Governance resource occupation evaluation method for sewage treatment scene

Abstract

The application discloses a treatment resource occupation evaluation method for a sewage treatment scene, relates to the technical field of sewage treatment, and is used for solving the problem of unbalanced allocation of supervision resources caused by the lack of dynamic risk quantification in the supervision stage after the black and odorous water body is removed, generating water quality rebound tendency characteristics by collecting key index fluctuation amplitude of the removed water body and upstream water quality fluctuation data, combining the changed amount of the direct discharge into river sewage outlet to generate a relapse risk preliminary evaluation grade, introducing social perception deviation degree to obtain a risk calibration grade by Sigmoid correction, and then performing exponential decay time sequence correction to obtain a modified risk grade, planning a supervision intensity benchmark according to the modified risk grade, evaluating resource occupation rationality by a logarithmic ratio method, generating a resource occupation planning benchmark by exponential scaling, and outputting occupation adjustment instructions, so that dynamic and accurate redistribution of supervision resources is realized.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and more specifically, to a method for assessing the resource occupancy of wastewater treatment in specific wastewater treatment scenarios. Background Technology

[0002] The treatment of polluted and odorous water bodies is an important component of urban water ecological environment protection. After completing the treatment of polluted and odorous water bodies and passing the acceptance inspection, local authorities need to conduct continuous follow-up supervision to prevent water quality rebound and recurrence in these water bodies. The follow-up supervision phase typically includes various supervision methods such as water quality sampling, on-site inspections, and public satisfaction reviews. In practice, based on data from multiple channels such as sewage outlet ledgers, online monitoring devices, and citizen complaints, periodic resource occupancy assessments and task scheduling are carried out for the water bodies that have been delisted to maintain effective long-term supervision coverage.

[0003] The existing technology has the following shortcomings:

[0004] Currently, existing technologies for monitoring and allocating resources for delisted water bodies mainly rely on a fixed-cycle, unified standard. This lacks dynamic and comprehensive quantification of water quality rebound risk, deviations in public perception, and operational sequence characteristics. Consequently, it cannot accurately reflect the differences in the actual recurrence risk of different delisted water bodies during the monitoring phase. This results in shortcomings such as missed management and inspection of high-risk water bodies, expansion of regulatory blind spots, and the inability of limited administrative resources to form a precise and dynamic reallocation. Therefore, this paper proposes a method for assessing the occupation of treatment resources for wastewater treatment scenarios.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for assessing the resource occupancy of wastewater treatment scenarios. This method integrates techniques such as generating water quality rebound tendency characteristics, dynamically correcting multi-dimensional risk levels, evaluating the logarithmic ratio of resource occupancy rationality, and outputting exponential scaling planning benchmarks to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the resource occupancy of wastewater treatment, the specific operation process of which is as follows:

[0008] Step S1: Collect the fluctuation range of key indicators of the delisted water body and the fluctuation data of upstream water quality, comprehensively generate the water quality rebound tendency characteristics, read the change of sewage outlets directly discharging into the river, and generate the initial assessment level of recurrence risk based on the water quality rebound tendency characteristics.

[0009] Step S2: Retrieve the complaint response record after the case is closed, analyze the social perception deviation based on the complaint response record, generate a risk calibration level using the initial risk assessment level of recurrence and the social perception deviation, retrieve the running time after the case is closed, perform time-series decay correction on the risk calibration level based on the running time after the case is closed, and generate a corrected risk level.

[0010] Step S3: Collect the current review and inspection task execution coverage rate, plan the regulatory intensity benchmark for delisted water bodies based on the revised risk level, assess the rationality of resource occupation in combination with the review and inspection task execution coverage rate, and generate a resource occupation planning benchmark after replanning the review and inspection regulatory resources based on the rationality of resource occupation.

[0011] Step S4: Determine whether to reduce or increase the occupation of water bodies that have been delisted based on the resource occupation planning benchmark. Based on the determination result, choose to increase the frequency of follow-up supervision or cancel redundant supervision tasks.

[0012] In a preferred embodiment, in step S1, the delisted water body is the water body that has been removed from the list of black and odorous water bodies after passing the acceptance of black and odorous water body treatment and has entered the continuous monitoring stage.

[0013] The ammonia nitrogen concentration of the delisted water body was sampled multiple times during the review period using water quality monitoring devices to obtain an ammonia nitrogen concentration sampling sequence. The mean and standard deviation of the ammonia nitrogen concentration sampling sequence were calculated, and the ratio of the standard deviation to the mean was taken as the fluctuation range of the key indicator sampling of the delisted water body.

