A self-circulation sedimentation and reuse system for flushing wastewater

Abstract

The present application relates to sewage treatment technical field, especially to a kind of flushing wastewater self-circulation precipitation recycling system, the system includes wastewater collection module, to collect wastewater;Multi-stage cascade control sedimentation module, to separate impurities in wastewater;Data acquisition module, to collect the water volume dimension data and impurity dimension data of wastewater in the outlet of water collecting tank and sedimentation tank;Data analysis module, to determine wastewater type based on the average wastewater flow at several detection points of water collecting tank outlet within a preset time length and the weight of grid intercepts within a preset time length;Sludge detection module, to determine the distribution type of sludge detection point based on the wastewater type and / or grid intercepts particle size distribution type;Intelligent dredging module, to determine sludge grabbing path planning mode based on sludge density gradient difference and sludge fluctuation coefficient;The present application improves the accuracy of wastewater type analysis to optimize sludge grabbing process and improve wastewater treatment efficiency.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a self-circulating sedimentation and reuse system for flushing wastewater. Background Technology

[0002] During vehicle washing, a large amount of wastewater containing impurities such as mud, tire debris, oil, and colloidal particles is generated. Therefore, the recycling and reuse of vehicle washing wastewater has become an industry necessity. An efficient and stable wastewater treatment system is the core support for achieving reuse. However, the traditional sedimentation treatment mode is inefficient. Existing vehicle washing wastewater treatment mostly adopts a crude mode of simple filtration in a single-stage sedimentation tank, without optimization for the dynamic changes in wastewater impurity load. At the same time, existing sedimentation tanks lack precise monitoring of the sludge stratification state, and sludge removal operations mostly rely on manual experience and are carried out periodically. This often results in problems such as the bottom dense sludge not being cleaned and the surface loose sludge being over-grabbed, causing the effective volume of the sedimentation tank to gradually decrease, which seriously affects the continuous operation of the system.

[0003] Chinese Patent Application No. CN118161891A discloses a backwash wastewater treatment system for a water plant, comprising: a sedimentation tank, a sludge conveying mechanism, a sludge pushing mechanism, and a thrust supplementing mechanism. This invention, when treating sludge in the sedimentation tank, utilizes a sludge pushing mechanism in conjunction with multiple sludge discharge outlets and a sludge conveying mechanism. This allows the sludge to be first propelled towards these outlets, and then rapidly transported and discharged by the sludge conveying mechanism, significantly improving sludge discharge efficiency. The sludge pushing mechanism, in conjunction with multiple sedimentation guide strips, provides sufficient propulsion to the sludge in the sedimentation tank, enhancing the comprehensiveness of sludge treatment by concentrating its movement towards the discharge outlets.

[0004] However, existing technologies still have the following problems:

[0005] Existing sedimentation treatment technologies mostly focus on improving the sludge discharge efficiency of a single sedimentation tank, without designing a multi-stage synergistic treatment mechanism to address the characteristics of vehicle washing wastewater, such as large fluctuations in impurity load and complex particle composition, resulting in low wastewater treatment efficiency. Summary of the Invention

[0006] To address this issue, the present invention provides a self-circulating sedimentation and reuse system for vehicle washing wastewater, which overcomes the problem that existing technologies often focus on improving sludge discharge efficiency in a single sedimentation tank and do not design a multi-stage synergistic treatment mechanism to address the characteristics of large fluctuations in impurity load and complex particle composition in vehicle washing wastewater, resulting in low wastewater treatment efficiency.

[0007] To achieve the above objectives, the present invention provides a self-circulating sedimentation and reuse system for flushing wastewater. It includes:

[0008] The wastewater collection module, located in the vehicle washing area and on both sides of the vehicle driving road, includes a water collection tank and a screen for collecting wastewater;

[0009] A multi-stage sedimentation module, which is connected to the wastewater collection module, includes a primary sedimentation tank, a secondary sedimentation tank and a tertiary sedimentation tank connected in sequence, for removing and separating impurities from the wastewater;

[0010] The data acquisition module is connected to the wastewater collection module and the multi-level control sedimentation module, and is used to collect water volume data and impurity data of the wastewater at the outlet of the water collection tank and in the sedimentation tank.

[0011] The data analysis module, which is connected to the data acquisition module, is used to calculate the wastewater impurity load tendency value based on the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the weight of the screen intercepted within a preset time period, and to determine the wastewater type based on the wastewater impurity load tendency value.

[0012] A sludge detection module, which is connected to the data analysis module, is used to determine the distribution type of sludge detection points based on the wastewater type and / or the particle size distribution type of the screen residue.

[0013] The intelligent dredging module is connected to the sludge detection module and the multi-level joint control sedimentation module respectively, and is used to determine the sludge grabbing path planning method based on the sludge density gradient difference and sludge fluctuation coefficient.

[0014] A composite filtration module, which is connected to the multi-stage controlled sedimentation module, includes a pretreatment filtration unit, a fine filtration unit, and an auxiliary function unit, for removing impurities that are not completely separated by the multi-stage controlled sedimentation module;

[0015] The water resource reuse module is connected to the composite filtration module and is used to deliver the filtered clean water to the flushing system via a booster pump. The flushed wastewater is then reintroduced into the primary sedimentation tank.

