System and method for indicating river pollution conditions through colors

Abstract

The invention relates to the technical field of environmental monitoring, and particularly discloses a system and a method for indicating a river pollution condition through colors. The system comprises an indication terminal deployed in a river channel and a remote monitoring center. The indication terminal synchronously obtains optical signals of a water body in a plurality of characteristic wave bands through the multispectral pollution response unit, analyzes the signals through the pollution type judgment module to distinguish main pollution types and evaluate the pollution degree, and drives the multicolor light source array through the composite color synthesis unit according to a preset mapping rule. And generating composite indicating light with specific hue and saturation corresponding to the pollution type and degree for visual display. And the remote monitoring center receives the data for storage and analysis. The system can visually and clearly distinguish and indicate different river pollutant types and degrees, and pollution information transmission efficiency and monitoring management pertinence are improved.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology, specifically relating to a system and method for indicating the pollution status of rivers through color. Background Technology

[0002] In the field of water environment monitoring and pollution control, real-time and accurate identification and assessment of river pollution status is a key link in ensuring water resource security and implementing effective governance.

[0003] Pollution indication technology utilizing optical principles has become an important development direction in this field due to its advantages such as rapid response and ease of remote observation. This type of technology typically relies on the absorption or reflection characteristics of pollutants on light of specific wavelengths, using sensors to collect optical signals and convert them into identifiable indication information.

[0004] Color-changing pollution indication systems aim to present complex pollution information intuitively to observers. Existing technologies mostly use a single-color light source, such as a monochrome LED, whose brightness or flashing frequency changes with the concentration of pollutants detected by the sensor.

[0005] This method of indication has significant limitations: when river water is affected by multiple pollutants, different pollutants may produce similar or overlapping responses to the same wavelength of light, making it impossible for the system to effectively distinguish the specific type of pollutant. For example, oil spills and certain heavy metal ions may cause the indicator light source to exhibit similar intensity changes.

[0006] The current single-color indication method is prone to causing confusion in complex real-world pollution scenarios. Observers can only know that the level of pollution may have changed, but cannot determine whether the pollution is caused by organic pollutants, inorganic heavy metals, or other substances.

[0007] This directly leads to a lack of targeted pollution control measures, potentially resulting in the mismixing of treatment agents or the use of inappropriate physical cleaning methods. This not only reduces treatment efficiency but may also cause secondary pollution or waste of resources due to improper measures. Therefore, how to provide a color-coded indicator scheme that can intuitively distinguish different types of pollution has become an urgent technical challenge. Summary of the Invention

[0008] The purpose of this invention is to provide a system and method for indicating the pollution status of a river by color, so as to solve the problem that the existing single-color indication technology cannot distinguish multiple types of pollutants, resulting in confusion of pollution information and lack of targeted subsequent treatment measures.

[0009] The technical solution of this invention is a system for indicating the pollution status of a river through color. This system includes indicator terminals deployed at river monitoring points and a remote monitoring center. The core of the indicator terminal consists of a multispectral pollution response unit and a composite color synthesis unit. The multispectral pollution response unit is used to simultaneously acquire optical response signals of the water body in at least three different characteristic bands, each of which exhibits selective sensitivity to different types of pollutants.

[0010] The composite color synthesis unit is used to synthesize at least three acquired optical response signals into a composite indicator light signal with specific hue and saturation according to a preset pollution type-color mapping rule, and then presents it intuitively through a multi-color light source array. The remote monitoring center is used to receive the raw optical response signal data and the synthesized color coding information from the indicator terminal, and to perform data storage, in-depth analysis, and historical trend comparison.

[0011] Furthermore, the multispectral pollution response unit includes a broadband light source emission module, a multi-channel optical sensor array, and a signal preprocessing module. The broadband light source emission module projects a light beam covering the visible light and part of the near-infrared band onto the water body to be tested. The multi-channel optical sensor array consists of at least three independent photodetectors, each with a narrowband optical filter integrated in front of it. The center wavelengths of these at least three narrowband optical filters correspond to at least three characteristic bands. The signal preprocessing module amplifies, performs analog-to-digital conversion, and performs background noise reduction on the raw electrical signal output from the multi-channel optical sensor array to generate a standardized optical response signal.

[0012] Furthermore, the selection of at least three characteristic bands is based on the following criteria: the first characteristic band, with a center wavelength in the range of 420 nm to 450 nm, is highly sensitive to changes in the concentration of dissolved organic matter, especially humic acids, in water; the second characteristic band, with a center wavelength in the range of 550 nm to 580 nm, is highly sensitive to water turbidity and suspended particulate matter concentration; and the third characteristic band, with a center wavelength in the range of 650 nm to 680 nm, is highly sensitive to the characteristic absorption of heavy metal ions with specific valence states, such as ferric ions or hexavalent chromium ions. By simultaneously monitoring the relative changes in the optical response signals of these three bands, basic data for distinguishing pollution types can be provided.