[0014] The ammonia nitrogen concentration of the upstream water was continuously monitored within the same review period to obtain a continuous monitoring sequence; the mean and standard deviation of the continuous monitoring sequence were calculated, and the ratio of the standard deviation to the mean was used to obtain the water quality fluctuation data of the upstream water.

[0015] The fluctuation range of key indicators of delisted water bodies and the fluctuation data of upstream water quality were standardized using the Max-min standardization method. The water quality rebound tendency characteristics were calculated by combining the standardized results.

[0016] In a preferred embodiment, in step S1, the changes in the river discharge outlets during the review period are read through the dynamic ledger database of discharge outlets, and the number of new, changed and blocked discharge outlets that directly discharge into the delisted water bodies are counted during the review period. The result of the sum of the replacement numbers is used as the change amount of direct discharge outlets into the river.

[0017] The initial risk index of recurrence was calculated by combining the water quality rebound tendency characteristics with the standardized results of changes in the amount of sewage outlets directly discharging into rivers using the harmonic mean method.

[0018] In a preferred embodiment, in step S1, the initial recurrence risk assessment index is compared with the upper and lower tier thresholds:

[0019] When the initial recurrence risk index is greater than the upper score threshold, the initial recurrence risk level is output as severe.

[0020] When the initial recurrence risk index is between the lower and upper thresholds, the initial recurrence risk level is output as medium.

[0021] When the initial recurrence risk index is less than the lower threshold, the initial recurrence risk level is output as mild.

[0022] In a preferred embodiment, in step S2, the post-cancellation complaint response records of the cancelled water bodies are read within the post-cancellation review period;

[0023] Extract two sub-dimensional parameters from the complaint response records after account closure as follows:

[0024] The complaint density is obtained by taking the ratio of the number of complaints related to the delisted water bodies to the length of the shoreline of the delisted water bodies;

[0025] The response closure degree is obtained by taking the ratio of the number of closed-loop complaints related to the delisted water bodies to the total number of complaints.

[0026] The complaint density and response closure degree were standardized using the Max-min standardization method to obtain the standardized results of the complaint density and response closure degree. The social perception deviation was calculated by combining the standardized results using the geometric mean method.

[0027] In a preferred embodiment, in step S2, a numerical result is set for the initial recurrence risk level, and the deviation between the numerical result of the initial recurrence risk level and the social perception is smoothed and corrected by the Sigmoid function to obtain the risk calibration level.

[0028] Among them, the numerical results of the risk calibration level are mapped back to the three levels of mild, moderate and severe according to 1, 2 and 3 respectively;

[0029] The time elapsed after the cancellation of the water body is obtained by retrieving the cancellation acceptance date from the cancellation archive database and taking the difference between the date and the current time.

[0030] After the cancellation is approved, the runtime is corrected by performing a time-series decay correction on the numerical result of the risk calibration level using an exponential decay function, resulting in the corrected numerical result of the risk level.

[0031] The revised risk level numerical results are mapped back to the three levels of mild, moderate, and severe as 1, 2, and 3 respectively, to obtain the revised risk level.

[0032] In a preferred embodiment, in step S3, the patrol execution records of the delisted water bodies during the review cycle are read from the patrol task management database, and the ratio of the length of the river segment that has been patrolled to the total length of the delisted water body is taken to obtain the review patrol task execution coverage rate.

[0033] The regulatory intensity benchmark is generated based on the revised risk level using a segmented mapping method. The specific rules are as follows:

[0034] When the revised risk level is minor, the regulatory intensity benchmark will be set to a low value.

[0035] When the revised risk level is medium, the regulatory intensity benchmark will be set to the median calibration value.

[0036] When the revised risk level is severe, the regulatory intensity benchmark will be set to a high value.

[0037] The low-level, median, and high-level calibration values ​​are set as follows: the historical review data of resource input in the region are grouped according to the corresponding corrected risk level, and the median of the review and inspection coverage rate of the delisted water bodies is taken as the corresponding calibration value.

[0038] In a preferred embodiment, in step S3, the deviation between the coverage rate of the follow-up inspection task and the regulatory intensity benchmark is evaluated using the logarithmic ratio method to obtain the rationality of resource utilization.

[0039] The rationality of resource utilization is adjusted by the deviation of the regulatory intensity benchmark through the exponential scaling method to generate the resource utilization planning benchmark.