[0016] Furthermore, the data analysis module calculates the wastewater impurity load tendency value based on the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the weight of the material trapped by the screen within a preset time period. The wastewater impurity load tendency value is the ratio of the weight of the material trapped by the screen within a preset time period to the product of the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the preset time period.

[0017] Furthermore, the data analysis module determines the wastewater type based on the comparison between the wastewater impurity load tendency value and the preset wastewater impurity load tendency value, wherein,

[0018] If the wastewater impurity load tendency value is greater than or equal to the preset wastewater impurity load tendency value, the data analysis module determines that the wastewater type is a high-load wastewater type.

[0019] If the wastewater impurity load tendency value is less than the preset wastewater impurity load tendency value, the data analysis module determines that the wastewater type is a low-load wastewater type.

[0020] Furthermore, the sludge detection module determines the distribution type of sludge detection points based on the wastewater type and / or the particle size distribution type of the screen residue, wherein,

[0021] If the wastewater type is high-load wastewater or the particle size distribution of the screen residue is predominantly coarse particles, the sludge detection module determines the distribution type of the sludge detection points to be a dense gradient stratified distribution type.

[0022] If the wastewater type is low-load wastewater and the particle size distribution of the screen residue is a mixed distribution of multiple particles, the sludge detection module determines that the distribution type of the sludge detection points is a balanced gradient stratified distribution.

[0023] Furthermore, the data analysis module is also used to determine the particle size distribution type of the screen-retained material based on the particle size data of the screen-retained material, wherein,

[0024] Obtain particle size data of the material trapped by the grid within a preset time period, and calculate the cumulative weight percentage of the trapped material with a particle size larger than the preset particle size;

[0025] If the cumulative weight percentage of the retained material with a particle size larger than the preset particle size is greater than the preset percentage, the data analysis module determines that the particle size distribution type of the grid retained material is a coarse particle-dominated distribution type.

[0026] Furthermore, the encrypted gradient layered distribution type arranges silt detection points based on the principle of associating the density of detection points with the silt density gradient change value; the balanced gradient layered distribution type arranges silt detection points based on the principle of equally spaced coverage of the full thickness of the silt layer.

[0027] Furthermore, the intelligent dredging module determines the dredging path planning method based on the dredging density gradient difference and the dredging fluctuation coefficient, wherein,

[0028] The intelligent dredging module calculates the silt density gradient difference based on the silt density at adjacent detection points, and calculates the silt fluctuation coefficient based on the silt density data at the same detection point within a preset time period.

[0029] If any silt density gradient difference is greater than a preset gradient difference threshold, the intelligent dredging module determines to adopt a partitioned grasping path planning method based on density gradient difference.

[0030] If all silt density gradient differences are less than or equal to a preset gradient difference threshold, and any silt fluctuation coefficient is greater than a preset fluctuation coefficient threshold, then the intelligent dredging module determines to adopt a dynamic adjustment grasping path planning method based on the fluctuation coefficient.

[0031] If all silt density gradient differences are less than or equal to a preset gradient difference threshold, and all silt fluctuation coefficients are less than or equal to a preset fluctuation coefficient threshold, then the intelligent dredging module determines to adopt a preset standard grasping path planning method.

[0032] Furthermore, the partitioned crawling path planning method based on density gradient difference includes:

[0033] Regions where the density gradient difference of silt is greater than a preset gradient difference threshold are identified as high density difference regions.

[0034] An independent grabbing sub-path is assigned to the high density difference area, and the silt in the area is grabbed first.

[0035] Assign regular crawling sub-paths to areas with gradual density changes.

[0036] Furthermore, the dynamic adjustment capture path planning method based on the fluctuation coefficient includes:

[0037] The area where the detection point has a sludge fluctuation coefficient greater than a preset fluctuation coefficient threshold is identified as a high fluctuation area;

[0038] A dynamically adjusted grabbing sub-path is assigned to the high-fluctuation region, and the grabbing frequency and / or grabbing depth of the sub-path are dynamically adjusted based on real-time monitored silt density data.

[0039] Furthermore, the preset standard crawling path planning method includes:

[0040] Control the dredging equipment to traverse the bottom of the sedimentation tank along a preset path;

[0041] The lowering depth of the grab bucket of the dredging equipment is set based on the average thickness of the silt layer obtained by the silt detection module.

[0042] Compared with existing technologies, the advantages of this invention are as follows: This invention dynamically adjusts the number of detection points according to the width of the collection tank and strictly controls the adjacent spacing to be less than or equal to 2m, ensuring coverage of the entire outlet cross-section and avoiding biased flow data due to local detection omissions; simultaneously, the detection points are set in the central area at a distance of greater than or equal to 0.3m from the tank wall, avoiding the vortex zone of the tank wall, reducing the interference of water flow disturbance on flow detection from the source; the 3σ criterion is used to remove outliers from instantaneous flow data, effectively filtering the influence of accidental factors such as water flow impact on the data; subsequently, the arithmetic mean method is used to calculate the average flow rate of a single detection point and the comprehensive average flow rate, and based on the characteristic of the equally spaced distribution of detection points, equal weights are assigned, further ensuring the objectivity of the average flow rate calculation results and providing accurate flow basis data for impurity load tendency values; without accurate classification, treating high-load wastewater as low-load wastewater easily leads to substandard effluent, and treating low-load wastewater as high-load wastewater easily leads to disordered system operating parameters. This solution, through accurate classification, ensures that the treatment parameters match the wastewater load, reducing system operation fluctuations.