[0013] Furthermore, the composite color synthesis unit includes a pollution type discrimination module, a color mapping engine, and a multi-color light source driving module. The pollution type discrimination module has a built-in classification algorithm based on multi-dimensional vector space analysis. The execution process of this algorithm is as follows: at least three pre-processed optical response signals are constructed into multi-dimensional feature vectors; the Euclidean distance or cosine similarity between the feature vector and multiple standard pollution type feature vector templates pre-stored in the database is calculated; the standard pollution type with the smallest distance or the highest similarity to the current feature vector is determined as the main pollution type of the current water body.

[0014] The color mapping engine stores a pollution type-color mapping rule database. This database predetermines a corresponding base hue for each standard pollution type and a corresponding saturation level for the pollution severity. The multi-color light source driver module receives hue and saturation control commands from the color mapping engine, driving a multi-color light source array composed of red, green, and blue primary color LEDs to mix and produce the target composite indicator light.

[0015] Furthermore, the specific settings of the pollution type-color mapping rule are as follows: when the pollution is determined to be mainly caused by dissolved organic matter, the base hue of the composite indicator light is set to orange; when the pollution is determined to be mainly caused by suspended particulate matter and has high turbidity, the base hue is set to yellow; when the pollution is determined to be mainly caused by specific heavy metal ions, the base hue is set to purple; when the pollution is determined to be caused by a mixture of multiple pollutants, the luminous intensity of the three primary colors is weighted according to the contribution weight of each pollution type, and a secondary color between the above base hues is generated, such as orange-yellow or purplish-red. Simultaneously, the quantified value of the pollution degree is linearly or non-linearly mapped to the saturation of the composite indicator light; the higher the pollution degree, the higher the saturation, and the more vivid and intense the color.

[0016] Furthermore, the indicator terminal also includes an ambient light adaptive adjustment module. This module includes an ambient light sensor for real-time monitoring of the ambient light intensity around the indicator terminal. The logic control process of the ambient light adaptive adjustment module is as follows: the ambient light intensity is divided into multiple levels; a corresponding basic luminous intensity of the multi-color light source array is preset for each level; when the drive module outputs the hue and saturation determined by the color mapping engine, this basic luminous intensity is superimposed to ensure that the composite indicator light has a clear and distinguishable visual effect under different ambient light conditions.

[0017] Furthermore, the system's operation method includes the following steps: Step S1: System initialization, power-on self-test of broadband light source emission module and multispectral pollution response unit, loading pollution type feature vector template database and color mapping rule database; In step S2, the broadband light source emission module projects a light beam onto the water body at a fixed period, and the multi-channel optical sensor array synchronously collects optical signals from at least three characteristic bands. Step S3: The signal preprocessing module processes the acquired signal to obtain a standardized optical response signal; Step S4: The pollution type discrimination module constructs a feature vector based on the standardized optical response signal, identifies the current main pollution type through a vector space analysis algorithm, and calculates the pollution degree index. Step S5: Based on the identified pollution type and pollution level index, the color mapping engine queries the mapping rules and generates color control instructions containing the target hue and saturation. Step S6: The ambient light adaptive adjustment module detects the ambient light intensity and determines the basic luminous intensity compensation value; Step S7: The multi-color light source driving module combines color control instructions and basic luminous intensity compensation values ​​to drive the three-primary-color light-emitting diode array to emit composite indicator light with specific hues and saturation. Step S8: Instruct the terminal to package the original optical response signal, the identified pollution type, the pollution level index, and the synthesized color coding information, and upload them to the remote monitoring center via the wireless communication module. In step S9, the remote monitoring center receives the data, updates the database, and can simultaneously display pollution information and terminal indicator colors in a digital and graphical manner on the monitoring interface.

[0018] Furthermore, the remote monitoring center also has pollution event tracing and early warning functions. This function is implemented through the following process: continuously receiving and storing time-series data from each monitoring point; when the pollution type of a monitoring point changes abruptly or the pollution level index continuously exceeds the threshold, an early warning is automatically triggered, and the monitoring point and its current indicator color are highlighted on the electronic map; simultaneously, historical data from upstream adjacent stations of the monitoring point are retrieved for correlation analysis to assist in locating the possible source of pollution.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention employs a multispectral pollution response unit to simultaneously acquire optical signals from at least three characteristic bands, constructing a multidimensional information space capable of reflecting the characteristics of different pollutants. Utilizing a pollution type discrimination algorithm based on vector space analysis, the system can analyze and distinguish major types of pollution, such as dissolved organic matter pollution, suspended particulate matter pollution, and specific heavy metal pollution, from complex optical responses. This fundamentally overcomes the inherent limitation of single-color indication technology in distinguishing pollutants, providing observers with clear and specific pollution type information.

