Water quality early warning method and device, electronic equipment and storage medium

Abstract

This application relates to the field of water environment management technology, and in particular to a water quality early warning method, device, electronic equipment, and storage medium. The method includes: acquiring a water quality monitoring sequence and a flow sequence of a target river; determining a comprehensive baseline value for the target river based on the water quality monitoring sequence; determining the exceedance probability corresponding to the flow value based on the flow sequence; identifying the hydrological period category of the target river based on the exceedance probability; calculating the seasonal variation coefficient corresponding to the hydrological period category; calculating an early warning threshold for the target river based on the comprehensive baseline value and the seasonal variation coefficient; and generating a comprehensive early warning threshold for the target river based on the early warning threshold. This allows for issuing an early warning for the target river when the water quality monitoring value exceeds the comprehensive early warning threshold. This solves the problem of related technologies using fixed thresholds and relying solely on statistical analysis of water quality monitoring data, which makes it difficult to adapt to differences in hydrological conditions and easily leads to false alarms or missed alarms.

Description

Technical Field

[0001] This application relates to the field of water environment management technology, and in particular to a water quality early warning method, device, electronic equipment and storage medium. Background Technology

[0002] Water environment quality monitoring is a crucial foundation for water environment management. With the rapid development of automatic monitoring technology and online monitoring networks, the temporal resolution of water environment monitoring data has continuously improved, making it possible to conduct water pollution risk early warnings based on real-time monitoring data. The scientific setting of water quality early warning thresholds is a critical aspect of the operation of water pollution early warning systems, directly affecting the accuracy of water quality anomaly identification and the effectiveness of early warning responses.

[0003] In related technologies, threshold setting methods include fixed threshold method and statistical distribution threshold method. Among them, the fixed threshold method usually directly uses environmental quality standards or historical statistical values ​​as warning thresholds, and this method has a simple structure; the statistical distribution threshold method constructs dynamic thresholds through statistical methods, such as setting quantile thresholds based on the distribution characteristics of historical data, or predicting background value changes through time series models.

[0004] However, among related technologies, the fixed threshold method is difficult to reflect the changes in the dilution capacity and hydrodynamic conditions of water bodies under different hydrological conditions. As a result, it cannot adapt to the differences in hydrological conditions when the river flow changes significantly, and is prone to false alarms or omissions. In addition, the statistical distribution threshold method only performs statistical analysis based on the water quality monitoring data itself and does not fully consider the impact of river hydrological conditions on the transport and dilution process of pollutants, which also leads to false alarms or omissions. These issues urgently need to be addressed. Summary of the Invention

[0005] This application provides a water quality early warning method, device, electronic equipment, and storage medium to solve the problem that related technologies use fixed thresholds and rely solely on statistical analysis based on water quality monitoring data, which makes it difficult to adapt to differences in hydrological conditions and easily leads to false alarms or missed alarms.

[0006] The first aspect of this application provides a water quality early warning method, comprising the following steps: acquiring a water quality monitoring sequence and a flow sequence of a target river; determining a comprehensive baseline value of the target river based on the water quality monitoring sequence, determining an exceedance probability corresponding to the flow value based on the flow sequence, identifying the hydrological period category of the target river based on the exceedance probability, and calculating a seasonal variation coefficient corresponding to the hydrological period category; calculating an early warning threshold of the target river based on the comprehensive baseline value and the seasonal variation coefficient, and generating a comprehensive early warning threshold of the target river based on the early warning threshold, so as to issue an early warning for the target river when the water quality monitoring value is greater than the comprehensive early warning threshold.

[0007] Based on the above technical means, this application embodiment determines a comprehensive basic value that takes into account both the current environmental status and management objectives by using water quality monitoring sequences as a basis, and calculates the seasonal variation coefficient by identifying hydrological period categories based on flow sequences, generating early warning thresholds that accompany changes in hydrological conditions, and then issuing early warnings by generating comprehensive early warning thresholds. This effectively incorporates upstream pollution source risks and downstream sensitive protection targets, realizes dynamic adaptation and multi-dimensional correction of early warning thresholds, improves the scientificity and accuracy of water pollution risk early warning, enhances the adaptability of water environment quality monitoring, and strengthens the ability to manage water pollution risks in a refined manner.

[0008] Optionally, in one embodiment of this application, determining the comprehensive baseline value of the target river based on the water quality monitoring sequence includes: calculating the statistical baseline value of the target river based on the water quality monitoring sequence; determining the water quality category limit corresponding to at least one water quality protection target that meets preset sensitivity conditions, and determining the target baseline value of the target river based on the water quality category limit; comparing the statistical baseline value and the target baseline value to determine the comprehensive baseline value.

[0009] Based on the above technical means, the embodiments of this application have pioneered a fusion mechanism of "statistical baseline value" and "target baseline value". The comprehensive baseline value is determined based on the statistical baseline value and the target baseline value. When the water quality is good, the statistical baseline value can be used as the comprehensive baseline value to reflect the actual water environment background level. When the water quality is poor, the target baseline value can be used as the comprehensive baseline value to reflect the concentration upper limit required by the water environment management target. This achieves the organic unity of "bottom-line control" and "fine adjustment" in the water quality early warning threshold base space.

[0010] Optionally, in one embodiment of this application, determining the hydrological period category of the target river channel based on the exceedance probability includes: determining the hydrological period category as a high-water season category when the exceedance probability is less than or equal to a first preset threshold; determining the hydrological period category as a normal-water season category when the exceedance probability is greater than the first preset threshold and less than a second preset threshold; and determining the hydrological period category as a low-water season category when the exceedance probability is greater than or equal to the second preset threshold, wherein the second preset threshold is greater than the first preset threshold.

[0011] Based on the above technical means, this application embodiment can classify the annual hydrological conditions of the target river into three categories: high water period, normal water period, and low water period by judging the relationship between the exceedance probability and the first preset threshold and the second preset threshold. This facilitates the subsequent calculation of the corresponding seasonal variation coefficient based on the hydrological period category and the generation of early warning thresholds that accompany changes in the hydrological period category, thereby enhancing hydrological adaptability and reducing false alarms and missed alarms.