[0040] In a preferred embodiment, in step S4, the regulatory intensity benchmark generated in the current cycle for the delisted water body is retrieved, and the ratio between the resource occupation planning benchmark and the regulatory intensity benchmark is taken to obtain the occupation change ratio.

[0041] Compare the percentage change in occupancy with the thresholds for determining increases in occupancy and reductions in occupancy:

[0042] When the change in occupancy ratio exceeds the threshold for reinforcement, an reinforcement instruction is output, the frequency of follow-up supervision is increased for the delisted water body, and recurrence risk tracking is initiated.

[0043] When the change in the occupancy ratio is between the threshold for reducing occupancy and releasing occupancy and the threshold for increasing occupancy and strengthening occupancy, a maintenance instruction is output to keep the current frequency of retrospective supervision unchanged.

[0044] When the change in occupancy is less than the threshold for reducing occupancy and releasing, a reduction and release command is output to cancel redundant monitoring tasks for the delisted water bodies and return the released retrospective monitoring resources to the resource scheduling pool.

[0045] The technical effects and advantages of this invention are as follows:

[0046] This invention constructs a harmonic average recurrence risk preliminary assessment mechanism based on water quality fluctuations and external disturbances, introduces social perception deviation and time-series decay function to implement dual dynamic correction of risk level, uses logarithmic ratio method to quantify the rationality of resource occupation and generates resource occupation planning benchmark through exponential scaling, realizes accurate dynamic redistribution of monitoring resources for delisted water bodies, effectively avoids the coexistence of high-risk water body leakage and low-risk water body over-occupation, and significantly improves the utilization efficiency and monitoring accuracy of regional remediation resources. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the implementation of the resource occupancy assessment method for wastewater treatment scenarios according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] This invention integrates water quality rebound tendency characteristics with a multi-dimensional risk correction mechanism, and combines logarithmic ratio resource occupancy assessment with exponential scaling planning output to achieve dynamic and precise reallocation of monitoring resources for delisted water bodies within the region, effectively improving the rationality of governance resource allocation and the accuracy of regulatory coverage.

[0050] Example 1, please refer to Figure 1 The specific operation process of the resource occupancy assessment method for wastewater treatment scenarios is as follows:

[0051] Step S1: Collect the fluctuation range of key indicators of the delisted water body and the fluctuation data of upstream water quality, comprehensively generate the water quality rebound tendency characteristics, read the change of sewage outlets directly discharging into the river, and generate the initial assessment level of recurrence risk based on the water quality rebound tendency characteristics.

[0052] Step S2: Retrieve the complaint response record after the case is closed, analyze the social perception deviation based on the complaint response record, generate a risk calibration level using the initial risk assessment level of recurrence and the social perception deviation, retrieve the running time after the case is closed, perform time-series decay correction on the risk calibration level based on the running time after the case is closed, and generate a corrected risk level.

[0053] Step S3: Collect the current review and inspection task execution coverage rate, plan the regulatory intensity benchmark for delisted water bodies based on the revised risk level, assess the rationality of resource occupation in combination with the review and inspection task execution coverage rate, and generate a resource occupation planning benchmark after replanning the review and inspection regulatory resources based on the rationality of resource occupation.

[0054] Step S4: Determine whether to reduce or increase the occupation of water bodies that have been delisted based on the resource occupation planning benchmark. Based on the determination result, choose to increase the frequency of follow-up supervision or cancel redundant supervision tasks.

[0055] The specific steps are as follows:

[0056] In step S1, a preliminary risk assessment of the combined risks of water quality rebound and external disturbances is conducted on the delisted water body within a single review cycle, and the preliminary risk assessment level of recurrence is output.

[0057] The delisted water bodies are those that have passed the acceptance inspection for black and odorous water body treatment and have been removed from the list of black and odorous water bodies and entered the continuous monitoring stage.

[0058] Looking back refers to the long-term supervision phase that takes place after the water bodies have been delisted, including water quality sampling, inspections, and public satisfaction reviews.

[0059] The review cycle refers to a single assessment window divided according to a preset time limit after the delisted water body enters the review phase.

[0060] By setting up water quality monitoring devices on the banks or sections of delisted water bodies, the ammonia nitrogen concentration of the delisted water bodies is sampled multiple times during the review period to obtain an ammonia nitrogen concentration sampling sequence. The mean and standard deviation of the ammonia nitrogen concentration sampling sequence are calculated, and the ratio of the standard deviation to the mean is taken as the sampling fluctuation range of the key indicator of the delisted water body.

[0061] A water quality monitoring device is a device that is deployed on the bank or cross-section of a delisted water body and is used to sample and test key water quality indicators at a predetermined frequency during a review period. In this example, it is used to collect the internal water quality fluctuation signal of the delisted water body.