[0020] 2. This invention transforms abstract pollution type and severity data into intuitive composite color light with specific hues and saturations through a composite color synthesis unit and pollution type-color mapping rules. Different pollution types correspond to different base hues, while pollution severity is intuitively represented by saturation. This "one color per type, severity indicated by intensity" approach allows on-site personnel or remote monitors to quickly and accurately grasp the nature and severity of pollution simply by looking at the color, without needing to interpret complex data, greatly improving the efficiency and intuitiveness of information transmission.

[0021] 3. This invention, by introducing an ambient light adaptive adjustment module, ensures the reliability and visibility of the color indication effect under various lighting conditions. The system can automatically adjust the overall brightness of the indicator light according to the ambient light intensity, avoiding the problems of unclear indication under strong light or excessive glare under dim light, thus ensuring the practicality and stability of the indicator system under all-weather conditions.

[0022] 4. This invention constructs a complete system consisting of on-site instruction terminals and a remote monitoring center. The on-site terminals are responsible for real-time sensing, judgment, and intuitive instruction, while the remote center is responsible for data aggregation, in-depth analysis, and historical tracing. This architecture not only meets the needs of rapid on-site response but also supports advanced management functions such as macro-monitoring, trend analysis, and pollution source tracing, achieving the integration of sensing, instruction, and decision support, and significantly improving the overall efficiency and intelligence level of river pollution monitoring and emergency management. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall technical solution architecture of the system for indicating the pollution status of rivers using color, as proposed in this invention. Figure 2 This is a schematic diagram of the core framework of the multispectral pollution response and composite color synthesis principle in this invention; Figure 3 This is a flowchart illustrating the logical process of pollution type identification and color mapping in this invention. Figure 4 This is a schematic diagram of the multi-level interaction and data flow between the instruction terminal and the remote monitoring center in this invention; Figure 5 This is a schematic diagram illustrating the working principle of the ambient light adaptive adjustment module in this invention. Detailed Implementation

[0024] Example 1: This example details a specific implementation of a system that uses color to indicate the pollution status of a river. Please refer to the appendix. Figure 1 To be continued Figure 5The system's overall architecture includes indicator terminals deployed at river monitoring points and a remote monitoring center located in the management department. The indicator terminals are the core of the system for on-site sensing and intuitive indication. Their physical structure is typically encapsulated within a waterproof and impact-resistant shell, and mounted on the riverbank or buoy platform using fixed brackets to ensure stable optical coupling between the detection window of their multispectral pollution response unit and the water body. The remote monitoring center consists of a server cluster, data storage array, network communication equipment, and monitoring workstations. It establishes continuous data connections with the indicator terminals distributed throughout the river via a wireless communication network, forming a complete network of centralized monitoring and distributed sensing.

[0025] The core functions of the indicator terminal are achieved collaboratively by a multispectral pollution response unit and a composite color synthesis unit. Please refer to the attached document. Figure 2 The multispectral pollution response unit is responsible for extracting optical information from the water body that can characterize the features of different pollutants. This unit specifically includes a broadband light source emission module, a multi-channel optical sensor array, and a signal preprocessing module.

[0026] The broadband light source emission module consists of a high-brightness light-emitting diode array and its driving circuit. The spectral output range of this light-emitting diode array covers a broad spectrum from 400 nanometers to 850 nanometers, including visible light and part of the near-infrared band. The driving circuit receives periodic trigger commands from the main control microprocessor and projects a parallel beam of light shaped by a collimating lens onto the water body under test at a fixed frequency, for example, once every 30 seconds. The beam enters the water surface at a certain incident angle; part of the light is absorbed and scattered by the water, while the backscattered light carries water composition information back.

[0027] A multi-channel optical sensor array is used to receive backscattered light returning from a body of water and decompose it into optical signals of at least three characteristic wavelengths. The array consists of at least three independent photodetectors, each with a narrowband optical filter precisely integrated in front of it. The center wavelengths of these at least three narrowband optical filters are rigorously selected to correspond to characteristic wavelengths sensitive to specific pollutants.