[0012] Optionally, in one embodiment of this application, calculating the seasonal variation coefficient corresponding to the hydrological period category includes: determining the flow sequence within the hydrological period category; calculating the average flow under the hydrological period category based on the flow sequence within the hydrological period category, and calculating the high flow characteristic value under the hydrological period category, wherein the high flow characteristic value is a preset high quantile value of the flow sequence within the hydrological period category; and calculating the seasonal variation coefficient based on the average flow and the high flow characteristic value.

[0013] Based on the above technical means, the embodiments of this application first calculate the average flow and high flow characteristic value under the hydrological period category, and then calculate the seasonal variation coefficient under the hydrological period category. This enables a quantitative expression of the degree of change of river hydrodynamic conditions relative to the average state under different hydrological period categories. It facilitates the subsequent dynamic correction of the comprehensive basic value using the seasonal variation coefficient, generates early warning thresholds that accompany changes in hydrological period categories, enhances hydrological adaptability, and reduces false alarms and missed alarms.

[0014] Optionally, in one embodiment of this application, generating a comprehensive early warning threshold for the target river based on the early warning threshold includes: calculating an average risk index of upstream pollution sources of the target river based on the risk level of upstream pollution sources; calculating a pollution source risk correction coefficient for the target river based on the average risk index of upstream pollution sources; correcting the early warning threshold based on the pollution source risk correction coefficient to generate a corrected early warning threshold for the target river; calculating a distance correction coefficient for pollutants based on the migration process of pollutants in the river; and combining the distance correction coefficient and the corrected early warning threshold to generate the comprehensive early warning threshold.

[0015] Based on the above technical means, this application embodiment innovatively constructs and realizes a dual spatial correction mechanism for "upstream pollution source risk" and "downstream sensitive protection target" by first correcting the early warning threshold using a pollution source risk correction coefficient to obtain a corrected early warning threshold, and then correcting the obtained corrected early warning threshold using a distance correction coefficient. Specifically, the pollution source risk correction coefficient focuses on the upstream of the monitoring section, quantifying the risk level of each pollution source into a unified average risk index, and converting it into a threshold tightening coefficient based on a safety redundancy benchmark value, achieving vertical risk management where "the higher the risk, the stricter the threshold." The distance correction coefficient focuses on the downstream of the monitoring section, converting the spatial distance from the section to the sensitive protection target into a threshold relaxation factor based on the first-order attenuation law of pollutants, achieving horizontal spatial compensation where "the farther the distance, the more sufficient the attenuation, and the threshold can be appropriately relaxed." By simultaneously incorporating the vertical dimension (upstream and downstream risk transmission) and the horizontal dimension (spatial distribution of protection targets) of risk management into the early warning threshold setting method, the regional relevance and spatial scientific nature of the early warning threshold are enhanced, improving the scientificity and accuracy of water pollution risk early warning.

[0016] Optionally, in one embodiment of this application, after generating a comprehensive early warning threshold for the target river based on the early warning threshold, the method further includes: generating a first early warning threshold for the target river based on the comprehensive early warning threshold; calculating a second early warning threshold for the target river based on the first early warning threshold and the seasonal variation coefficient corresponding to the hydrological period category, wherein the second early warning threshold is greater than the first early warning threshold; and generating a third early warning threshold for the target river based on the second early warning threshold, wherein the third early warning threshold is greater than the second early warning threshold.

[0017] Based on the above technical means, this application embodiment innovatively constructs a three-level hierarchical early warning system with a comprehensive early warning threshold as the benchmark and seasonal variation coefficient and fixed amplification coefficient as the difference factors. The three-level threshold gradients are distinct and the progression is reasonable. Its construction mode does not depend on a specific watershed or specific pollutant, and it has good portability and business readability. It is easy to promote and apply in different water environment management scenarios, realize the hierarchical judgment of water environment anomalies, and carry out refined early warning management of water pollution risks.

[0018] A second aspect of this application provides a water quality early warning device, comprising: an acquisition module for acquiring a water quality monitoring sequence and a flow sequence of a target river; a calculation module for determining a comprehensive baseline value of the target river based on the water quality monitoring sequence, determining an exceedance probability corresponding to the flow value based on the flow sequence, identifying the hydrological period category of the target river based on the exceedance probability, and calculating a seasonal variation coefficient corresponding to the hydrological period category; and an early warning module for calculating an early warning threshold of the target river based on the comprehensive baseline value and the seasonal variation coefficient, and generating a comprehensive early warning threshold of the target river based on the early warning threshold, so as to issue an early warning to the target river when the water quality monitoring value is greater than the comprehensive early warning threshold.

[0019] Based on the above technical means, this application embodiment determines a comprehensive basic value that takes into account both the current environmental status and management objectives by using water quality monitoring sequences as a basis, and calculates the seasonal variation coefficient by identifying hydrological period categories based on flow sequences, generating early warning thresholds that accompany changes in hydrological conditions, and then issuing early warnings by generating comprehensive early warning thresholds. This effectively incorporates upstream pollution source risks and downstream sensitive protection targets, realizes dynamic adaptation and multi-dimensional correction of early warning thresholds, improves the scientificity and accuracy of water pollution risk early warning, enhances the adaptability of water environment quality monitoring, and strengthens the ability to manage water pollution risks in a refined manner.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the water quality early warning method as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the water quality early warning method described above.

[0022] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the water quality early warning method described above.

[0023] This application's embodiments, based on water quality monitoring sequences, determine comprehensive baseline values ​​that balance environmental status and management objectives. By identifying hydrological period categories based on flow sequences to calculate seasonal variation coefficients, early warning thresholds accompanying changes in hydrological conditions are generated. These comprehensive early warning thresholds effectively incorporate upstream pollution source risks and downstream sensitive protection targets, achieving dynamic adaptation and multi-dimensional correction of early warning thresholds. This improves the scientific rigor and accuracy of water pollution risk early warnings, enhances the adaptability of water environment quality monitoring, and strengthens the ability to manage water pollution risks with precision. Therefore, it solves the problem of related technologies using fixed thresholds and relying solely on statistical analysis of water quality monitoring data, which makes it difficult to adapt to differences in hydrological conditions and easily leads to false alarms or missed alarms.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a water quality early warning method provided according to an embodiment of this application; Figure 2 This is a block diagram of a water quality early warning device provided according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0026] Figure label: 20-Water quality early warning device; 100-Acquisition module, 200-Calculation module, 300-Early warning module; 301-Memory, 302-Processor, 303-Communication interface. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] Among relevant water environment early warning methods, threshold setting methods mainly include fixed threshold methods, statistical distribution threshold methods, and empirical threshold methods. Fixed threshold methods typically use environmental quality standards or historical statistical values ​​directly as warning thresholds. While this method is simple in structure, it struggles to reflect changes in water body dilution capacity and hydrodynamic conditions under different hydrological conditions. When river flow changes significantly, fixed thresholds often fail to adapt to differences in hydrological conditions, easily leading to false alarms or missed alarms. Some studies have attempted to construct dynamic thresholds using statistical methods, such as setting quantile thresholds based on historical data distribution characteristics or predicting background value changes through time series models. However, these methods usually only perform statistical analysis based on the water quality monitoring data itself, without fully considering the impact of river hydrological conditions on pollutant transport and dilution processes. Furthermore, these methods typically fail to consider spatial factors such as pollution source distribution risks and downstream sensitive protection targets (national surface water assessment sections, cross-boundary sections, centralized drinking water source protection areas) during threshold construction, resulting in warning thresholds lacking specificity under different regional environmental risk conditions.

