier the ranking is;
and displaying the sequencing results of all polluted enterprises existing at the same time.
In some implementations of the first aspect, the method further includes:
for the situation that the data of the polluted enterprises of the river at the upstream of the same monitoring point are changed, the pollution indexes of the polluted enterprises of different levels at each monitoring point are cached; in particular, the method comprises the steps of,
when the user-defined pollution range varies in size, the pollution enterprise data of the river at the upstream of the same monitoring point in the considered pollution range can be changed along with the pollution enterprise data, and the pollution indexes of pollution enterprises at different levels of each monitoring point are cached so as to be directly called when being used next time; wherein,,
contaminated business data includes, but is not limited to, the name, number, location of the contaminated business.
According to a second aspect of the present disclosure, a river pollution tracing device is provided. The device comprises:
the calling module is used for calling the space-time database and acquiring river water quality monitoring point data to be detected;
the judging module is used for judging whether the monitoring point data is abnormal or not; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range;
if the pollution enterprise is one, the pollution enterprise is used as a pollution source of the monitoring point; if the number of the polluted enterprises is multiple, acquiring water quality monitoring point data of other rivers in the upstream rivers of the monitoring points at the same time;
judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained;
and the sequencing module is used for sequencing the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points and displaying sequencing results so as to position the pollution enterprises.
According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method as described above.
According to a fifth aspect of the present disclosure, the disclosed embodiments provide a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
In the method, whether the monitoring point data is abnormal or not is judged by acquiring the river water quality monitoring point data to be detected, and if so, pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range is acquired; judging pollution sources of the monitoring points according to the number of pollution enterprises, if the pollution enterprises are one, taking the pollution enterprises as the pollution sources of the monitoring points, if the pollution enterprises are a plurality of, acquiring data of other river water quality monitoring points in the river at the upstream of the monitoring points at the same time, and judging whether the data of the other monitoring points are abnormal; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained; the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points is sequenced and the sequencing result is displayed, so that the pollution enterprises can be accurately positioned, and abnormal data of the monitoring points and the pollution enterprises are warned and recorded in real time.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:
FIG. 1 shows a flow chart of a river pollution tracing method provided by an embodiment of the present disclosure;
fig. 2 shows a block diagram of a river pollution tracing device provided by an embodiment of the present disclosure;
fig. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Aiming at the problems in the background technology, the embodiment of the disclosure provides a river pollution tracing method and device. Specifically, judging whether the monitoring point data is abnormal or not by acquiring the river water quality monitoring point data to be detected, and if so, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range; judging pollution sources of the monitoring points according to the number of pollution enterprises, if the pollution enterprises are one, taking the pollution enterprises as the pollution sources of the monitoring points, if the pollution enterprises are a plurality of, acquiring data of other river water quality monitoring points in the river at the upstream of the monitoring points at the same time, and judging whether the data of the other monitoring points are abnormal; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained; the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points is sequenced and the sequencing result is displayed, so that the pollution enterprises can be accurately positioned, and abnormal data of the monitoring points and the pollution enterprises are warned and recorded in real time.
The river pollution tracing method and device provided by the embodiment of the disclosure are described in detail below through specific embodiments with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a river pollution tracing method provided by an embodiment of the disclosure, and a method 100 includes the following steps:
s110, calling a space-time database to acquire river water quality monitoring point data to be detected.
In some embodiments, the spatiotemporal database includes river data, pollution enterprise data, river water quality monitoring point data; in particular, the method comprises the steps of,
coding river data, pollution enterprise data and river water quality monitoring point data through Beidou grid codes, and storing the river data, the pollution enterprise data and the river water quality monitoring point data into a space-time database; in particular, the method comprises the steps of,
the Beidou grid codes decompose the earth into grids with different sizes, each grid is endowed with a globally unique identification code, the Beidou grid codes are used for carrying out light weight, discretization, gridding, fusion and coding on river data, polluted enterprise data and river water quality monitoring point data, and the gridded data are coded according to the coding rule of the Beidou grid codes and then stored in a space-time database; compared with the traditional engine storage space, the grid after coding can save 60%, and the space relation operation performance is improved by 30%.
In some embodiments, the river water quality monitoring point data includes the location and detection data of the river water quality monitoring point.
S120, judging whether the monitoring point data is abnormal or not; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in the user-defined pollution range.