[0043] Furthermore, for high-load wastewater or wastewater dominated by coarse particles, this invention employs a dense gradient stratified distribution. By increasing the density of detection points and refining the stratification, it can more accurately capture the distribution differences of sludge at different depths under high impurity loads, avoiding sludge accumulation assessment bias caused by insufficient detection. For low-load wastewater with mixed particles, a balanced gradient stratified distribution is adopted, which reduces redundant detection points while ensuring basic detection accuracy, lowering system operating costs and achieving optimal resource allocation. By dynamically adjusting the detection point distribution through dual determination of wastewater type and particle size distribution type, this invention avoids resource waste caused by high-density detection for low-load wastewater and also prevents treatment risks caused by insufficient detection of high-load wastewater.

[0044] Furthermore, this invention, based on the principle of correlating detection point density with density gradient changes, densifies the detection points in areas of significant density change. This allows for precise capture of subtle changes in silt layer interfaces and depositional characteristics, avoiding the loss of gradient change information due to sparse detection points. The invention also employs differentiated detection point distribution for different regions of the silt layer, focusing on key changes in high-density and transitional zones while appropriately reducing detection points in low-density areas. Quantitative guidance based on density gradient changes helps establish a dynamic correlation between detection point distribution and actual silt characteristic changes, ensuring the data accurately reflects the physical process of silt deposition. Furthermore, based on the principle of equal spacing covering the entire thickness, points are evenly distributed along the vertical direction of the silt layer, comprehensively reflecting the overall distribution characteristics of the silt layer formed by low-load, multi-particle mixed wastewater, avoiding overall assessment bias caused by missing local data. Finally, the invention dynamically adjusts the number of detection points according to the maximum thickness of the silt layer, ensuring complete coverage while avoiding over-detection, thus balancing data comprehensiveness and detection costs.

[0045] Furthermore, this invention employs a zoned grabbing strategy based on density gradient differences, which can accurately identify the stratification of silt layers. For high-density areas, low-speed deep digging is used to ensure that compacted silt is fully removed; for low-density areas, rapid shallow grabbing is used to reduce the diffusion and pollution of suspended solids. When the silt fluctuation coefficient exceeds a threshold, dynamic path planning is used to avoid areas with severe fluctuations and to adjust parameters in a coordinated manner, thus preventing the grab bucket from emptying or overloading due to sudden density changes, adapting to the dynamic characteristics of the silt layer. For uniform and stable silt layers, a preset standard path is used, operating with fixed parameters, ensuring dredging quality while improving operational efficiency and reducing [the risk of silt buildup]. Unnecessary parameter adjustment time was avoided; the zoned grabbing strategy ensured thorough cleaning of high-density areas and reduced unnecessary deep digging in low-density areas, preventing energy waste; the dynamic adjustment mode reduced the probability of grab bucket idling or jamming by avoiding fluctuating areas; the standardized path achieved efficient full coverage under stable operating conditions, significantly reducing equipment energy consumption; the grab bucket closing speed was reduced for high-density sludge, mitigating mechanical impact; the sinking depth was adjusted for fluctuating areas, reducing the probability of hard contact between the grab bucket and the pool bottom, extending equipment lifespan; the dynamic adjustment mechanism avoided damage to the transmission system caused by sudden load changes, reducing equipment failure rate and maintenance frequency.

[0046] Furthermore, this invention accurately identifies areas with density gradient differences exceeding a threshold, precisely pinpointing sludge stratification interfaces and localized accumulation zones. This ensures that these critical areas, often overlooked by traditional dredging methods, receive priority treatment. Independent grabbing sub-paths and dedicated parameters are configured for high-density-difference areas to specifically address the difficulty in removing compacted sludge. Prioritizing dredging operations in these areas avoids increased processing difficulty due to continuous sludge accumulation, reducing subsequent rework costs. The seamless integration of independent sub-paths with conventional paths ensures both adequate treatment of abnormal areas and overall coverage of the pool bottom, avoiding the contradiction between localized over-treatment and global omission. Differentiated operations are employed based on the physical characteristics of high-density-difference areas, minimizing over-treatment during dredging. The process of sludge disturbance and suspended solids diffusion ensures stable effluent quality from the sedimentation tank. High-fluctuation areas are accurately identified using the K-nearest neighbor algorithm or radius delineation, and the grabbing frequency and depth are dynamically adjusted based on real-time density monitoring data, ensuring the dredging strategy remains synchronized with the sludge condition. High-fluctuation areas are given high priority, allowing for the suspension of routine operations to prioritize the treatment of risky areas and prevent excessive sludge accumulation. Simultaneously, an avoidance strategy is adopted for areas with extremely low density and drastic fluctuations to prevent ineffective grabbing and secondary disturbance. An S-shaped or bow-shaped path designed based on the tank's geometry ensures complete coverage of the tank bottom without blind spots, significantly improving dredging efficiency. The lowering depth is calculated based on the average sludge thickness, combined with a safety margin, ensuring sufficient grabbing while avoiding damage caused by the grab bucket touching the tank bottom. Attached Figure Description

[0047] Figure 1This is a schematic diagram of the self-circulating sedimentation and reuse system for flushing wastewater according to the present invention;

[0048] Figure 2 This is a flowchart illustrating the workflow of the data analysis module in the self-circulating sedimentation and reuse system for flushing wastewater of the present invention.