[0029] In practical water environment management, the hydrodynamic conditions of river systems exhibit significant seasonal variations, with substantial differences in river flow, water dilution capacity, and pollutant transport characteristics across different hydrological periods. Furthermore, the risk levels of upstream pollution sources and the presence of downstream sensitive protection targets also significantly impact water environment risk. Therefore, it is necessary to develop a dynamic adjustment method for early warning thresholds that comprehensively considers changes in hydrological conditions, pollution source risks, and spatial protection needs to improve the scientific rigor and applicability of water pollution early warning systems.

[0030] Therefore, this application provides a water quality early warning method, apparatus, electronic device, and storage medium. The water quality early warning method, apparatus, electronic device, and storage medium of this application are described below with reference to the accompanying drawings.

[0031] Figure 1 This is a flowchart of a water quality early warning method provided according to an embodiment of this application.

[0032] like Figure 1 As shown, this water quality early warning method includes the following steps: In step S101, the water quality monitoring sequence and flow sequence of the target river are obtained.

[0033] Specifically, a water quality monitoring sequence refers to a set of ordered monitoring values ​​arranged chronologically, formed by continuously collecting and recording the same water quality indicator (such as ammonia nitrogen, permanganate index, etc.) in a target river at different consecutive monitoring times, used to statistically analyze water concentration.

[0034] A flow sequence is an ordered set of monitoring values ​​arranged chronologically from the continuous collection and recording of flow values ​​of a target river at different monitoring times. It is used to reflect hydrological conditions and classify hydrological periods.

[0035] In some cases, embodiments of this application may limit the statistical time window to after January 1, 2022, to reflect the current background level of the water environment, thereby obtaining the water quality monitoring sequence within the statistical time window to take into account both the current environmental status and management objectives, and to obtain the river flow sequence corresponding to the monitoring section to classify the hydrological period category.

[0036] In step S102, the comprehensive baseline value of the target river is determined based on the water quality monitoring sequence, the exceedance probability corresponding to the flow value is determined based on the flow sequence, the hydrological period category of the target river is identified based on the exceedance probability, and the seasonal variation coefficient corresponding to the hydrological period category is calculated.

[0037] The following details how embodiments of this application determine the comprehensive baseline value of a target river based on a water quality monitoring sequence, determine the exceedance probability corresponding to the flow value based on the flow sequence, identify the hydrological period category of the target river based on the exceedance probability, and calculate the seasonal variation coefficient corresponding to the hydrological period category.

[0038] In one embodiment of this application, determining the comprehensive baseline value of a target river based on a water quality monitoring sequence includes: calculating the statistical baseline value of the target river based on the water quality monitoring sequence; determining the water quality category limit corresponding to at least one water quality protection target that meets preset sensitivity conditions, and determining the target baseline value of the target river based on the water quality category limit; and comparing the statistical baseline value and the target baseline value to determine the comprehensive baseline value.

[0039] Specifically, in this embodiment of the application, the monitoring time is set to be The water quality monitoring sequence is The total number of valid samples in the water quality monitoring sequence is Then the statistical baseline value The expression can be, but is not limited to, as: (1) in, The statistical baseline value (unit: mg / L, representing the average concentration level of the monitored index within the statistical time window, reflecting the actual background level of the water environment) This represents the total number of valid samples in the water quality monitoring sequence. This is a water quality monitoring sequence.

[0040] This application's embodiments introduce water quality category limits (such as concentration limits specified in Class III and Class IV standards) corresponding to at least one water quality protection target that meets preset sensitivity conditions (such as national surface water assessment sections, cross-boundary sections, and centralized drinking water sources) as the target baseline value. (The unit is mg / L, which represents the upper limit of the concentration required by water environment management targets.)

[0041] To balance the current environmental situation with management objectives, this application defines a comprehensive base value in its embodiments. The formula for finding the minimum of the baseline value and the target baseline value is: (2) in, This is a comprehensive baseline value (unit: mg / L, representing the baseline concentration under the management objectives). To perform the minimum value operation, As the statistical baseline value, This is the target baseline value. When water quality is good, the baseline value is used for statistical analysis. Less than the target base value Comprehensive base value Take the statistical baseline value This reflects the actual background level of the water environment; when the water quality is poor, the statistical baseline value is used. Greater than the target base value Comprehensive base value Take the target base value This reflects the upper limit of concentration required by water environment management objectives.

[0042] Therefore, in this embodiment, the statistical baseline value of the target river can be calculated first based on the water quality monitoring sequence, then the water quality category limit of at least one target that meets the preset sensitivity conditions can be determined, and the water quality category limit can be used as the target baseline value of the target river. Then, the size of the statistical baseline value and the target baseline value can be compared, and the minimum value between the two can be selected as the comprehensive baseline value.

[0043] This application embodiment determines the comprehensive baseline value based on statistical baseline values ​​and target baseline values. When the water quality is good, the statistical baseline value can be used as the comprehensive baseline value to reflect the actual water environment background level. When the water quality is poor, the target baseline value can be used as the comprehensive baseline value to reflect the upper limit of the concentration required by the water environment management target, thus achieving a balance between the current environmental status and management objectives.