[0028] In this embodiment, three characteristic wavelength bands are employed. The first characteristic wavelength band has a center wavelength of 435 nm, falling within the 420 nm to 450 nm range. This band is highly sensitive to the UV-Vis absorption characteristics of dissolved organic matter in water, particularly humic acids, and the attenuation of its optical response signal is strongly correlated with the concentration of dissolved organic matter. The second characteristic wavelength band has a center wavelength of 565 nm, falling within the 550 nm to 580 nm range. This band is most sensitive to changes in water turbidity caused by suspended sediment, algae, and other particulate matter, and the intensity of its optical response signal is directly related to the particulate matter scattering coefficient. The third characteristic wavelength band has a center wavelength of 665 nm, falling within the 650 nm to 680 nm range. This band targets the characteristic charge transfer absorption band of heavy metal ions with specific valence states, such as ferric or hexavalent chromium ions, in the visible light region. Changes in its optical response signal can effectively indicate the presence and concentration trend of such heavy metal pollution. Each photodetector converts the received specific wavelength light signal into a weak analog current signal.

[0029] The signal preprocessing module is responsible for converting the raw analog electrical signals output from the multi-channel optical sensor array into standardized digital signals that can be processed by subsequent algorithms. This module includes a preamplifier circuit, an analog-to-digital converter, and a digital signal processor. The preamplifier circuit amplifies the current signal of each channel across impedance, converting it into a voltage signal. The amplification factor can be pre-configured according to the expected light transmittance of the water body.

[0030] The analog-to-digital converter synchronously samples the amplified multi-channel voltage signals with 16-bit precision. The digital signal processor executes a noise floor subtraction algorithm. This algorithm records the dark current noise value of each channel with the broadband light source off during each system initialization or periodic calibration. During each formal measurement, it subtracts the corresponding dark current noise value from the sampled value to obtain the net optical response voltage value. Subsequently, the processor normalizes the net voltage value into a standardized optical response signal ranging from 0 to 1, denoted as R435, R565, and R665, according to preset calibration coefficients. These standardized signals characterize the relative transmission or backscattering intensity of the water body in each characteristic wavelength band.

[0031] The core task of the composite color synthesis unit is to transform the abstract optical signals acquired by the multispectral pollution response unit into intuitive composite color light that contains information about the type and degree of pollution. Please refer to the attached diagram. Figure 2 With appendix Figure 3 This unit consists of a contamination type discrimination module, a color mapping engine, and a multi-color light source driving module. The contamination type discrimination module runs in the embedded microprocessor of the indicator terminal, and its core is a classification algorithm based on multi-dimensional vector space analysis.

[0032] This algorithm requires a pre-trained and stored database of standard pollution type feature vector templates as a discrimination benchmark. The database is built through laboratory simulations or historical field data accumulation and contains standard feature vectors under various typical pollution conditions. For example, the template vector for "clean water" is... The template vector for "highly soluble organic matter pollution" is The template vector for "high turbidity pollution" is The template vector for "heavy metal pollution" is wait.

[0033] The execution flow of the pollution type discrimination module is as follows. First, the three standardized optical response signals R435, R565, and R665 acquired in the current cycle are constructed into a three-dimensional feature vector. Then, the Euclidean distance Di between the feature vector V and the feature vector template Ti for each standard pollution type in the database is calculated. The formula for calculating the Euclidean distance is: ; This formula quantifies the degree of difference between the current optical characteristics of the water body and various standard pollution types. The algorithm traverses all templates in the database to find the standard pollution type that minimizes the distance Di, and identifies it as the main pollution type of the current water body. For example, if V has the smallest Euclidean distance to the "high turbidity pollution" template Ts, then the current main pollution type is determined to be high turbidity pollution caused by suspended particulate matter.

[0034] Meanwhile, the pollution level index is calculated based on the distance between the current feature vector and the determined pollution type template vector, or by comprehensively determining the relative rate of change obtained by comparing the current signal with the clean water body template signal. For example, the pollution level index P can be defined as the normalized value of the distance between the current vector and the clean water body template vector; the larger the P value, the more severe the pollution.

[0035] The color mapping engine stores a database of pollution types and color mapping rules. These rules predetermine a base hue for each standard pollution type and a corresponding saturation level for different pollution intensity ranges.

[0036] The specific mapping rules are set as follows: When the pollution is determined to be mainly caused by dissolved organic matter, the base hue is set to orange, and its theoretical proportion in the red, green and blue primary color space is the red component intensity. High, green component intensity Medium intensity of blue component Very low. When the pollution is determined to be high turbidity primarily caused by suspended particulate matter, the baseline hue is set to yellow, with a theoretical ratio of [missing information]. and All are high. Very low. When pollution is determined to be primarily caused by specific heavy metal ions, the baseline hue is set as purple, with a theoretical ratio of [missing information]. and All are relatively high. Very low. When the output of the pollution type discrimination module is "mixed pollution" or the algorithm calculates that the contributions of the two main pollution types are similar, the color mapping engine performs a weighted calculation of the three primary color intensities of the corresponding base hue based on the weight of the contribution of each pollution type.