[0049] Figure 3 This is a flowchart illustrating the process of the sludge detection module in the self-circulating sedimentation and reuse system for flushing wastewater of the present invention. Detailed Implementation

[0050] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0051] It should be noted that the data in this embodiment are all derived from a comprehensive analysis and evaluation of historical data from the six months prior to this determination and the corresponding historical determination results by the system described in this invention. Those skilled in the art will understand that the system described in this invention can determine the above-mentioned parameters for a single item by selecting the value with the highest proportion based on the data distribution as the preset standard parameter, using weighted summation to obtain the value as the preset standard parameter, substituting each historical data point into a specific formula and using the value obtained by that formula as the preset standard parameter, or other selection methods, as long as the system described in this invention can clearly define different specific situations in the single-item determination process through the obtained values.

[0052] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0054] Please see Figures 1-3 As shown, Figure 1 This is a schematic diagram of the self-circulating sedimentation and reuse system for flushing wastewater according to the present invention; Figure 2 This is a flowchart illustrating the workflow of the data analysis module in the self-circulating sedimentation and reuse system for flushing wastewater of the present invention. Figure 3This is a flowchart illustrating the process of the sludge detection module in the self-circulating sedimentation and reuse system for flushing wastewater of the present invention.

[0055] The flushing wastewater self-circulation sedimentation and reuse system provided in this embodiment includes:

[0056] The wastewater collection module, located in the vehicle washing area and on both sides of the vehicle driving road, includes a water collection tank and a screen for collecting wastewater;

[0057] A multi-stage sedimentation module, which is connected to the wastewater collection module, includes a primary sedimentation tank, a secondary sedimentation tank and a tertiary sedimentation tank connected in sequence, for removing and separating impurities from the wastewater;

[0058] The data acquisition module is connected to the wastewater collection module and the multi-level control sedimentation module, and is used to collect water volume data and impurity data of the wastewater at the outlet of the water collection tank and in the sedimentation tank.

[0059] The data analysis module, which is connected to the data acquisition module, is used to calculate the wastewater impurity load tendency value based on the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the weight of the screen intercepted within a preset time period, and to determine the wastewater type based on the wastewater impurity load tendency value.

[0060] A sludge detection module, which is connected to the data analysis module, is used to determine the distribution type of sludge detection points based on the wastewater type and / or the particle size distribution type of the screen residue.

[0061] The intelligent dredging module is connected to the sludge detection module and the multi-level joint control sedimentation module respectively, and is used to determine the sludge grabbing path planning method based on the sludge density gradient difference and sludge fluctuation coefficient.

[0062] A composite filtration module, which is connected to the multi-stage controlled sedimentation module, includes a pretreatment filtration unit, a fine filtration unit, and an auxiliary function unit, for removing impurities that are not completely separated by the multi-stage controlled sedimentation module;

[0063] The water resource reuse module is connected to the composite filtration module and is used to deliver the filtered clean water to the flushing system via a booster pump. The flushed wastewater is then reintroduced into the primary sedimentation tank.

[0064] The water volume data in this embodiment includes, but is not limited to, "real-time wastewater flow rate at each detection point at the outlet of the collection tank, influent flow rate of the first-stage sedimentation tank, influent flow rate of the second-stage sedimentation tank, and influent flow rate of the third-stage sedimentation tank in the multi-stage controlled sedimentation module"; the impurity data includes, but is not limited to, "total weight of the material trapped by the screen within a preset time, wet weight of the material trapped by the screen, dry weight of the material trapped by the screen, and weight of different particle size components in the material trapped by the screen"; the preset time can be set to 1 hour, which can be dynamically adjusted by real-time monitoring of the flow rate at the outlet of the collection tank. If the flow rate change rate exceeds a threshold (e.g., ±20%) within a continuous period of time (e.g., 15 minutes), the preset time will be automatically switched to a shorter mode (e.g., 30 minutes) to quickly respond to changes. When the flow rate returns to a stable state, it will be switched back to the normal mode; the sludge detection module includes, but is not limited to, "ultrasonic density sensor, adjustable mounting bracket, and laser level sensor"; the intelligent sludge removal module includes, but is not limited to, "gantry crane main beam, variable frequency travel motor, track positioning component, ... The system includes: an electric hoist, a four-rope grab bucket, and a weight sensor; the pretreatment filtration unit includes, but is not limited to, a switchable grid filter assembly (using multi-layer stainless steel filter screens, equipped with several different pore sizes, which can be automatically switched via an electric valve) and a filter support frame and sealing structure (the support frame is made of aluminum alloy and connected to the filter unit housing via a flange; the seals are made of oil-resistant rubber to ensure no wastewater leakage); the fine filtration unit includes, but is not limited to, a gradient precision filter cartridge assembly (using a modular filter cartridge design, equipped with several composite filter cartridges of different precision to adapt to different water quality scenarios, which can be automatically switched via an electric translation mechanism) and a filter cartridge support and sealing assembly (the support frame is made of 304 stainless steel, each filter cartridge is independently installed in a sealed chamber, and the chamber and frame are sealed with O-rings); the auxiliary function unit includes, but is not limited to, a pH sensor, an ORP sensor, and a backwash water collection tank; the flushing system includes, but is not limited to, a tire washing system and a road sprinkler system.