[0044] Furthermore, in this embodiment of the application, the flow sequence is set as follows: (Unit: m) 3 / s indicates the monitoring time. (The monitoring section corresponds to the river's flow capacity), and the flow values ​​are sorted from largest to smallest. ( =1,2,…,m), and then define the sorted order... The probability of exceeding a flow value Exceeding probability The expression can be, but is not limited to, as: (3) in, For the sorted number The exceedance probability of the i-th flow value (dimensionless, representing the i-th flow value) The frequency at which a flow value is exceeded in a long-term series. Sort the flow values ​​from largest to smallest using an index. This represents the total number of valid samples in the flow sequence.

[0045] Furthermore, in one embodiment of this application, determining the hydrological period category of the target river channel based on the exceedance probability includes: when the exceedance probability is less than or equal to a first preset threshold, determining the hydrological period category as the high-water period category; when the exceedance probability is greater than the first preset threshold and less than a second preset threshold, determining the hydrological period category as the normal-water period category; when the exceedance probability is greater than or equal to the second preset threshold, determining the hydrological period category as the low-water period category, wherein the second preset threshold is greater than the first preset threshold.

[0046] Specifically, the first preset threshold refers to an exceedance probability threshold set by those skilled in the art based on the statistical characteristics of the flow duration curve after calculating the exceedance probability. The first preset threshold is used as... For example, when the exceedance probability is less than or equal to At that time, the hydrological period category was determined to be the high-water period category.

[0047] The second preset threshold refers to an exceedance probability threshold set by those skilled in the art based on the statistical characteristics of the flow duration curve after calculating the exceedance probability. The second preset threshold is used as the benchmark. For example, when the exceedance probability is greater than At that time, the hydrological period category was determined to be the dry season category.

[0048] Therefore, the embodiments of this application can classify the hydrological period category of the flow sequence according to the statistical characteristics of the flow duration curve, so as to classify the annual hydrological conditions of the target river into the categories of high water period, normal water period and low water period.

[0049] Continue using the first preset threshold as And the second preset threshold is For example, the specific division rule is as follows: when At this time, the hydrological conditions of the target river channel are classified as high-water season; when At this time, the hydrological conditions of the target river channel are in the normal water period category; when At this time, the hydrological conditions of the target river channel are classified as dry season.

[0050] This application embodiment, by judging the relationship between the exceedance probability and the first preset threshold and the second preset threshold, can classify the annual hydrological conditions of the target river into three categories: high-water season, normal-water season, and low-water season. This facilitates the subsequent calculation of the corresponding seasonal variation coefficient based on the hydrological season category and the generation of early warning thresholds that change with the hydrological season category, thereby enhancing hydrological adaptability and reducing false alarms and missed alarms. Thus, this application embodiment transforms the numerical method of flow duration curves into engineering parameters for graded early warning, realizing the automatic identification of hydrological season categories and the objective quantification of seasonal variation coefficients.

[0051] Furthermore, in one embodiment of this application, calculating the seasonal variation coefficient corresponding to a hydrological period category includes: determining the flow sequence within the hydrological period category; calculating the average flow under the hydrological period category based on the flow sequence within the hydrological period category, and calculating the high flow characteristic value under the hydrological period category, wherein the high flow characteristic value is a preset high quantile value of the flow sequence within the hydrological period category; and calculating the seasonal variation coefficient based on the average flow and the high flow characteristic value.

[0052] Based on the descriptions of other embodiments, the embodiments of this application can divide the flow sequence into multiple time sets belonging to different hydrological period categories, denoted as... ,in, This is an index for hydrological period categories.

[0053] The embodiments of this application are for the first Each hydrological period category is defined as follows: The total number of valid samples for flow sequences within each hydrological period category is , No. The flow value within each hydrological period category is Then the first Average flow within each hydrological period category The expression can be, but is not limited to, as: (4) in, For the first Average flow within each hydrological period category For the first Total number of valid samples of flow sequences within each hydrological period category For the first Index of flow values ​​within each hydrological period category For the first Flow values ​​within each hydrological period category.

[0054] To characterize the first The embodiments of this application use the high quantile value of the flow sequence within that period to calculate the high-flow hydrological conditions that may occur within a hydrological period category. High flow characteristic values ​​for each hydrological period category . No. High flow characteristic values ​​for each hydrological period category The expression can be, but is not limited to, as: (5) in, For the first High flow characteristic values ​​for each hydrological period category To obtain the 90th percentile (i.e., the 90th percentile) After sorting the flow values ​​within each hydrological period category from largest to smallest, the values ​​greater than 90% and less than 10% of the flow values ​​are selected. For the first The first value after sorting the flow values ​​within each hydrological period category from largest to smallest. For the first The second value after sorting the flow values ​​within each hydrological period category from largest to smallest. For the first The flow values ​​within each hydrological period category are sorted from largest to smallest. A number.

[0055] After obtaining the average flow rate and high flow rate characteristic values, the embodiments of this application calculate the first... Seasonal variation coefficients for each hydrological period category . No. Seasonal variation coefficients for each hydrological period category The expression can be, but is not limited to, as: (6) in, For the first Seasonal variation coefficients for each hydrological period category (dimensionless, representing the degree of change in river hydrodynamic conditions relative to the mean state under different hydrological period categories). For the first High flow characteristic values ​​for each hydrological period category For the first Average flow rate within each hydrological period category.

[0056] It should be noted that the above statistical calculations are all based on a defined statistical time window, that is, the time range must be after January 1, 2022, and cover at least one complete hydrological cycle.

[0057] Therefore, in this embodiment of the application, after dividing the flow sequence into multiple time sets belonging to different hydrological period categories, the average flow under a certain hydrological period category is calculated for the flow sequence within a certain hydrological period category, and the high flow characteristic value under a certain hydrological period category is calculated. Then, the seasonal variation coefficient of a certain hydrological period category is calculated based on the average flow and the high flow characteristic value.

[0058] This application embodiment first calculates the average flow and high flow characteristic value under the hydrological period category, and then calculates the seasonal variation coefficient under the hydrological period category. This enables a quantitative expression of the degree of change of river hydrodynamic conditions relative to the average state under different hydrological period categories. It facilitates the subsequent dynamic correction of the comprehensive basic value using the seasonal variation coefficient, generates early warning thresholds that accompany changes in hydrological period categories, enhances hydrological adaptability, and reduces false alarms and missed alarms.