[0062] By deploying online monitoring devices at the upstream section of the delisted water body, the ammonia nitrogen concentration of the upstream water is continuously monitored within the same review cycle to obtain a continuous monitoring sequence; the mean and standard deviation of the continuous monitoring sequence are calculated, and the ratio of the standard deviation to the mean is taken to obtain the upstream water quality fluctuation data;

[0063] The fluctuation range of key indicators of delisted water bodies and the fluctuation data of upstream water quality were standardized using the Max-min standardization method.

[0064] ;

[0065] in, The data includes the fluctuation range of key indicators for water bodies to be delisted, or the fluctuation data of upstream water quality. and These represent the minimum and maximum values ​​of the same parameter across all delisted water bodies within the region. This is the result after standardization of the corresponding parameters.

[0066] Based on the standardized results of the fluctuation amplitude of key indicators in the combined water bodies after delisting and the standardized results of the upstream water quality fluctuation data, the water quality rebound tendency characteristics were calculated using the geometric mean method.

[0067] ;

[0068] in, This is a characteristic of water quality rebound. The results are the standardized values ​​of the fluctuation range of key indicators in the sampling of water bodies that have been delisted. This is the result after standardizing the upstream water quality fluctuation data.

[0069] The larger the standardized result of the fluctuation range of key indicators of delisted water bodies, the greater the tendency of water quality rebound, indicating that the dispersion of water quality indicators of delisted water bodies is higher and the internal disturbance is more significant; the larger the standardized result of upstream water quality fluctuation data, the greater the tendency of water quality rebound, indicating that the upstream external disturbance is more significant; when any standardized result is close to zero, the overall tendency of water quality rebound is close to zero.

[0070] The water quality rebound tendency reflects the combined intensity level of internal and external water quality disturbances in the delisted water body;

[0071] By reading the changes in the sewage outlets into the river during the review period from the dynamic ledger database, the number of new sewage outlets that directly discharge into the delisted water bodies, the number of changes, the number of blocked sewage outlets, the number of replacements, and the total number of changes in direct sewage outlets into the river are calculated during the period.

[0072] The dynamic ledger database of sewage outlets is a structured data collection maintained by the water ecological environment authorities. It stores the spatial location, discharge nature, and change records of sewage outlets into rivers, indexed by water body numbers and sewage outlet numbers.

[0073] The change in the amount of sewage discharge outlets directly into rivers was standardized using the Max-min standardization method, and the standardized result of the change in the amount of sewage discharge outlets directly into rivers was obtained.

[0074] Based on the characteristics of water quality rebound tendency and combined with the standardized results of changes in direct discharge outlets into rivers, the preliminary recurrence risk index was calculated using the harmonic mean method.

[0075] ;

[0076] in, This is an initial index for assessing the risk of recurrence. This is a characteristic of water quality rebound. The results are the standardized figures for changes in the amount of sewage discharged directly into the river. To avoid extremely small constants with a denominator of zero.

[0077] The greater the tendency of water quality to rebound, the higher the initial assessment index of recurrence risk; the greater the standardized result of the change in the amount of sewage outlets directly discharging into rivers, the higher the initial assessment index of recurrence risk; when any dimension is extremely small, the initial assessment index of recurrence risk is significantly reduced by the corresponding smaller value.

[0078] Compare the initial recurrence risk assessment index with the upper and lower score thresholds:

[0079] When the initial recurrence risk index is greater than the upper score threshold, the initial recurrence risk level is output as severe.

[0080] When the initial recurrence risk index is between the lower and upper thresholds, the initial recurrence risk level is output as medium.

[0081] When the initial recurrence risk index is less than the lower threshold, the initial recurrence risk level is output as mild.

[0082] The upper and lower thresholds are set as follows: calculate the initial risk index of recurrence for all delisted water bodies in the region during the pilot period, sort all values ​​in ascending order, take the 25th quantile as the lower threshold, and take the 75th quantile as the upper threshold, so that the sample size distribution of the three levels is approximately balanced.

[0083] By generating and harmonizing the above-mentioned water quality rebound tendency characteristics, a preliminary risk assessment level reflecting the tendency of water quality recurrence in delisted water bodies is obtained, which is then passed to subsequent steps for social perception dimension correction and temporal decay correction.

[0084] In step S2, the complaint response records for the delisted water bodies within the post-delisting review period are retrieved from the citizen complaint and reporting database.