In some embodiments, obtaining pollution enterprise data for a river upstream of the monitoring point within a user-defined pollution range includes:
and obtaining the Beidou grid range of the river upstream of the monitoring point according to the user-defined pollution range size and an upstream river calculation algorithm, and obtaining pollution enterprise data of the upstream river in the user-defined pollution range according to the pollution enterprise data in the range and the space-time database.
In some embodiments, the contamination range size is customized according to the user, including but not limited to:
the value of the abnormal data of the monitoring point is inversely proportional to the pollution range defined by the user; in particular, the method comprises the steps of,
if the abnormal condition of the monitoring point data is stronger, the user-defined pollution range is smaller; if the abnormal condition of the monitoring point data is weaker, the user-defined pollution range is larger;
further, the user may also obtain the contamination range according to other rules defined by the user.
In some embodiments, the upstream river calculation algorithm obtains the grid in the river where the monitoring point is located through the position of the monitoring point, and then judges through the distance from the grid to other grids of the monitoring point, wherein the grid with the minimum distance is the grid of the river upstream of the monitoring point; wherein,,
the calculation method of the distance from the grid to other grids of the monitoring point adopts GIS distance calculation, and the GIS distance calculation formula is as follows:
lat1 is the latitude of the first grid center point, lat2 is the latitude of the second grid center point, lon1 is the longitude of the first grid center point, lon2 is the longitude of the second grid center point, and earth_real is the EARTH radius.
In some embodiments, according to the pollution enterprise data in the range and the space-time database, obtaining the pollution enterprise data of the upstream river in the user-defined pollution range specifically includes:
and calling a space-time database of the river to be detected to obtain polluted enterprise data of the river, and then reducing the polluted enterprise range by combining the Beidou grid range of the river upstream of the monitoring point to obtain polluted enterprise data of the river upstream of the monitoring point in the user-defined polluted range.
In some embodiments, for the case of a change in contaminated enterprise data of a river upstream of the same monitoring point, the contamination indexes of contaminated enterprises at different levels of each monitoring point are cached; in particular, the method comprises the steps of,
when the user-defined pollution range varies, the pollution enterprise data of the river at the upstream of the same monitoring point in the pollution range under consideration also changes, for example, at a certain moment, the abnormal condition of the data of the monitoring point is weaker, the user-defined pollution range is larger, the abnormal condition of the data of the monitoring point is enhanced at the next moment, the user-defined pollution range is smaller, so that the pollution enterprise data of the river at the upstream of the same monitoring point in the pollution range under consideration changes, and therefore, each monitoring point is cached according to the pollution indexes of pollution enterprises at different levels corresponding to the pollution range, so that the pollution enterprises can be directly called when being used next time, and the tracing time can be saved; wherein,,
the contaminated enterprise data includes the name, number, location, etc. of the contaminated enterprise.
S130, if the pollution enterprise is one, using the pollution enterprise as a pollution source of the monitoring point; and if the number of the polluted enterprises is multiple, acquiring data of other river water quality monitoring points in the river upstream of the monitoring point at the same time.
S140, judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained.
In some embodiments, at the same time, the data of other monitoring points in the river upstream of the monitoring point are abnormal, for example, the data of the A number monitoring point, the B number monitoring point, the C number monitoring point and the D number monitoring point are all abnormal, then the above steps are repeated, each polluted enterprise and the corresponding multiple associated monitoring point data thereof are respectively obtained, for example, the polluted enterprise existing in the user-defined pollution range of the A number monitoring point and the B number monitoring point is an M enterprise, the polluted enterprise existing in the user-defined pollution range of the M enterprise and the corresponding A number monitoring point and the B number monitoring point is an N enterprise, and the polluted enterprise existing in the user-defined pollution range of the C number monitoring point and the D number monitoring point is an N enterprise.
And S150, sorting the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points, and displaying sorting results so as to position the pollution enterprises.