[0059] In step S103, the warning threshold of the target river is calculated based on the comprehensive baseline value and the seasonal variation coefficient, and a comprehensive warning threshold of the target river is generated based on the warning threshold, so as to issue a warning to the target river when the water quality monitoring value is greater than the comprehensive warning threshold.

[0060] Specifically, the warning threshold refers to a dynamic warning threshold calculated based on the differences in hydrological period categories and combined with comprehensive baseline values, which is used to adaptively adjust with seasonal changes in hydrological conditions.

[0061] The comprehensive early warning threshold refers to the dynamic early warning threshold obtained by overlaying the risk level of upstream pollution sources, the spatial distance of downstream sensitive targets, and the characteristics of pollutant transport and attenuation on the basis of the early warning threshold. It is used to construct a graded early warning judgment mechanism.

[0062] The following details how the embodiments of this application calculate the warning threshold of the target river based on the comprehensive basic value and the seasonal variation coefficient, and generate a comprehensive warning threshold for the target river based on the warning threshold, so as to issue a warning for the target river when the water quality monitoring value is greater than the comprehensive warning threshold.

[0063] First, the embodiments of this application set the monitoring time. Hydrological period category is Then the first Warning thresholds for each hydrological period category The expression can be, but is not limited to, as: (7) in, For the first Warning thresholds for each hydrological period category, To synthesize the base value, For the first Seasonal variation coefficients for each hydrological period category.

[0064] Therefore, the embodiments of this application obtain a comprehensive basic value. With the Seasonal variation coefficients for each hydrological period category After that, the first generation is generated. Warning thresholds for each hydrological period category.

[0065] As can be understood from the description of other embodiments, when the monitoring system operates in different hydrological period categories, it calls the corresponding seasonal variation coefficient according to the period to which the current time belongs, thereby obtaining the corresponding early warning threshold.

[0066] Furthermore, in one embodiment of this application, generating a comprehensive early warning threshold for a target river based on an early warning threshold includes: calculating an average risk index of upstream pollution sources for the target river based on the risk level of upstream pollution sources; calculating a pollution source risk correction coefficient for the target river based on the average risk index of upstream pollution sources; correcting the early warning threshold based on the pollution source risk correction coefficient to generate a corrected early warning threshold for the target river; calculating a distance correction coefficient for pollutants based on the migration process of pollutants in the river; and combining the distance correction coefficient and the corrected early warning threshold to generate a comprehensive early warning threshold.

[0067] It will be understood from the description of other embodiments that, upon obtaining the first... Warning thresholds for each hydrological period category Subsequently, embodiments of this application can combine the risk level of upstream pollution sources with downstream sensitive protection targets (i.e., at least one target that meets preset sensitive conditions) to set early warning thresholds. Make corrections to obtain a comprehensive early warning threshold.

[0068] Specifically, this application embodiment first considers the risk level of pollution sources upstream of the monitoring section. This application embodiment assumes a common... There are one upstream pollution source, and the risk level of the upstream pollution source is: ( The risk level is determined according to the "Enterprise Emergency Environmental Incident Risk Classification Method" (HJ 941—2018), then the average risk index of upstream pollution sources is... The expression can be, but is not limited to, as: (8) in, This represents the average risk index of upstream pollution sources. This represents the total number of upstream pollution sources. For upstream pollution source indexing, The risk level of upstream pollution sources.

[0069] Then, in this embodiment of the application, the security redundancy value in the risk level is set to... Its value is the sum of the highest quantified value in the risk level system and 1. Taking the quantified values ​​in the risk level system as 0, 1, 2, 3, and 4 as an example, the safety redundancy value... This is used to maintain a level of safety redundancy at the highest known risk level. Pollution source risk correction factor. The expression can be, but is not limited to, as: (9) in, This is the pollution source risk correction coefficient (dimensionless, ranging from 0 to 1). This represents the average risk index of pollution sources. This is a safety redundancy value. Ideally, if the risk level quantification value of all upstream pollution sources is 0, then the pollution source risk correction coefficient... A value of 1 indicates that the threshold does not need to be tightened further; if the quantified risk level values ​​of all upstream pollution sources reach the safe redundancy value. Then the pollution source risk correction coefficient A value of 0 indicates that the threshold is tightened to its most stringent level.

[0070] Based on this, the embodiments of this application specify the warning threshold. The first correction is performed to obtain the correction warning threshold. Adjust the warning threshold. The expression can be, but is not limited to, as: (10) in, To correct the warning threshold, As the warning threshold, This is a pollution source risk correction coefficient. If the average risk index of upstream pollution sources... The larger the value, the higher the pollution source risk correction coefficient. The smaller the value, the better the warning threshold. The smaller the average risk index of upstream pollution sources; The smaller the value, the lower the pollution source risk correction coefficient. The larger the value, the higher the warning threshold. The closer it is to constant. Taking the case where all upstream pollution sources are at the highest risk level as an example, the average risk index of upstream pollution sources at this time is... Pollution source risk correction coefficient Warning threshold The value is tightened to 20% of its original value, but not reduced to zero, ensuring that the early warning system still has physical significance under extreme risks.

[0071] Furthermore, considering that the migration process of pollutants in rivers follows a first-order decay law, the expression of this application embodiment can be, but is not limited to, as follows: (11) in, The concentration at the downstream section, The concentration at the upstream section, The pollutant attenuation coefficient, This refers to the time it takes for pollutants to spread.

[0072] because , This refers to the distance from the cross-section to sensitive targets (national surface water assessment cross-sections, transboundary cross-sections, centralized drinking water source protection areas, etc.). If the average flow velocity is used, the expression can be expanded as follows: (12) Next, the equivalent attenuation coefficient is defined in the embodiments of this application. Then the distance correction factor The expression can be, but is not limited to, as: (13) in, This is the distance correction factor. The equivalent attenuation coefficient, This represents the distance from the cross-section to the sensitive target.

[0073] Therefore, the embodiments of this application specify the warning threshold. A second correction was made to obtain the comprehensive early warning threshold. Comprehensive early warning threshold The expression can be, but is not limited to, as: (14) in, To establish a comprehensive early warning threshold, To correct the warning threshold, This is the distance correction factor.