[0085] The citizen complaint and reporting database is a structured data collection maintained by the local citizen service hotline operator, which stores fields such as case filing time, closed-loop time, and handling conclusion according to the complaint item identifier.

[0086] Extract two sub-dimensional parameters from the complaint response records after account closure as follows:

[0087] During the review period, the number of complaints related to the delisted water bodies is taken as the ratio of the length of the shoreline of the delisted water bodies to obtain the complaint density, with the dimension being complaints per kilometer.

[0088] Within the same period window, the ratio of the number of closed-loop complaints related to delisted water bodies to the total number of complaints is used to obtain the response closure degree. Dimensionless ratio.

[0089] The complaint density and response closure degree were standardized using the Max-min standardization method to obtain the standardized results of complaint density and response closure degree.

[0090] Calculating social perception deviation using the geometric mean method:

[0091] ;

[0092] in, This refers to the degree of deviation in social perception. This is the result after standardizing the complaint density. In response to the results after the closure degree is standardized, That is, the results after the response closure degree is standardized are taken as complements, and the direction is unified so that "the larger the value, the greater the deviation" before entering the fusion.

[0093] The higher the standardized result of complaint density, the greater the deviation in social perception, indicating that the public complaints are more concentrated on the unit shoreline length of the delisted water body; the lower the standardized result of response closure degree, the greater the deviation in social perception, indicating that the closure level of complaint handling is lower; when any dimension is close to zero (i.e. no complaints or complete closure), the overall deviation in social perception is close to zero.

[0094] The initial relapse risk level was numerically adjusted and its deviation from social perception was corrected using a Sigmoid function to obtain the risk calibration level.

[0095] ;

[0096] ;

[0097] in, The initial risk level of recurrence is quantified (1 for mild, 2 for moderate, and 3 for severe). This refers to the degree of deviation in social perception. This represents the median calibration value for the degree of social perception deviation. To adjust the preset slope parameter for sensitivity, The results of the risk calibration level are quantified. The function truncates the values ​​to the integer range of 1 to 3.

[0098] The numerical results of the risk calibration level are mapped back to the three levels of slight, moderate and severe according to 1, 2 and 3 respectively to obtain the risk calibration level.

[0099] When the social perception deviation is greater than the median calibration value, the Sigmoid function output is greater than 0.5. The adjustment amount obtained by "subtracting 0.5 and multiplying by 2" is rounded positively, and the risk calibration level is upgraded to a higher level based on the initial relapse risk assessment level. When the social perception deviation is equal to the median calibration value, the adjustment amount is zero, and the risk calibration level is consistent with the initial relapse risk assessment level. When the social perception deviation is less than the median calibration value, the adjustment amount is rounded negatively, and the risk calibration level is downgraded to a lower level based on the initial relapse risk assessment level. The function also ensures that the upgrade result does not exceed the boundary between the lowest and highest grades.

[0100] The median calibration value of the social perception deviation is set as follows: on the historical review periodic data of all delisted water bodies in the region, the median of all values ​​is calculated periodically according to the social perception deviation formula mentioned above; the preset slope parameter for adjusting the sensitivity is obtained by fitting historical samples, so that the upgrading boundary of the Sigmoid function is aligned with the "significant deviation" boundary in actual management experience.

[0101] The deregistration acceptance date of the deregistered water body is retrieved from the deregistration archive database, and the difference between this date and the current time is used to obtain the running time after deregistration approval, in days.

[0102] The deregistration archive database is a structured data collection maintained by the water ecological environment authorities, which stores the deregistration acceptance date, key indicator sampling and re-evaluation records, and technical archives of deregistered water bodies according to their numbers.

[0103] The runtime after the account cancellation is approved is processed using the Max-min standardization method to obtain the standardized runtime after the account cancellation is approved.

[0104] After the cancellation is approved, the standardized runtime result is used to perform time-series decay correction on the numerical result of the risk calibration level using an exponential decay function:

[0105] ;

[0106] ;

[0107] in, This is the result after standardizing the runtime following the account cancellation approval. To stabilize the observation period, a standardized threshold was established. This refers to the time-series decay rate. This is the time-series decay coefficient. The results of the risk calibration level are quantified. To correct the numerical results of the risk level, The function guarantees that the corrected risk level value will not be lower than 1, thus preventing it from decaying to below "slight".

[0108] The revised risk level numerical results are mapped back to the three levels of mild, moderate, and severe as 1, 2, and 3 respectively, to obtain the revised risk level.