[0037] For example, if the weight of dissolved organic matter pollution is 0.6 and the weight of heavy metal pollution is 0.4, then the intensity of the three primary colors of the final target hue is: ; ; ; in, This indicates the intensity of the red component in the final color. The red component intensity of orange corresponds to the base hue of dissolved organic matter pollution. This indicates the intensity of the red component of purple, the base hue corresponding to heavy metal pollution. This indicates the intensity of the green component in the final color. This indicates the intensity of the green component of the reference hue orange, representing dissolved organic matter pollution. This indicates the intensity of the green component of the base hue purple, corresponding to heavy metal pollution. This indicates the intensity of the blue component in the final color. This indicates the intensity of the blue component of the reference hue orange, representing dissolved organic matter pollution. This indicates the intensity of the blue component of the base hue of purple, corresponding to heavy metal pollution. This allows for the mixing of intermediate colors between orange and purple, such as orange-red or magenta.

[0038] After determining the intensity ratio of the three primary colors corresponding to the base hue, the color mapping engine further modulates the color saturation based on the calculated pollution level index P. The saturation level is divided into eight discrete levels, each corresponding to one of the eight equal intervals of the pollution level index P. The pollution level index P ranges from 0 to 1, where 0 represents clean and 1 represents severely polluted. Based on the interval in which the P value falls, the color mapping engine looks up the saturation modulation coefficient S in a table, with the S value increasing linearly from 0.3 to 1.0.

[0039] The final output color control command contains two sets of parameters: one set is the target intensity ratio coefficient of the three primary colors after saturation modulation. These are determined by the base hue ratio and saturation coefficient S, ensuring that color vibrancy increases as pollution worsens; the other set is pollution type identifiers.

[0040] The multi-color light source driver module receives color control commands from the color mapping engine. This module drives a multi-color light source array composed of red, green, and blue LEDs arranged in a specific geometric pattern. The driver circuit employs pulse width modulation (PWM) technology, adjusting the color according to the three primary color ratio coefficients specified in the commands. The duty cycle of the current flowing through the red, green, and blue LEDs is adjusted separately to precisely control the luminous intensity of each color of light. The three primary colors of light are mixed in space and emitted through a milky white or frosted diffuser, ultimately presenting a composite indicator light with a specific hue and saturation. For example, a highly saturated yellow light indicates that the water body is currently suffering from severe suspended particulate matter pollution.

[0041] To ensure the composite indicator light remains clearly visible under varying ambient lighting conditions, the indicator terminal integrates an ambient light adaptive adjustment module. Please refer to the attached document. Figure 5 This module includes an ambient light sensor, typically employing a photodiode or integrated ambient light sensor chip, to monitor and indicate the ambient light intensity (L, measured in lux) around the terminal's installation location in real time. The ambient light adaptive adjustment module has a pre-stored table mapping ambient light intensity to baseline luminous intensity.

[0042] This table divides ambient light intensity (L) into five levels: low light (L < 10 lux); medium light (L between 10 and 100 lux); medium light (L between 100 and 1000 lux); high light (L between 1000 and 10000 lux); and strong light (L > 10000 lux). Each level has a preset baseline luminous intensity compensation value (B) for the multi-color light source array, in milliamperes. The low light level corresponds to a lower B value to avoid glare, while the strong light level corresponds to a higher B value to ensure the indicator light is visible in sunlight.

[0043] The module's workflow is periodic. The ambient light sensor samples the ambient light intensity L every 5 seconds. The microprocessor looks up the L value in a lookup table to determine the current light level and the corresponding basic luminous intensity compensation value B. This compensation value B, used as the global brightness gain, is sent to the multi-color light source driver module. When calculating the final drive current of each primary color LED according to the color control instructions, the driver module adds the basic luminous intensity compensation value B to the color scaling factor.

[0044] Specifically, the final drive current duty cycle of the red LED The same applies to green and blue. In this way, while maintaining the same hue and saturation, the overall brightness of the composite indicator light can adapt to changes in ambient light, automatically dimming at night and automatically brightening under midday sunlight, always maintaining optimal visual contrast.

[0045] The indicator terminal establishes a data link with the remote monitoring center via a wireless communication module. The wireless communication module can use 4G, 5G, narrowband IoT, or long-range radio communication standards, configured according to the on-site network coverage. After each measurement and indication cycle, the indicator terminal's microcontroller packages the multiple data points generated in that cycle into a standard data frame. The data frame includes a frame header, terminal device identification code, timestamp, original standardized optical response signals R435, R565, and R665, pollution type discrimination result code, calculated pollution level index P, hue and saturation codes output by the color mapping engine, ambient light sensor reading L, basic luminous intensity compensation value B, and a frame check sequence. This data frame is then transmitted to the remote monitoring center via the wireless communication module.