[0074] It should be noted that the distance correction factor Characterizing the natural attenuation effect of pollutants during transport, the greater the distance (i.e., the distance from the cross-section to the sensitive target). The larger the value, or the stronger the attenuation (i.e., the equivalent attenuation coefficient). The larger the threshold, the more lenient the overall early warning threshold. (The larger the value). Pollutant attenuation coefficient Information can be obtained through the following methods: public databases or literature; selection of experience range: industry experience values ​​are selected according to the "Technical Guidelines for Water Environment Impact Assessment - Surface Water Environment" (HJ 2.3—2018); inversion calculation based on upstream and downstream monitoring data. ,in, The concentration at the upstream section, The concentration at the downstream section is... This refers to the time it takes for pollutants to spread.

[0075] Therefore, in this embodiment, the average risk index of upstream pollution sources of the target river is first calculated based on the risk level of upstream pollution sources of the target river. Then, the pollution source risk correction coefficient of the target river is calculated based on the average risk index of upstream pollution sources. Subsequently, the pollution source risk correction coefficient is used to correct the early warning threshold, thereby generating the corrected early warning threshold of the target river. Then, based on the migration process of pollutants in the river, the distance correction coefficient of pollutants is calculated, and the distance correction coefficient is used to correct the obtained corrected early warning threshold, thereby generating a comprehensive early warning threshold.

[0076] This application embodiment first uses a pollution source risk correction coefficient to correct the early warning threshold, and then uses a distance correction coefficient to correct the obtained corrected early warning threshold, thus obtaining a comprehensive early warning threshold. This achieves the goal of combining the risk level of upstream pollution sources with the downstream sensitive protection targets to calculate the threshold, thereby enhancing the regional and spatial targeting and improving the scientific nature and accuracy of water pollution risk early warning.

[0077] Furthermore, in one embodiment of this application, after generating a comprehensive early warning threshold for the target river based on the early warning threshold, the method further includes: generating a first early warning threshold for the target river based on the comprehensive early warning threshold; calculating a second early warning threshold for the target river based on the first early warning threshold and the seasonal variation coefficient corresponding to the hydrological period category, wherein the second early warning threshold is greater than the first early warning threshold; and generating a third early warning threshold for the target river based on the second early warning threshold, wherein the third early warning threshold is greater than the second early warning threshold.

[0078] Specifically, the first warning threshold refers to the warning threshold directly set by those skilled in the art based on the comprehensive warning threshold, and is used as the criterion for judging slight water pollution.

[0079] The second warning threshold refers to a warning threshold obtained by those skilled in the art based on the first warning threshold and modified by a seasonal variation coefficient. Since the seasonal variation coefficient is obtained by dividing the high flow characteristic value by the average flow, the seasonal variation coefficient is greater than... Therefore, the second warning threshold is greater than the first warning threshold and is used as the criterion for determining moderate water pollution.

[0080] The third warning threshold refers to the warning threshold obtained by those skilled in the art by amplifying the second warning threshold using a fixed ratio, and is used as a criterion for determining serious water pollution.

[0081] Based on the descriptions of other embodiments, the embodiments of this application address the comprehensive early warning threshold. Based on this, higher-level early warning thresholds can be constructed to build a tiered early warning mechanism.

[0082] Taking the three-level warning system (yellow, orange, and red) as an example, this application embodiment will comprehensively consider the warning thresholds. Defined as the first warning threshold First warning threshold The expression can be, but is not limited to, as: (15) in, For monitoring time The first warning threshold This is the comprehensive early warning threshold. It can be understood that the first early warning threshold... The study has taken into account hydrological changes, upstream pollution source risks, and the spatial relationships of downstream sensitive targets.

[0083] Based on this, the embodiments of this application use the obtained seasonal variation coefficients. A higher-level early warning threshold is established. This application embodiment sets the monitoring time... Hydrological period category is Then the second warning threshold The expression can be, but is not limited to, as: (16) in, For monitoring time The second warning threshold, For monitoring time The first warning threshold For the first The seasonal variation coefficients for each hydrological period category. It is understandable that when the monitored value exceeds the second warning threshold... This indicates that the water quality has significantly improved relative to the baseline state of the current hydrological conditions.

[0084] This application embodiment has a second early warning threshold. Based on this, a third early warning threshold is constructed. The third warning threshold The expression can be, but is not limited to, as: (17) in, The third warning threshold, This is the second warning threshold. This is the amplification factor (used to distinguish higher-intensity abnormal states). It can be understood that when the monitored value exceeds the third warning threshold... This indicates a high degree of abnormality in water quality, potentially posing a significant risk of pollution.

[0085] It should be noted that the relationship between the first warning threshold, the second warning threshold, and the third warning threshold is as follows: (18) Therefore, the embodiments of this application determine that the real-time water quality monitoring values ​​meet the requirements. When the water quality is normal, no warning will be triggered. This application embodiment determines that the water quality monitoring value meets the requirements. When this occurs, it indicates that the water quality has slightly improved relative to the current hydrological conditions, and there is a possibility of slight water pollution, triggering a yellow alert. This application embodiment determines that the water quality monitoring value meets the requirements. When this occurs, it indicates that the water quality has significantly deteriorated compared to the current hydrological conditions, and there is a possibility of moderate pollution. At this time, an orange alert is triggered. This application embodiment determines that the water quality monitoring value meets the requirements. When this occurs, it indicates that the water quality has risen sharply relative to the current hydrological conditions, and there is a possibility of significant water pollution, triggering a red alert.

[0086] This application embodiment generates multiple early warning thresholds based on a comprehensive early warning threshold, effectively forming a graded early warning judgment mechanism to achieve graded judgment of water environment anomalies, facilitating water environment quality monitoring of rivers and refined early warning management of water pollution risks.

[0087] The principle of the water quality early warning method proposed in this application is illustrated below with a specific embodiment.

[0088] In this embodiment, the statistical time window can be limited to after January 1, 2022, so as to obtain the water quality monitoring sequence within the statistical time window, so as to take into account both the current environmental status and management objectives, and obtain the river flow sequence corresponding to the monitoring section to classify the hydrological period category.

[0089] Furthermore, in this embodiment, the statistical baseline value of the target river channel can be calculated first based on the water quality monitoring sequence, then the water quality category limit value that meets the preset sensitivity conditions can be determined, and the water quality category limit value can be used as the target baseline value of the target river channel. The statistical baseline value and the target baseline value can then be compared, and the minimum value between the two can be selected as the comprehensive baseline value. In addition, this embodiment sorts the flow values ​​in the flow sequence from largest to smallest and calculates the exceedance probability corresponding to each flow value.