[0065] Specifically, the data analysis module calculates the wastewater impurity load tendency value based on the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the weight of the material trapped by the screen within a preset time period. The wastewater impurity load tendency value is the ratio of the weight of the material trapped by the screen within a preset time period to the product of the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the preset time period.

[0066] In this embodiment of the invention, the detection points at the outlet of the water collection tank are evenly distributed. The number of detection points is determined based on the actual width of the water collection tank. If the width of the water collection tank is less than or equal to 2m, two detection points are set up with a spacing of 1 / 2 of the width of the water collection tank. If the width of the water collection tank is greater than 2m and less than or equal to 5m, three detection points are set up with a spacing of 1 / 3 of the width of the water collection tank. If the width of the water collection tank is greater than 5m, one additional detection point is added for every 2m increase, with a maximum number of detection points not exceeding five, ensuring that the spacing between adjacent detection points is less than or equal to 2m to avoid missing local flow data due to excessive spacing. The detection points are all located in the middle area of ​​the water collection tank outlet cross-section (≥0.3m from the tank wall), avoiding the vortex area near the tank wall to reduce the impact of water flow disturbance on the accuracy of flow detection. Each detection point is equipped with an electromagnetic flowmeter (measuring range 0-50m). 3 / h (accuracy ±1%), the flow meter probe is installed at a 45° angle to the bottom of the collection tank, with the center line of the probe 0.2-0.5m from the bottom of the tank, ensuring that the probe is completely submerged in the wastewater and does not contact the silt at the bottom of the tank; the flow meter collects the instantaneous wastewater flow at each detection point in real time at a preset collection frequency (3 minutes / time), and transmits the data to the data acquisition module for storage, providing basic data for subsequent average wastewater flow calculation; the "average wastewater flow at several detection points at the outlet of the collection tank within a preset time period" refers to the instantaneous flow data collected at each detection point within a preset time period (such as 1 hour, 30 minutes) The average value obtained after statistical analysis is calculated using the following steps: All instantaneous flow data from each detection point within a preset time period are extracted from the data acquisition module. Outliers are removed using the 3σ criterion. The mean (μ1) and standard deviation (σ1) of the instantaneous flow data at each detection point are calculated. If a certain instantaneous flow data exceeds the range [μ1-3σ1, μ1+3σ1], it is determined to be an outlier (such as a sudden increase / decrease in instantaneous flow due to water flow impact) and is removed. For the instantaneous flow data at each detection point after removing outliers, the average flow rate of a single detection point within a preset time period is calculated using the arithmetic mean method. The calculation formula is as follows: ;in, The average flow rate at the i-th detection point (unit: m³) 3 / h), The instantaneous flow rate (unit: m³) collected at the i-th detection point during the j-th measurement. 3 / h), where n is the number of valid data points at the i-th detection point after removing outliers; since the detection points are evenly distributed and cover the entire cross-section of the water collection tank outlet, the contribution weight of the flow data of each detection point to the overall flow is consistent. Therefore, the average flow rate of each detection point is calculated using the arithmetic mean method to obtain the comprehensive average wastewater flow rate of the water collection tank outlet within the preset time period. The calculation formula is: ;in, The average wastewater flow rate at the outlet of the collection tank within a preset time period (unit: m³). 3 / h), where m is the total number of detection points. The average flow rate at the i-th detection point (unit: m³) 3 / h).

[0067] Specifically, such as Figure 2 As shown, the data analysis module determines the wastewater type based on the comparison between the wastewater impurity load tendency value and the preset wastewater impurity load tendency value.

[0068] If the wastewater impurity load tendency value is greater than or equal to the preset wastewater impurity load tendency value, the data analysis module determines that the wastewater type is a high-load wastewater type.

[0069] If the wastewater impurity load tendency value is less than the preset wastewater impurity load tendency value, the data analysis module determines that the wastewater type is a low-load wastewater type.

[0070] The preset wastewater impurity load tendency value mentioned in this embodiment of the invention can be determined by the following method: Before the system is officially put into use, basic data is collected according to the following requirements, covering different flushing scenarios and time periods; the actual calculation results of the wastewater impurity load tendency value at different time periods each day are recorded, and the flushing vehicle type, flushing volume, and weather conditions at the corresponding time periods are also recorded; the data is calculated and stored according to the preset duration, and at least 12-24 sets of valid data are obtained each day to ensure that the data covers different load states; the corresponding treatment effect verification results are marked for each set of valid data. If the turbidity of the effluent from the subsequent multi-stage joint control sedimentation module is less than or equal to 20 NTU and the turbidity of the effluent from the composite filtration module is less than or equal to 5 NTU (meeting the standard)... If the turbidity of the effluent from sedimentation is greater than 20 NTU or the turbidity of the filtered effluent is greater than 5 NTU (not meeting the standard), it is marked as insufficient treatment. The collected data are statistically analyzed by using frequency distribution histograms and correlation analysis of treatment effect. The effective data are grouped by interval, and frequency distribution histograms are plotted to determine the concentrated distribution interval of the data. Correlation of treatment effect: The effective treatment rate (number of effective treatments / total number of treatments × 100%) corresponding to different groups of data is statistically analyzed. On the correlation curve between effective treatment rate and wastewater impurity load tendency value, the load tendency value corresponding to the first drop of effective treatment rate to below 95% is the preset wastewater impurity load tendency value (which can be dynamically updated).