[0046] Please refer to the appendix. Figure 4 The remote monitoring center, serving as the system's data brain and decision support hub, has its software deployed on a server. Its main functional modules include a communication service module, a data parsing and storage module, a real-time monitoring and alarm module, a historical data analysis module, and a human-machine interface. The communication service module continuously listens to the network port, receiving data frames uploaded from various monitoring terminals, performing preliminary protocol parsing and authentication to ensure the data source is legitimate. The data parsing and storage module fully parses the authenticated data frames, extracting each field and associating it with the terminal device identifier and timestamp, storing it in a structured time-series database. Each record contains complete information such as time, terminal location, optical signal, pollution type, pollution index, and color code.

[0047] The real-time monitoring and alarm module is responsible for dynamically monitoring the real-time data entering the database. The human-machine interface is typically centered around an electronic map, which displays icons for all online indicator terminals. The icon colors are synchronized with the colors of the composite indicator lights actually emitted by the on-site terminals, achieving remote visualization. Monitoring personnel can clearly grasp the distribution of pollution status at each monitoring point throughout the entire river basin.

[0048] This module includes multi-level alarm rules. Rule 1 is based on pollution type mutation: The system continuously tracks the pollution type code reported by each terminal. If the type code reported in two adjacent cycles changes, and this change occurs between non-clean and clean periods, a type mutation alarm event is immediately generated. The alarm records the time, terminal location, old type, and new type, and displays the terminal icon flashing on the electronic map. Simultaneously, an SMS or app push notification is sent to relevant management personnel. Rule 2 is based on pollution level index thresholds: A pollution level index threshold is set for each monitoring point or each type of pollution. When the system detects that the contamination level index P of a certain terminal exceeds its threshold for three consecutive cycles... When the continuous exceedance alarm is triggered, the alarm event is logged, and the terminal icon is displayed with a highlighted red border.

[0049] The pollution event tracing and early warning functions of the remote monitoring center are a manifestation of its advanced intelligence. When an alarm is triggered, the system automatically initiates the tracing and analysis process. First, it retrieves all historical data from the monitoring point that triggered the alarm, referred to as the current point, and plots a pollution index trend chart for a period of time prior to the alarm, such as within 24 hours. Second, based on the river hydrological model or preset topological relationships, it automatically locates one or two upstream adjacent monitoring points on the electronic map for the current point.

[0050] The system retrieves historical data from these upstream points within the same time period and performs time-series correlation analysis. The analysis algorithm calculates the time lag correlation between changes in the pollution index at upstream points and changes in the pollution index at the current point. If a specific type of pollution or pollution index peak at an upstream point occurs earlier than the same peak at the current point, and the time difference matches the estimated water flow velocity, the system indicates in the analysis report that this upstream point may be a potential source of the pollution event, providing crucial clues for emergency investigation.

[0051] Furthermore, the historical data analysis module supports multi-dimensional querying and statistical analysis of data from any terminal and any time period. Users can query the frequency of pollution type changes at a specific monitoring point over the past week and generate pie charts; they can compare the pollution level indices of different monitoring points during the same period and generate bar charts; they can also plot the spatial distribution evolution of a pollution index, such as the R565 signal representing turbidity, over the entire watershed. These in-depth analysis functions help managers grasp the long-term evolution patterns of pollution, evaluate the effectiveness of control measures, and provide data support for the total pollution load control of the watershed.

[0052] The entire system operates in a closed loop, from data acquisition to intuitive indication and then to remote analysis. After power-on, the terminal initializes, including self-testing of each sensor, wireless network registration, and synchronization of the latest feature vector templates and color mapping rules from the remote monitoring center. It then enters the main loop. At each fixed interval, such as 30 seconds, the broadband light source emission module is triggered, projecting a beam of light into the water. The multi-channel optical sensor array simultaneously acquires reflected light signals from three characteristic wavelength bands.

[0053] The signal preprocessing module amplifies, performs analog-to-digital conversion, and subtracts background noise, outputting a standardized optical response signal. The contamination type discrimination module immediately constructs feature vectors based on these signals, runs an Euclidean distance calculation algorithm to find the best-matching contamination type from pre-stored templates, and calculates the contamination level index. The color mapping engine then queries mapping rules based on the discerned type and index to generate color control instructions containing the target hue and saturation ratio.