[0090] Furthermore, in this embodiment, the flow sequence can be classified into hydrological period categories based on the statistical characteristics of the flow duration curve, thereby dividing the annual hydrological conditions of the target river into high-water, normal-water, and low-water periods. After dividing the flow sequence into multiple time sets belonging to different hydrological period categories, the average flow rate under a specific hydrological period category is calculated for the flow sequence within that category, and a high-flow characteristic value is also calculated. Then, the seasonal variation coefficient for that hydrological period category is calculated based on the average flow rate and the high-flow characteristic value. Finally, in this embodiment, the comprehensive baseline value is multiplied by the seasonal variation coefficient to obtain the warning threshold for the target river.

[0091] Furthermore, in this embodiment, the average risk index of upstream pollution sources in the target river is first calculated based on the risk level of upstream pollution sources in the target river. Then, the pollution source risk correction coefficient of the target river is calculated based on the average risk index of upstream pollution sources. Subsequently, the pollution source risk correction coefficient is used to correct the early warning threshold, thereby generating the corrected early warning threshold of the target river. Then, based on the migration process of pollutants in the river, the distance correction coefficient of pollutants is calculated, and the distance correction coefficient is used to correct the obtained corrected early warning threshold, thereby generating a comprehensive early warning threshold.

[0092] Furthermore, in this embodiment of the application, a first warning threshold for the target river is generated based on a comprehensive warning threshold, so as to trigger a yellow warning when the water quality monitoring value is greater than the first warning threshold. A second warning threshold for the target river is generated based on the first warning threshold and a seasonal variation coefficient, so as to trigger an orange warning when the water quality monitoring value is greater than the second warning threshold. A third warning threshold for the target river is generated based on the second warning threshold, so as to trigger a red warning when the water quality monitoring value is greater than the third warning threshold.

[0093] The water quality early warning method proposed in this application determines a comprehensive baseline value that balances environmental status and management objectives based on water quality monitoring sequences. It then identifies hydrological period categories based on flow sequences to calculate seasonal variation coefficients, generating early warning thresholds that align with changes in hydrological conditions. This comprehensive early warning threshold effectively incorporates upstream pollution source risks and downstream sensitive protection targets, enabling dynamic adaptation and multi-dimensional correction of the early warning thresholds. This improves the scientific rigor and accuracy of water pollution risk early warning, enhances the adaptability of water environment quality monitoring, and strengthens the ability to manage water pollution risks with precision. Therefore, it solves the problem of related technologies using fixed thresholds and relying solely on statistical analysis of water quality monitoring data, which makes it difficult to adapt to differences in hydrological conditions and easily leads to false alarms or missed alarms.

[0094] Next, the water quality early warning device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0095] Figure 2 This is a block diagram of a water quality early warning device provided according to an embodiment of this application.

[0096] like Figure 2 As shown, the water quality early warning device 20 includes: an acquisition module 100, a calculation module 200, and an early warning module 300.

[0097] The acquisition module 100 is used to acquire the water quality monitoring sequence and flow sequence of the target river.

[0098] The calculation module 200 is used to determine the comprehensive basic value of the target river based on the water quality monitoring sequence, determine the exceedance probability corresponding to the flow value based on the flow sequence, identify the hydrological period category of the target river based on the exceedance probability, and calculate the seasonal variation coefficient corresponding to the hydrological period category.

[0099] The early warning module 300 is used to calculate the early warning threshold of the target river based on the comprehensive baseline value and the seasonal variation coefficient, and to generate a comprehensive early warning threshold for the target river based on the early warning threshold, so as to issue an early warning for the target river when the water quality monitoring value is greater than the comprehensive early warning threshold.

[0100] Optionally, in one embodiment of this application, the calculation module 200 includes: a first calculation unit, a first determination unit, and a second determination unit.

[0101] The first calculation unit is used to calculate the statistical baseline value of the target river channel based on the water quality monitoring sequence.

[0102] The first determining unit is used to determine the water quality category limit corresponding to at least one water quality protection target that meets the preset sensitive conditions, and to determine the target basic value of the target river based on the water quality category limit.

[0103] The second determining unit is used to compare the statistical baseline value and the target baseline value to determine the comprehensive baseline value.

[0104] Optionally, in one embodiment of this application, the calculation module 200 includes: a third determining unit, a fourth determining unit, and a fifth determining unit.

[0105] The third determining unit is used to determine the hydrological period category as the high-water period category when the exceedance probability is less than or equal to the first preset threshold.

[0106] The fourth determining unit is used to determine the hydrological period category as the normal water period category when the exceedance probability is greater than the first preset threshold and less than the second preset threshold.

[0107] The fifth determining unit is used to determine the hydrological period category as the dry season category when the exceedance probability is greater than or equal to the second preset threshold, wherein the second preset threshold is greater than the first preset threshold.

[0108] Optionally, in one embodiment of this application, the calculation module 200 includes: a sixth determining unit, a second calculation unit, and a third calculation unit.

[0109] The sixth determining unit is used to determine the flow sequence within the hydrological period category.

[0110] The second calculation unit is used to calculate the average flow under the hydrological period category based on the flow sequence within the hydrological period category, and to calculate the high flow characteristic value under the hydrological period category, wherein the high flow characteristic value is a preset high quantile value of the flow sequence within the hydrological period category.

[0111] The third calculation unit is used to calculate the seasonal variation coefficient based on the average flow and high flow characteristic values.

[0112] Optionally, in one embodiment of this application, the early warning module 300 includes: a fourth calculation unit, a fifth calculation unit, a correction unit, a sixth calculation unit, and a generation unit.

[0113] The fourth calculation unit is used to calculate the average risk index of upstream pollution sources in the target river based on the risk level of upstream pollution sources in the target river.

[0114] The fifth calculation unit is used to calculate the pollution source risk correction coefficient of the target river channel based on the average risk index of upstream pollution sources.

[0115] The correction unit is used to correct the early warning threshold based on the pollution source risk correction coefficient and generate the corrected early warning threshold for the target river.

[0116] The sixth calculation unit is used to calculate the distance correction factor for pollutants based on their migration process in the river.