[0071] This invention dynamically adjusts the number of detection points according to the width of the collection tank and strictly controls the adjacent spacing to be less than or equal to 2m to ensure coverage of the entire outlet cross-section and avoid biased flow data due to local detection omissions. Simultaneously, the detection points are set in the central area at a distance of more than or equal to 0.3m from the tank wall, avoiding the vortex zone of the tank wall and reducing the interference of water flow disturbance on flow detection from the source. The 3σ criterion is used to remove outliers from instantaneous flow data, effectively filtering the influence of accidental factors such as water flow impact on the data. Subsequently, the average flow rate of a single detection point and the comprehensive average flow rate are calculated using the arithmetic mean method, and based on the characteristic of equally spaced detection points, they are assigned equal weights to further ensure the objectivity of the average flow rate calculation results, providing accurate flow basis data for impurity load tendency values. Without accurate classification, treating high-load wastewater as low-load wastewater can easily lead to substandard effluent, while treating low-load wastewater as high-load wastewater can easily lead to disordered system operating parameters. This solution, through accurate classification, ensures that the treatment parameters match the wastewater load, reducing system operation fluctuations.

[0072] Specifically, such as Figure 3 As shown, the sludge detection module determines the distribution type of sludge detection points based on the wastewater type and / or the particle size distribution type of the screen residue, wherein,

[0073] If the wastewater type is high-load wastewater or the particle size distribution of the screen residue is predominantly coarse particles, the sludge detection module determines the distribution type of the sludge detection points to be a dense gradient stratified distribution type.

[0074] If the wastewater type is low-load wastewater and the particle size distribution of the screen residue is a mixed distribution of multiple particles, the sludge detection module determines that the distribution type of the sludge detection points is a balanced gradient stratified distribution.

[0075] Specifically, the data analysis module is also used to determine the particle size distribution type of the screen-retained material based on the particle size data of the screen-retained material, wherein,

[0076] Obtain particle size data of the material trapped by the grid within a preset time period, and calculate the cumulative weight percentage of the trapped material with a particle size larger than the preset particle size;

[0077] If the cumulative weight percentage of the retained material with a particle size larger than the preset particle size is greater than the preset percentage, the data analysis module determines that the particle size distribution type of the grid retained material is a coarse particle-dominated distribution type.

[0078] The preset particle size in this embodiment of the invention can be determined by the following method: During the system trial operation, particle size data of the screen retainers are collected according to the following requirements, covering different scenarios and time periods. The screen retainers are collected daily for a preset duration, and graded and screened using a standard sieving method. The weight of the retainers on each screen with different aperture sizes is recorded. At the same time, the type of vehicle being washed and the total weight of the retainers are recorded for the corresponding time period to ensure that the data covers different impurity characteristic scenarios. For the retainers of each screen, their settling velocity is measured. The retainers are placed in simulated water with the same water quality as the primary sedimentation tank, and the time it takes for 90% of the retainers to settle to the bottom of the container is recorded. This time must match the design retention time of the primary sedimentation tank. Based on the collected data, the initial value of the preset particle size is determined by correlating the settling velocity with the retention rate. The 90% settling time of retainers of different particle sizes is statistically analyzed, and those with a 90% settling time less than or equal to the design retention time of the primary sedimentation tank are selected. The minimum particle size between the particles is used as the preset particle size. After the system is officially running, the preset particle size is calibrated monthly according to the following logic: If particles larger than the current preset particle size are frequently detected at the inlet of the primary sedimentation tank (in continuous 24-hour monitoring, the number of times particles larger than the current preset particle size are captured by the online particle analyzer at the inlet sampling point is greater than or equal to 20 times, and such events occur for more than 3 days within a week), it indicates that the preset particle size setting is too large and needs to be reduced by 0.5-1mm; if the proportion of particles larger than the preset particle size in the screen interception material is less than 10%, but a large number of coarse particles still settle in the primary sedimentation tank, it indicates that the preset particle size setting is too small and needs to be increased by 0.5mm-1mm; the method for determining the preset proportion is the same as the method for determining the preset particle size (preferred value is 60%), and will not be repeated here, but the above values ​​are not limited to this, and those skilled in the art can adjust them according to the actual situation.

[0079] This invention addresses the challenges of high-load wastewater or coarse-particle-dominated conditions by employing a dense gradient stratified distribution. By increasing the density of detection points and refining the stratification, it can more accurately capture the distribution differences of sludge at different depths under high impurity loads, avoiding assessment biases due to insufficient detection. For low-load wastewater with a mixture of particles, a balanced gradient stratified distribution is used. This ensures basic detection accuracy while reducing redundant detection points, lowering system operating costs, and optimizing resource allocation. The invention dynamically adjusts the detection point distribution through dual determination of wastewater type and particle size distribution type, avoiding resource waste caused by high-density detection for low-load wastewater and preventing treatment risks due to insufficient detection of high-load wastewater.