[0054] Meanwhile, the ambient light adaptive adjustment module detects the current ambient light intensity and determines the basic luminous intensity compensation value. The multi-color light source driving module integrates color control commands and brightness compensation values ​​to drive the red, green, and blue primary color light-emitting diode array, mixing and emitting the final composite indicator light. Finally, the terminal packages all data for this cycle and uploads it to the remote monitoring center via the wireless communication module. After receiving the data, the remote monitoring center updates the database and monitoring interface, and runs real-time alarm and historical analysis logic. This cycle repeats continuously, achieving continuous, intelligent, and intuitive monitoring and indication of river pollution status.

[0055] Example 2: This example provides an alternative implementation of a system that indicates the pollution status of a river by color, focusing on expanding the number of characteristic bands in the multispectral pollution response unit and refining the color mapping rules in the composite color synthesis unit to cope with more complex pollution co-occurrence scenarios.

[0056] In the multispectral pollution response unit, the number of characteristic bands has been expanded from 3 to 5. In addition to the three core bands described in Example 1, namely center wavelengths of 435 nm, 565 nm, and 665 nm, a fourth and fifth characteristic band have been added. The center wavelength of the fourth characteristic band is selected as 525 nm, located in the 510 nm to 540 nm range. This band is sensitive to the fluorescence excitation or characteristic absorption of chlorophyll a in water and can be used to indicate organic pollution or eutrophication related to algal proliferation. The center wavelength of the fifth characteristic band is selected as 615 nm, located in the 600 nm to 630 nm range. This band has selective absorption of certain dyes and specific chromogenic substances in chemical wastewater and can be used to assist in identifying color pollution from artificial emissions. The multi-channel optical sensor array has been correspondingly expanded from 3 channels to 5 channels, with each channel equipped with a narrowband optical filter and photodetector corresponding to the center wavelength. The signal preprocessing module processes the 5 signals in parallel and outputs 5 standardized optical response signals, denoted as... .

[0057] The algorithmic basis for the pollution type discrimination module has been upgraded accordingly. The feature vector has been expanded from three dimensions to five dimensions, that is... The standard pollution type feature vector template database also needs to be expanded to include new template vectors such as "algal bloom" and "colored industrial wastewater". The discrimination algorithm still uses a distance-based classification method, but due to the increased dimensionality, Mahalanobis distance can be used instead of Euclidean distance to improve discrimination accuracy and efficiency, taking into account potential correlations between different feature dimensions.

[0058] The calculation of Mahalanobis distance requires pre-estimation of the covariance matrix from a large amount of training data. The discrimination module calculates the Mahalanobis distance between the current five-dimensional feature vector and each template vector, and determines the pollution type based on the principle of minimum distance. Due to the richer features, the system can distinguish more subtle differences in pollution, such as distinguishing between yellow pollution mainly caused by humus and green pollution caused by algae.

[0059] The color mapping engine's rule database has been significantly expanded to match more refined pollution type identification results. In addition to the orange color corresponding to dissolved organic matter, yellow to high turbidity, and purple to heavy metals defined in Example 1, two new baseline hues have been added. For cases identified as "algal pollution," the baseline hue is set to cyan, with its proportion in the red-green-blue color space being equal to the green component intensity. and blue component intensity High, red component intensity Low.

[0060] For cases identified as "colored industrial wastewater", the base color is set as magenta, and its proportion is the red component intensity. and blue component intensity High, green component intensity Low. For mixed pollution where two pollutants coexist, the color mapping engine employs more complex mixing logic. The system not only receives the pollution type discrimination result, but also the confidence score or contribution score of each pollution type output by the discrimination module, which is a five-dimensional vector. , , represent the probability scores of the 5 preset pollution types, with a total score of 1.

Claims

1. A system for indicating the pollution status of a river using color, characterized in that, include: The indicator terminal deployed at river monitoring points is used to sense water pollution information in real time and generate intuitive composite color indicator lights; The indicator terminal includes a multispectral pollution response unit and a composite color synthesis unit; The multispectral pollution response unit is used to simultaneously acquire optical response signals of water bodies in at least three different characteristic bands, and the at least three characteristic bands are selectively sensitive to different types of pollutants. The composite color synthesis unit is used to synthesize at least three optical response signals acquired by the multispectral pollution response unit into a composite indicator light signal with a specific hue and saturation according to a preset pollution type-color mapping rule, and to present it intuitively through a multicolor light source array. The remote monitoring center is used to receive raw optical response signal data and synthesized color coding information from the indicator terminal, and to perform data storage, in-depth analysis and historical trend comparison.

2. The system for indicating river pollution status by color according to claim 1, characterized in that, The multispectral pollution response unit includes a broadband light source emission module, a multi-channel optical sensor array, and a signal preprocessing module. The broadband light source emission module is used to project a light beam covering the visible light and part of the near-infrared band onto the water body to be tested. The multi-channel optical sensor array consists of at least three independent photodetectors, each photodetector having a narrowband optical filter integrated in front of it, and the center wavelengths of the at least three narrowband optical filters corresponding to at least three characteristic bands respectively. The signal preprocessing module is used to amplify, convert analog to digital and deduct background noise from the raw electrical signal output by the multi-channel optical sensor array to generate a standardized optical response signal.