[0117] The generation unit is used to combine the distance correction coefficient and the correction warning threshold to generate a comprehensive warning threshold.

[0118] Optionally, in one embodiment of this application, it further includes: a first generation module, a second generation module, and a third generation module.

[0119] The first generation module is used to generate the first early warning threshold for the target river channel based on the comprehensive early warning threshold.

[0120] The second generation module is used to calculate the second warning threshold of the target river channel based on the first warning threshold and the seasonal variation coefficient corresponding to the hydrological period category, wherein the second warning threshold is greater than the first warning threshold.

[0121] The third generation module is used to generate a third early warning threshold for the target river channel based on the second early warning threshold, wherein the third early warning threshold is greater than the second early warning threshold.

[0122] It should be noted that the foregoing explanation of the water quality early warning method embodiment also applies to the water quality early warning device of this embodiment, and will not be repeated here.

[0123] The water quality early warning device proposed in this application determines a comprehensive baseline value that balances environmental status and management objectives based on water quality monitoring sequences. It then identifies hydrological period categories based on flow sequences to calculate seasonal variation coefficients, generating early warning thresholds that align with changes in hydrological conditions. This comprehensive early warning threshold effectively incorporates upstream pollution source risks and downstream sensitive protection targets, enabling dynamic adaptation and multi-dimensional correction of the early warning thresholds. This improves the scientific rigor and accuracy of water pollution risk early warning, enhances the adaptability of water environment quality monitoring, and strengthens the ability to manage water pollution risks with precision. Therefore, it solves the problem of related technologies using fixed thresholds and relying solely on statistical analysis of water quality monitoring data, which makes it difficult to adapt to differences in hydrological conditions and easily leads to false alarms or missed alarms.

[0124] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0125] When the processor 302 executes the program, it implements the water quality early warning method provided in the above embodiments.

[0126] Furthermore, electronic devices also include: Communication interface 303 is used for communication between memory 301 and processor 302.

[0127] The memory 301 is used to store computer programs that can run on the processor 302.

[0128] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0129] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0130] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0131] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0132] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the water quality early warning method described above.

[0133] This application also provides a computer program product, including a computer program that, when executed, implements the water quality early warning method described above.

[0134] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0135] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0136] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0138] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0139] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0140] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0141] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A water quality early warning method, characterized in that, Includes the following steps: Obtain water quality and flow sequences for the target river channel; The comprehensive baseline value of the target river is determined based on the water quality monitoring sequence, and the exceedance probability corresponding to the flow value is determined based on the flow sequence. The hydrological period category of the target river is identified based on the exceedance probability, and the seasonal variation coefficient corresponding to the hydrological period category is calculated. The warning threshold of the target river is calculated based on the comprehensive baseline value and the seasonal variation coefficient, and a comprehensive warning threshold of the target river is generated based on the warning threshold, so as to issue a warning for the target river when the water quality monitoring value is greater than the comprehensive warning threshold.

2. The method according to claim 1, characterized in that, The determination of the comprehensive baseline value of the target river channel based on the water quality monitoring sequence includes: Calculate the statistical baseline value of the target river channel based on the water quality monitoring sequence; Determine the water quality category limit corresponding to at least one water quality protection target that meets the preset sensitivity conditions, and determine the target basic value of the target river based on the water quality category limit; The statistical baseline value and the target baseline value are compared to determine the comprehensive baseline value.

3. The method according to claim 1, characterized in that, Determining the hydrological period category of the target river channel based on the exceedance probability includes: When the exceedance probability is less than or equal to the first preset threshold, the hydrological period category is determined to be the high-water period category; When the exceedance probability is greater than the first preset threshold and less than the second preset threshold, the hydrological period category is determined to be the normal water period category. When the exceedance probability is greater than or equal to the second preset threshold, the hydrological period category is determined to be the dry season category, wherein the second preset threshold is greater than the first preset threshold.

4. The method according to claim 1, characterized in that, The calculation of the seasonal variation coefficient corresponding to the hydrological period category includes: Determine the flow sequence within the specified hydrological period category; The average flow rate under the hydrological period category is calculated based on the flow rate sequence within the hydrological period category, and the high flow rate characteristic value under the hydrological period category is calculated, wherein the high flow rate characteristic value is the preset high quantile value of the flow rate sequence within the hydrological period category; The seasonal variation coefficient is calculated based on the average flow rate and the high flow rate characteristic value.

5. The method according to claim 1, characterized in that, The step of generating a comprehensive early warning threshold for the target river channel based on the early warning threshold includes: Calculate the average risk index of upstream pollution sources of the target river based on the risk level of upstream pollution sources of the target river. The pollution source risk correction coefficient for the target river channel is calculated based on the average risk index of the upstream pollution sources. The warning threshold is corrected based on the pollution source risk correction coefficient to generate the corrected warning threshold for the target river. Based on the migration process of pollutants in the river, a distance correction factor for the pollutants is calculated; The comprehensive warning threshold is generated by combining the distance correction coefficient and the correction warning threshold.

6. The method according to claim 1, characterized in that, After generating the comprehensive early warning threshold for the target river channel based on the aforementioned early warning threshold, the method further includes: A first early warning threshold for the target river channel is generated based on the comprehensive early warning threshold. The second warning threshold for the target river is calculated based on the seasonal variation coefficient corresponding to the first warning threshold and the hydrological period category, wherein the second warning threshold is greater than the first warning threshold. A third warning threshold for the target river channel is generated based on the second warning threshold, wherein the third warning threshold is greater than the second warning threshold.

7. A water quality early warning device, characterized in that, include: The acquisition module is used to acquire water quality monitoring sequences and flow sequences of the target river channel; The calculation module is used to determine the comprehensive basic value of the target river channel based on the water quality monitoring sequence, determine the exceedance probability corresponding to the flow value based on the flow sequence, identify the hydrological period category of the target river channel based on the exceedance probability, and calculate the seasonal variation coefficient corresponding to the hydrological period category. The early warning module is used to calculate the early warning threshold of the target river based on the comprehensive basic value and the seasonal variation coefficient, and generate a comprehensive early warning threshold of the target river based on the early warning threshold, so as to issue an early warning to the target river when the water quality monitoring value is greater than the comprehensive early warning threshold.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the water quality early warning method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the water quality early warning method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the water quality early warning method as described in any one of claims 1-6.

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