[0080] Specifically, the encrypted gradient layered distribution type arranges silt detection points based on the principle of associating the density of detection points with the silt density gradient change value; the balanced gradient layered distribution type arranges silt detection points based on the principle of equally spaced coverage of the full thickness of the silt layer.

[0081] In this embodiment of the invention, the detection point density is determined based on the principle of correlating the density gradient change value with the silt density. Specifically, in areas with significant density gradient changes (i.e., intervals with large density change rates per unit height) along the vertical direction of the silt layer, detection points are densely deployed to accurately capture density stratification interfaces and depositional characteristics. The density gradient change value (Δρ / Δh) is calculated using historical data or real-time ultrasonic density sensor measurements, where Δρ is the density difference between adjacent detection points and Δh is the vertical spacing between detection points. The detection point density is positively correlated with the density gradient change value; that is, the larger the gradient change value, the denser the detection point deployment (e.g., for every 0.1 g / cm³ increase in gradient change value). 3 / m, the spacing between detection points is reduced by 0.2m); detection points are placed at intervals of 0.3-0.5m at the bottom (high density zone) and middle (density transition zone) of the silt layer, and at intervals of 0.8-1.0m at the top (low density zone); the gradient stratification distribution is balanced, and the detection points are laid out based on the principle of equal spacing to cover the full thickness of the silt layer, including uniformly placing detection points along the vertical direction of the silt layer from the bottom of the pool to the silt surface, with a fixed spacing between adjacent detection points (e.g., 0.5-0.7m); the number of detection points is determined according to the maximum designed thickness of the silt layer (e.g., 4 points are placed when the thickness is ≤2m, and 2 more points are added for every 1m increase in thickness); density data is collected independently at each detection point, reflecting the overall density distribution of the silt layer, without the need to adjust the density for local gradient changes.

[0082] This invention is based on the principle of correlating detection point density with density gradient changes. It densifies the detection points in areas of significant density change, accurately capturing subtle changes in silt layer interfaces and depositional characteristics, avoiding the loss of gradient change information due to sparse detection points. The invention also employs differentiated detection point placement for different regions of the silt layer, focusing on key changes in high-density and transitional zones while appropriately reducing detection points in low-density areas. Quantitative guidance of detection point density using density gradient changes dynamically correlates the distribution of detection points with actual silt characteristics, ensuring the data accurately reflects the physical process of silt deposition. Furthermore, based on the principle of equal spacing covering the entire thickness, detection points are evenly distributed along the vertical direction of the silt layer, comprehensively reflecting the overall distribution characteristics of silt layers formed by low-load, multi-particle mixed wastewater, avoiding overall assessment bias caused by missing local data. Finally, the invention dynamically adjusts the number of detection points according to the maximum thickness of the silt layer, ensuring complete coverage while avoiding over-detection, thus balancing data comprehensiveness and detection costs.

[0083] Specifically, the intelligent dredging module determines the dredging path planning method based on the dredging density gradient difference and the dredging fluctuation coefficient, wherein,

[0084] The intelligent dredging module calculates the silt density gradient difference based on the silt density at adjacent detection points, and calculates the silt fluctuation coefficient based on the silt density data at the same detection point within a preset time period.

[0085] If any silt density gradient difference is greater than a preset gradient difference threshold, the intelligent dredging module determines to adopt a partitioned grasping path planning method based on density gradient difference.

[0086] If all silt density gradient differences are less than or equal to a preset gradient difference threshold, and any silt fluctuation coefficient is greater than a preset fluctuation coefficient threshold, then the intelligent dredging module determines to adopt a dynamic adjustment grasping path planning method based on the fluctuation coefficient.

[0087] If all silt density gradient differences are less than or equal to a preset gradient difference threshold, and all silt fluctuation coefficients are less than or equal to a preset fluctuation coefficient threshold, then the intelligent dredging module determines to adopt a preset standard grasping path planning method.

[0088] In this embodiment of the invention, the preset gradient difference threshold can be set to 0.15-0.25 g / cm³ based on initial operating data. 3 The preset fluctuation coefficient threshold can be set to 0.1-0.2. After the system is officially running, the threshold will be reverse-optimized and calibrated monthly based on the thickness of the residual sludge at the bottom of the pool after dredging (target ≤ 5cm). The sludge density gradient difference reflects the variation in sludge density between adjacent detection points and is used to identify whether there is obvious stratification within the sludge layer. The calculation formula is: Δρ i =∣ρ i+1 -ρ i |, where ρ i and ρ i+1 Real-time density measurements at two adjacent detection points (unit: g / cm³) 3 ), Δρ i The density gradient difference between adjacent detection points in the i-th group; this parameter helps determine the uniformity of the silt layer: if the gradient difference is large, it indicates that the silt layer has significant stratification, requiring targeted sampling in different areas; if the gradient difference is small, it indicates that the silt layer is relatively uniform overall; the silt fluctuation coefficient is used to quantify the degree of fluctuation in silt density at the same detection point within a preset time period, reflecting the stability or dynamic change characteristics of the silt layer); the calculation formula is: C v = Where μ is the arithmetic mean of all density measurements at a certain detection point within a preset time period, σ is its standard deviation, and C v This is the fluctuation coefficient of the detection point; the larger the fluctuation coefficient, the more drastic the change in silt density at that point over time, and the dredging strategy needs to be dynamically adjusted to adapt to real-time changes; conversely, it indicates that the silt layer is stable and a fixed path can be used for dredging.