In some embodiments, ranking the possibility that the pollution enterprises existing at the same time become the pollution sources of the monitoring points and displaying the ranking result comprises:
according to a pollution weight calculation algorithm, each pollution enterprise is associated with a plurality of monitoring points corresponding to the pollution enterprise;
traversing each pollution enterprise, accumulating calculation results of each index weight of the associated monitoring point corresponding to each pollution enterprise, and taking the results as pollution indexes of each pollution enterprise;
ranking the possibility that each pollution enterprise becomes a pollution source of the monitoring point according to the pollution index, wherein the greater the pollution index is, the earlier the ranking is;
displaying the sequencing results of all polluted enterprises existing at the same time; wherein,,
for the situation that a plurality of monitoring points possibly detect a plurality of pollution enterprises at a certain moment, combining a plurality of pollution factors such as industrial wastewater, domestic sewage, farmland drainage and the like, adopting a pollution weight calculation algorithm to correlate each pollution enterprise with a plurality of corresponding monitoring points, traversing each pollution enterprise, and calculating the pollution index of each pollution enterprise; in particular, the method comprises the steps of,
the weight calculation formula of the pollution weight calculation algorithm is as follows:
α＝|v-s|*w
alpha represents the weight calculation result of a certain detection index, v represents the current detection value of the index, s represents the standard value of the index, and w represents the weight value of the index.
In some embodiments, monitoring points with abnormal data and their corresponding contaminated enterprises are alerted and recorded in real-time.
The foregoing is a description of embodiments of the method, and further description of the aspects of the present disclosure follows by way of specific embodiments employing the method.
Taking river data, pollution enterprise data and river water quality monitoring point data in the Jinan city as supporting data, tracing the pollution condition of a certain part of a hike river to obtain water quality monitoring point data of the certain part of the hike river, namely first water quality monitoring point data;
acquiring grids of the first water quality monitoring point in the bare river, namely a first grid, acquiring grids of other positions in the bare river except the first water quality monitoring point in the bare river, namely a second grid, a third grid, a fourth grid and the like by using the same method, respectively calculating the distances from the first grid to the grids of the second grid, the third grid, the fourth grid and the like by using a GIS distance calculation method, wherein the grid with the minimum distance is the grid of the river upstream of the first water quality monitoring point, for example, the grid with the minimum distance from the first grid to the second grid is the grid of the river upstream of the first water quality monitoring point, and the river range covered by the second grid is the river upstream of the first water quality monitoring point; invoking a space-time database of a bare and hacked river to obtain polluted enterprise data of the bare and hacked river, and then reducing the polluted enterprise range by combining the Beidou grid range of the river at the upstream of the first water quality monitoring point to obtain polluted enterprise data of the river at the upstream of the first water quality monitoring point in a user-defined polluted range;
two pollution enterprises of the upstream river of the first water quality monitoring point of the bare and hacked river in the user-defined pollution range are respectively a Henan unified enterprise limited company and a ren healthcare medical science limited company, and other monitoring point data in the upstream river of the first water quality monitoring point of the bare and hacked river at the same time are acquired for determining the pollution enterprises;
the data of other monitoring points in the river upstream of the first water quality monitoring point at the same time are abnormal, for example, the data of a second monitoring point, a third monitoring point, a fourth monitoring point and a fifth monitoring point are abnormal, the steps are repeated, the two pollution enterprises and a plurality of corresponding related monitoring point data are respectively obtained, for example, the pollution enterprises existing in the second monitoring point and the third monitoring point in the user-defined pollution range are Henan unified enterprise limited companies, the Henan unified enterprise limited companies and the corresponding second monitoring point and third monitoring point data are obtained, the pollution enterprises existing in the fourth monitoring point and the fifth monitoring point in the user-defined pollution range are Henan healthcare limited companies, and the Henan healthcare limited companies and the corresponding fourth monitoring point and the fifth monitoring point data are obtained;
the pollution indexes of the two enterprises are calculated by using the method, and the pollution indexes of the Henan unified enterprise limited company are found to be larger, so that the enterprise is a pollution source of the first water quality monitoring point;
simultaneously, real-time alarming and recording are carried out on monitoring points with abnormal data and corresponding pollution enterprises;
by testing 50 different time points of 50 monitoring points of a bare hacker, the following conclusion is obtained: the probability of correctly solving the polluted enterprises is 99%, and the incorrect solving of the polluted enterprises is caused by the deviation of the data of the individual water quality monitoring points.
According to the embodiment of the disclosure, whether the monitoring point data is abnormal is judged by acquiring the river water quality monitoring point data to be detected, and if so, the pollution enterprise data of the river upstream of the monitoring point in the user-defined pollution range is acquired; judging pollution sources of the monitoring points according to the number of pollution enterprises, if the pollution enterprises are one, taking the pollution enterprises as the pollution sources of the monitoring points, if the pollution enterprises are a plurality of, acquiring data of other river water quality monitoring points in the river at the upstream of the monitoring points at the same time, and judging whether the data of the other monitoring points are abnormal; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained; the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points is sequenced and the sequencing result is displayed, so that the pollution enterprises can be accurately positioned, and abnormal data of the monitoring points and the pollution enterprises are warned and recorded in real time.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.