[0109] The results of the standardized runtime after the account cancellation were compared with the standardized threshold of the stable observation period:

[0110] When the standardized result of the running time after the cancellation is less than the standardized threshold of the stable observation period, the time decay coefficient is 1, and the corrected risk level is consistent with the risk calibration level.

[0111] When the standardized result of the running time after the cancellation is not less than the standardized threshold of the stable observation period, the time decay coefficient is less than 1, and the corrected risk level decreases based on the risk calibration level.

[0112] It should be explained that if the deregistration archive database has recorded abnormal events in the sampling and re-evaluation of key indicators during the review period, the time-series decay coefficient will be forcibly set to 1, and decay will not be performed to avoid premature downgrading of deregistered water bodies that show signs of recurrence; otherwise, the time-series decay coefficient will be calculated normally according to the above exponential decay function.

[0113] The standardization threshold for the stable observation period is set as follows: For historical recurrence statistics of all delisted water bodies in the region, construct a curve of the cumulative recurrence probability versus the standardized running time after delisting, and take the standardized time corresponding to the first time the cumulative recurrence probability falls below the set low probability level as the standardization threshold for the stable observation period; the time decay rate is obtained by fitting historical samples using the maximum likelihood method.

[0114] In step S3, the patrol execution records of the delisted water bodies within the follow-up review cycle are read from the patrol task management database. The ratio of the length of the river segment that has been patrolled to the total length of the delisted water body is used to obtain the follow-up review patrol task execution coverage rate. Dimensionless ratio.

[0115] The patrol task management database refers to a structured data set maintained by the river chief system office of the jurisdiction to which the water body is delisted, storing patrol task assignment records, patrol execution records, and patrol section lengths according to the water body number.

[0116] The regulatory intensity benchmark is generated based on the revised risk level using a segmented mapping method. The specific rules are as follows:

[0117] When the revised risk level is minor, the regulatory intensity benchmark will be set to a low value.

[0118] When the revised risk level is medium, the regulatory intensity benchmark will be set to the median calibration value.

[0119] When the revised risk level is severe, the regulatory intensity benchmark will be set to a high value.

[0120] The low, median, and high calibration values ​​are set as follows: the historical review resource input data of the region is grouped according to the corresponding corrected risk level, and the median of the review inspection task execution coverage rate of all delisted water bodies in the group is taken as the corresponding calibration value, reflecting the typical coverage level of historical regulatory resource input in the region under that level.

[0121] The logarithmic ratio method is used to assess the deviation between the coverage rate of the follow-up inspection tasks and the regulatory intensity benchmark:

[0122] ;

[0123] in, To ensure reasonable resource utilization, To review the coverage of patrol missions, As a benchmark for regulatory intensity, To avoid the minimal constant of logarithmic divergence.

[0124] When the coverage rate of the retrospective inspection task execution is higher than the regulatory intensity benchmark, the resource utilization rationality is positive, and the larger the value, the more the actual utilization exceeds the target benchmark; when the coverage rate of the retrospective inspection task execution is lower than the regulatory intensity benchmark, the resource utilization rationality is negative, and the smaller the value, the less the actual utilization is; when the two are close, the resource utilization rationality is close to zero, indicating that the actual utilization matches the target benchmark.

[0125] Resource utilization planning benchmarks are generated by correcting deviations from regulatory intensity benchmarks using exponential scaling.

[0126] ;

[0127] in, As a benchmark for resource utilization planning, As a benchmark for regulatory intensity, To ensure reasonable resource utilization, The preset scaling factor for adjusting sensitivity has a range of values. Interval.

[0128] When the rationality of resource utilization is zero, the index term is 1, and the resource utilization planning benchmark is equal to the regulatory intensity benchmark; when the rationality of resource utilization is greater than zero, the index term is less than 1, and the resource utilization planning benchmark is less than the regulatory intensity benchmark, corresponding to reduction; when the rationality of resource utilization is less than zero, the index term is greater than 1, and the resource utilization planning benchmark is greater than the regulatory intensity benchmark, corresponding to reinforcement.

[0129] The preset scaling factor for adjusting sensitivity is set as follows: on the regional calibration sample, regression fitting is performed between the resource occupancy planning benchmark and the actual investment in the next cycle, and the scaling factor value with the smallest fitting error is selected.

[0130] Through the above logarithmic ratio evaluation and exponential scaling correction, a resource occupancy planning benchmark reflecting the target regulatory resource level of the delisted water body in the next cycle is obtained, and then passed into subsequent steps for dispatching instruction output.