3. A system for indicating river pollution status by color according to claim 2, characterized in that, At least three characteristic bands, including the first characteristic band, the second characteristic band, and the third characteristic band; The center wavelength of the first characteristic band is located in the range of 420 nanometers to 450 nanometers, and it is highly sensitive to changes in the concentration of dissolved organic matter in water. The center wavelength of the second characteristic band is located in the range of 550 nanometers to 580 nanometers, and it is highly sensitive to water turbidity and suspended particulate matter concentration. The center wavelength of the third characteristic band is located in the range of 650 nm to 680 nm, and it is highly sensitive to the characteristic absorption of heavy metal ions in specific valence states.

4. A system for indicating river pollution status by color according to claim 1, characterized in that, The composite color synthesis unit includes a pollution type discrimination module, a color mapping engine, and a multi-color light source driving module; The pollution type discrimination module has a built-in classification algorithm based on multidimensional vector space analysis, which is used to construct a multidimensional feature vector from at least three pre-processed optical response signals, calculate the Euclidean distance or cosine similarity between the feature vector and multiple standard pollution type feature vector templates pre-stored in the database, and determine the standard pollution type with the smallest distance or the highest similarity to the current feature vector as the main pollution type of the current water body. The color mapping engine stores a pollution type-color mapping rule database. The rules preset a corresponding base hue for each standard pollution type and preset a corresponding saturation level for the degree of pollution. The multi-color light source driving module is used to receive hue and saturation control commands from the color mapping engine, drive the multi-color light source array composed of red, green and blue primary color light-emitting diodes, and mix them to produce target composite indicator light.

5. A system for indicating river pollution status by color according to claim 4, characterized in that, The pollution type-color mapping rule is set as follows: when the pollution is determined to be mainly caused by dissolved organic matter, the base hue of the composite indicator light is set to orange; When the pollution is determined to be high turbidity primarily caused by suspended particulate matter, the baseline color is set to yellow. When pollution is determined to be mainly caused by specific heavy metal ions, the baseline color is set to purple. When it is determined to be the effect of multiple pollutants, the luminous intensity of the three primary colors is weighted according to the contribution weight of each pollution type, and the mixture generates an intermediate color between the above-mentioned base colors. Meanwhile, the quantified value of the pollution level is mapped to the saturation of the composite indicator light; the higher the pollution level, the higher the saturation.

6. A system for indicating river pollution status by color according to claim 1, characterized in that, The indicator terminal also includes an ambient light adaptive adjustment module; The ambient light adaptive adjustment module includes an ambient light sensor for real-time monitoring of the ambient light intensity around the terminal. The ambient light adaptive adjustment module is used to divide the ambient light intensity into multiple levels, preset the basic luminous intensity of the multi-color light source array for each level, and superimpose this basic luminous intensity when the multi-color light source driving module outputs the hue and saturation determined by the color mapping engine.

7. A system for indicating river pollution status by color according to claim 1, characterized in that, The remote monitoring center has pollution incident tracing and early warning functions; The pollution incident tracing and early warning function is implemented through the following process: continuously receiving and storing time-series data from each monitoring point; When the pollution type of a monitoring point changes abruptly or the pollution level index exceeds the threshold continuously, an early warning is automatically triggered, and the monitoring point and its current indicator color are highlighted on the electronic map; At the same time, historical data from upstream adjacent stations of the monitoring point were retrieved and correlation analysis was performed.

8. A system for indicating river pollution status by color according to claim 4, characterized in that, The execution process of the classification algorithm based on multidimensional vector space analysis in the pollution type discrimination module is as follows: at least three preprocessed optical response signals are constructed into multidimensional feature vectors; Calculate the Euclidean distance between the feature vector and multiple standard pollution type feature vector templates pre-stored in the database. The Euclidean distance is calculated as the square root of the sum of the squares of the differences between each dimension component of the feature vector and the corresponding template component. The standard pollution type with the smallest Euclidean distance to the current feature vector is determined as the main pollution type of the current water body.

9. A system for indicating river pollution status by color according to claim 6, characterized in that, The ambient light adaptive adjustment module divides the ambient light intensity into 5 levels: dark light, low light, medium light, high light, and strong light. Each level corresponds to a preset overall basic luminous intensity compensation value for the multi-color light source array.

10. A method for indicating the pollution status of a river by color, characterized in that, Environmental monitoring and management can be achieved using a system that uses color to indicate the pollution status of a river, as described in any one of claims 1-9.

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