Fig. 2 shows a block diagram of a river pollution tracing device provided by an embodiment of the present disclosure. The apparatus 200 comprises:
and the calling module 210 is used for calling the space-time database and acquiring river water quality monitoring point data to be detected.
A judging module 220, configured to judge whether the monitoring point data is abnormal; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range;
if the pollution enterprise is one, the pollution enterprise is used as a pollution source of the monitoring point; if the number of the polluted enterprises is multiple, acquiring water quality monitoring point data of other rivers in the upstream rivers of the monitoring points at the same time;
judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained.
The ranking module 230 is configured to rank the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points, and display the ranking result, so as to locate the pollution enterprises.
In some embodiments, the calling module 210 is specifically configured to:
coding river data, pollution enterprise data and river water quality monitoring point data through Beidou grid codes, and storing the river data, the pollution enterprise data and the river water quality monitoring point data into a space-time database; wherein,,
the river water quality monitoring point data comprises the position and detection data of the river water quality monitoring point.
In some embodiments, the determining module 220 is specifically configured to:
according to the user-defined pollution range size and an upstream river calculation algorithm, a Beidou grid range of the upstream river of the monitoring point is obtained, and according to the pollution enterprise data in the range and the space-time database, pollution enterprise data of the upstream river in the user-defined pollution range is obtained; wherein,,
depending on the user-defined contamination range size, including but not limited to:
the value of the abnormal data of the monitoring point is inversely proportional to the pollution range defined by the user;
further, under the condition that the data of the polluted enterprises of the river at the upstream of the same monitoring point are changed, the pollution indexes of the polluted enterprises at different levels of each monitoring point are cached; in particular, the method comprises the steps of,
when the user-defined pollution range varies in size, the pollution enterprise data of the river at the upstream of the same monitoring point in the considered pollution range can be changed along with the pollution enterprise data, and the pollution indexes of pollution enterprises at different levels of each monitoring point are cached so as to be directly called when being used next time; wherein,,
the contaminated enterprise data includes the name, number, location, etc. of the contaminated enterprise.
In some embodiments, the ranking module 230 is specifically configured to:
according to a pollution weight calculation algorithm, each pollution enterprise is associated with a plurality of monitoring points corresponding to the pollution enterprise;
traversing each pollution enterprise, accumulating calculation results of each index weight of the associated monitoring point corresponding to each pollution enterprise, and taking the results as pollution indexes of each pollution enterprise;
ranking the possibility that each pollution enterprise becomes a pollution source of the monitoring point according to the pollution index, wherein the greater the pollution index is, the earlier the ranking is;
and displaying the sequencing results of all polluted enterprises existing at the same time.
It can be appreciated that each module/unit in the detection apparatus 200 shown in fig. 2 has a function of implementing each step in the detection method 100 provided in the embodiment of the disclosure, and can achieve the corresponding technical effects, which are not described herein for brevity.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
Fig. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure. Electronic device 300 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic device 300 may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 3, the electronic device 300 includes a computing unit 301 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 302 or a computer program loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic device 300 may also be stored. The computing unit 301, the ROM302, and the RAM303 are connected to each other by a bus 304. I/O interface 305 is also connected to bus 304.
Various components in the electronic device 300 are connected to the I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 300 via the ROM302 and/or the communication unit 309. One or more of the steps of the method 100 described above may be performed when the computer program is loaded into RAM303 and executed by the computing unit 301. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the present disclosure further provides a non-transitory computer readable storage medium storing computer instructions, where the computer instructions are configured to cause a computer to perform the method 100 and achieve corresponding technical effects achieved by performing the method according to the embodiments of the present disclosure, which are not described herein for brevity.
In addition, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method 100.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: display means for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. The river pollution tracing method is characterized by comprising the following steps of:
invoking a space-time database to acquire river water quality monitoring point data to be detected;
judging whether the monitoring point data is abnormal or not; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range;
if the pollution enterprise is one, the pollution enterprise is used as a pollution source of the monitoring point; if the number of the polluted enterprises is multiple, acquiring water quality monitoring point data of other rivers in the upstream rivers of the monitoring points at the same time;
judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained;
and sequencing the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points and displaying sequencing results so as to position the pollution enterprises.