[0131] In step S4, an occupation adjustment instruction is output and sent to the implementing department based on the relative relationship between the resource occupation planning benchmark and the regulatory intensity benchmark;

[0132] The regulatory intensity benchmark generated in the current cycle for the delisted water bodies is retrieved, and the ratio of the resource occupation planning benchmark to the regulatory intensity benchmark is taken to obtain the occupation change ratio.

[0133] Compare the percentage change in occupancy with the thresholds for determining increases in occupancy and reductions in occupancy:

[0134] When the change in occupancy ratio exceeds the threshold for reinforcement, an reinforcement instruction is output, the frequency of follow-up supervision is increased for the delisted water body, and recurrence risk tracking is initiated.

[0135] When the change in the occupancy ratio is between the threshold for reducing occupancy and releasing occupancy and the threshold for increasing occupancy and strengthening occupancy, a maintenance instruction is output to keep the current frequency of retrospective supervision unchanged.

[0136] When the change in occupancy is less than the threshold for reducing occupancy and releasing, a reduction and release command is output to cancel redundant monitoring tasks for the delisted water bodies and return the released retrospective monitoring resources to the resource scheduling pool.

[0137] The resource scheduling pool is a virtual pool that stores reallocatable resources that have been written off from the reduced-occupancy release objects and are subject to retrospective supervision. Resources in the resource scheduling pool are preferentially allocated to the increased-occupancy reinforcement objects in the next cycle.

[0138] The thresholds for increasing and reducing occupancy are set as follows: the resource input adjustment records of historical samples in the region are reviewed and organized in the form of ratios. For samples that have actually implemented upward adjustments, the median of the occupancy change ratio is taken as the threshold for increasing occupancy (value greater than 1). For samples that have actually implemented downward adjustments, the median of the occupancy change ratio is taken as the threshold for reducing and releasing occupancy (value less than 1). This reflects the minimum effective adjustment range that is meaningful in engineering.

[0139] Steps S1 to S4 are executed one by one for all delisted water bodies in the region, and the corresponding occupancy adjustment instructions for each delisted water body are output and sent to the relevant implementing departments to realize the dynamic redistribution of regulatory resources in the region.

[0140] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0141] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0142] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0143] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the resource occupancy of wastewater treatment, characterized by: Includes the following steps: Step S1: Collect the fluctuation range of key indicators of the delisted water body and the fluctuation data of upstream water quality, comprehensively generate the water quality rebound tendency characteristics, read the change of sewage outlets directly discharging into the river, and generate the initial assessment level of recurrence risk based on the water quality rebound tendency characteristics. Step S2: Retrieve the complaint response record after the case is closed, analyze the social perception deviation based on the complaint response record, generate a risk calibration level using the initial risk assessment level of recurrence and the social perception deviation, retrieve the running time after the case is closed, perform time-series decay correction on the risk calibration level based on the running time after the case is closed, and generate a corrected risk level. Step S3: Collect the current review and inspection task execution coverage rate, plan the regulatory intensity benchmark for delisted water bodies based on the revised risk level, assess the rationality of resource occupation in combination with the review and inspection task execution coverage rate, and generate a resource occupation planning benchmark after replanning the review and inspection regulatory resources based on the rationality of resource occupation. Step S4: Determine whether to reduce or increase the occupation of water bodies that have been delisted based on the resource occupation planning benchmark. Based on the determination result, choose to increase the frequency of follow-up supervision or cancel redundant supervision tasks.

2. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 1, characterized in that: In step S1, the delisted water bodies are those that have passed the acceptance inspection for black and odorous water body treatment, have been removed from the list of black and odorous water bodies, and have entered the continuous monitoring stage. The ammonia nitrogen concentration of the delisted water body was sampled multiple times during the review period using water quality monitoring devices to obtain an ammonia nitrogen concentration sampling sequence. The mean and standard deviation of the ammonia nitrogen concentration sampling sequence were calculated, and the ratio of the standard deviation to the mean was taken as the fluctuation range of the key indicator sampling of the delisted water body. The ammonia nitrogen concentration of the upstream water was continuously monitored within the same review period to obtain a continuous monitoring sequence; the mean and standard deviation of the continuous monitoring sequence were calculated, and the ratio of the standard deviation to the mean was used to obtain the water quality fluctuation data of the upstream water. The fluctuation range of key indicators of delisted water bodies and the fluctuation data of upstream water quality were standardized using the Max-min standardization method. The water quality rebound tendency characteristics were calculated by combining the standardized results.

3. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 1, characterized in that: In step S1, the changes in the sewage outlets into the river during the review period are read through the dynamic ledger database of sewage outlets. The number of new, changed and blocked sewage outlets that directly discharge into the delisted water bodies are counted during the review period. The result of the sum of the numbers is used as the change amount of direct sewage outlets into the river. The initial risk index of recurrence was calculated by combining the water quality rebound tendency characteristics with the standardized results of changes in the amount of sewage outlets directly discharging into rivers using the harmonic mean method.

4. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 3, characterized in that: In step S1, the initial recurrence risk assessment index is compared with the upper and lower tier thresholds: When the initial recurrence risk index is greater than the upper score threshold, the initial recurrence risk level is output as severe. When the initial recurrence risk index is between the lower and upper thresholds, the initial recurrence risk level is output as medium. When the initial recurrence risk index is less than the lower threshold, the initial recurrence risk level is output as mild.

5. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 1, characterized in that: In step S2, read the post-cancellation complaint response records of the cancelled water bodies within the post-cancellation review period; Extract two sub-dimensional parameters from the complaint response records after account closure as follows: The complaint density is obtained by taking the ratio of the number of complaints related to the delisted water bodies to the length of the shoreline of the delisted water bodies; The response closure degree is obtained by taking the ratio of the number of closed-loop complaints related to the delisted water bodies to the total number of complaints. The complaint density and response closure degree were standardized using the Max-min standardization method to obtain the standardized results of the complaint density and response closure degree. The social perception deviation was calculated by combining the standardized results using the geometric mean method.

6. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 4, characterized in that: In step S2, a numerical result is set for the initial relapse risk level. The deviation between the numerical result of the initial relapse risk level and the social perception is smoothed and corrected by the Sigmoid function to obtain the risk calibration level. Among them, the numerical results of the risk calibration level are mapped back to the three levels of mild, moderate and severe according to 1, 2 and 3 respectively; The time elapsed after the cancellation of the water body is obtained by retrieving the cancellation acceptance date from the cancellation archive database and taking the difference between the date and the current time. After the cancellation is approved, the runtime is corrected by performing a time-series decay correction on the numerical result of the risk calibration level using an exponential decay function, resulting in the corrected numerical result of the risk level. The revised risk level numerical results are mapped back to the three levels of mild, moderate, and severe as 1, 2, and 3 respectively, to obtain the revised risk level.

7. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 6, characterized in that: In step S3, the patrol execution records of the delisted water bodies during the review cycle are read from the patrol task management database. The ratio of the length of the river section that has been patrolled to the total length of the delisted water body is taken to obtain the review patrol task execution coverage rate. The regulatory intensity benchmark is generated based on the revised risk level using a segmented mapping method. The specific rules are as follows: When the revised risk level is minor, the regulatory intensity benchmark will be set to a low value. When the revised risk level is medium, the regulatory intensity benchmark will be set to the median calibration value. When the revised risk level is severe, the regulatory intensity benchmark will be set to a high value. The low-level, median, and high-level calibration values ​​are set as follows: the historical review data of resource input in the region are grouped according to the corresponding corrected risk level, and the median of the review and inspection coverage rate of the delisted water bodies is taken as the corresponding calibration value.

8. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 7, characterized in that: In step S3, the deviation between the coverage rate of the follow-up inspection task and the regulatory intensity benchmark is evaluated using the logarithmic ratio method to obtain the rationality of resource utilization. The rationality of resource utilization is adjusted by the deviation of the regulatory intensity benchmark through the exponential scaling method to generate the resource utilization planning benchmark.

9. The method for assessing the resource occupancy of wastewater treatment scenarios according to claim 8, characterized in that: In step S4, the regulatory intensity benchmark generated in the current cycle for the delisted water body is retrieved, and the ratio between the resource occupation planning benchmark and the regulatory intensity benchmark is taken to obtain the occupation change ratio. Compare the percentage change in occupancy with the thresholds for determining increases in occupancy and reductions in occupancy: When the change in occupancy ratio exceeds the threshold for reinforcement, an reinforcement instruction is output, the frequency of follow-up supervision is increased for the delisted water body, and recurrence risk tracking is initiated. When the change in the occupancy ratio is between the threshold for reducing occupancy and releasing occupancy and the threshold for increasing occupancy and strengthening occupancy, a maintenance instruction is output to keep the current frequency of retrospective supervision unchanged. When the change in occupancy is less than the threshold for reducing occupancy and releasing, a reduction and release command is output to cancel redundant monitoring tasks for the delisted water bodies and return the released retrospective monitoring resources to the resource scheduling pool.

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