2. The method of claim 1, wherein the spatio-temporal database is generated by:
coding river data, pollution enterprise data and river water quality monitoring point data through Beidou grid codes, and storing the river data, the pollution enterprise data and the river water quality monitoring point data into a space-time database; wherein,,
the river water quality monitoring point data comprise the position and detection data of the river water quality monitoring point.
3. The method of claim 1, wherein the obtaining pollution enterprise data for the river upstream of the monitoring point within the user-defined pollution range comprises:
and obtaining the Beidou grid range of the river upstream of the monitoring point according to the user-defined pollution range size and an upstream river calculation algorithm, and obtaining pollution enterprise data of the upstream river in the user-defined pollution range according to the pollution enterprise data in the range and the space-time database.
4. A method according to claim 3, wherein the pollution scope size is customized according to the user, including but not limited to:
the value of the monitoring point anomaly data is inversely proportional to the user-defined pollution range.
5. The method of claim 1, wherein ranking the likelihood of the contaminated business existing at the same time being a source of contamination for the monitoring point and displaying the ranking result comprises:
according to a pollution weight calculation algorithm, each pollution enterprise is associated with a plurality of monitoring points corresponding to the pollution enterprise;
traversing each pollution enterprise, accumulating calculation results of each index weight of the associated monitoring point corresponding to each pollution enterprise, and taking the results as pollution indexes of each pollution enterprise;
ranking the possibility that each pollution enterprise becomes a pollution source of the monitoring point according to the pollution index, wherein the greater the pollution index is, the earlier the ranking is;
and displaying the sequencing results of all polluted enterprises existing at the same time.
6. The method according to claim 4, wherein the method further comprises:
and for the situation that the data of the polluted enterprises of the river at the upstream of the same monitoring point are changed, the pollution indexes of the polluted enterprises at different levels of each monitoring point are cached.
7. The method of claim 6, wherein the caching the pollution index of the polluted enterprises of different levels for each monitoring point for the situation that the polluted enterprise data of the river upstream of the same monitoring point is changed, comprises:
when the user-defined pollution range varies in size, the pollution enterprise data of the river at the upstream of the same monitoring point in the considered pollution range can be changed along with the pollution enterprise data, and the pollution indexes of pollution enterprises at different levels of each monitoring point are cached so as to be directly called when being used next time; wherein,,
the contaminated enterprise data includes, but is not limited to, the name, number, location of the contaminated enterprise.
8. A river pollution tracing device, comprising:
the calling module is used for calling the space-time database and acquiring river water quality monitoring point data to be detected;
the judging module is used for judging whether the monitoring point data is abnormal or not; if yes, acquiring pollution enterprise data of the river upstream of the monitoring point in a user-defined pollution range;
if the pollution enterprise is one, the pollution enterprise is used as a pollution source of the monitoring point; if the number of the polluted enterprises is multiple, acquiring water quality monitoring point data of other rivers in the upstream rivers of the monitoring points at the same time;
judging whether the data of the other monitoring points are abnormal or not; repeating the steps until each polluted enterprise and a plurality of corresponding associated monitoring points are obtained;
and the sequencing module is used for sequencing the possibility that the pollution enterprises existing at the same time become pollution sources of the monitoring points and displaying sequencing results so as to position the pollution enterprises.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
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| CN202310537841.5A CN116681562A (en) | 2023-05-12 | 2023-05-12 | River pollution tracing method and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117575161A (en) * | 2023-11-30 | 2024-02-20 | 生态环境部土壤与农业农村生态环境监管技术中心 | Industrial pollution source monitoring and point distribution method and device |
| CN119378827A (en) * | 2024-12-28 | 2025-01-28 | 深圳市洪桦环保科技有限公司 | A method and system for tracing pollutants from industrial sewage plants |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117575161A (en) * | 2023-11-30 | 2024-02-20 | 生态环境部土壤与农业农村生态环境监管技术中心 | Industrial pollution source monitoring and point distribution method and device |
| CN117575161B (en) * | 2023-11-30 | 2024-06-11 | 生态环境部土壤与农业农村生态环境监管技术中心 | Industrial pollution source monitoring point layout method and device |
| CN119378827A (en) * | 2024-12-28 | 2025-01-28 | 深圳市洪桦环保科技有限公司 | A method and system for tracing pollutants from industrial sewage plants |
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