[0089] In this embodiment of the invention, a zoned grasping path planning method based on density gradient difference is adopted: when the density gradient difference of silt between any adjacent detection points is greater than a preset gradient difference threshold, it indicates that there is obvious stratification of the silt layer; at this time, the intelligent sludge removal module divides the bottom of the sedimentation tank into high-density and low-density areas and plans different grasping strategies; the high-density area adopts a low-speed deep digging strategy, reducing the closing speed of the grab bucket by 20%-30% to ensure sufficient grasping and avoid leaving compacted silt behind; the low-density area adopts a fast shallow grasping strategy, reducing the sinking depth of the grab bucket and increasing the closing speed to prevent disturbance from causing the diffusion of suspended matter. A dynamic adjustment grasping path planning method based on fluctuation coefficient is adopted: when all density gradient differences do not exceed the preset threshold, but the fluctuation coefficient of any detection point is greater than the preset fluctuation coefficient threshold, it indicates that the silt layer is significantly stratified. The mud layer is in an unstable state. At this time, the intelligent dredging module starts the dynamic adjustment mode, monitors the detection points with significant density fluctuations in real time, and prioritizes avoiding areas with violent fluctuations in the grab path (to prevent the grab bucket from emptying or overloading due to sudden density changes). The module dynamically adjusts the sinking depth and moving speed of the grab bucket according to the magnitude of the fluctuation coefficient (for every 0.05 increase in the fluctuation coefficient, the sinking depth of the grab bucket decreases by 5%, and the moving speed decreases by 10%). A preset standard grab path planning method is adopted: when all density gradient differences are below the threshold and the fluctuation coefficients of all detection points are below the threshold, it indicates that the mud layer is uniform and stable. At this time, the intelligent dredging module adopts a preset standard path (such as an "S" shaped full-coverage path) and grabs the mud according to fixed parameters (the grab bucket sinks to 0.3m above the bottom of the pool and moves at a constant speed), without the need for real-time adjustment.

[0090] This invention employs a zoned grabbing strategy based on density gradient differences. It accurately identifies the stratification of silt layers, using low-speed deep digging in high-density areas to ensure thorough removal of compacted silt; and rapid shallow grabbing in low-density areas to reduce the spread of suspended solids. When the silt fluctuation coefficient exceeds a threshold, dynamic path planning is used. By prioritizing avoidance of areas with severe fluctuations and adjusting parameters in tandem, it prevents the grab bucket from emptying or overloading due to sudden density changes, adapting to the dynamic characteristics of the silt layer. For uniform and stable silt layers, a preset standard path is used, operating with fixed parameters, ensuring dredging quality while improving operational efficiency and reducing unnecessary... The required parameter adjustment time is reduced; the zoned grabbing strategy ensures thorough cleaning of high-density areas and reduces unnecessary deep digging in low-density areas, avoiding energy waste; the dynamic adjustment mode reduces the probability of grab bucket idling or jamming by avoiding fluctuating areas; the standardized path achieves efficient full coverage under stable operating conditions, significantly reducing equipment energy consumption; the grab bucket closing speed is reduced for high-density sludge, mitigating mechanical impact; the sinking depth is adjusted for fluctuating areas, reducing the probability of hard contact between the grab bucket and the pool bottom, extending equipment lifespan; the dynamic adjustment mechanism avoids damage to the transmission system caused by sudden load changes, reducing equipment failure rate and maintenance frequency.

[0091] Specifically, the partitioned crawling path planning method based on density gradient difference includes:

[0092] Regions where the density gradient difference of silt is greater than a preset gradient difference threshold are identified as high density difference regions.

[0093] An independent grabbing sub-path is assigned to the high density difference area, and the silt in the area is grabbed first.

[0094] Assign regular crawling sub-paths to areas with gradual density changes.

[0095] In this embodiment of the invention, the intelligent dredging module receives and processes density data from the sludge detection module in real time; calculates the real-time sludge density gradient difference between all adjacent detection points, and converts the calculated Δρ... i Compare with a preset gradient difference threshold; identify and locate all conditions that satisfy Δρ i Spatial areas with a gradient difference exceeding a preset threshold are marked as high-density difference areas. These areas typically correspond to density abrupt change interfaces in the silt layer, such as the boundary between the bottom high-density sediment layer and the upper low-density suspended layer, or localized accumulation areas formed by water inflow. Independent grabbing sub-path generation and priority execution: An independent, closed grabbing sub-path is generated for each identified high-density difference area. The planning of this sub-path closely follows the boundary of the high-density difference area to ensure complete coverage of the abnormal area. A higher grabbing priority is assigned to this independent sub-path. At the start of the dredging operation, the intelligent dredging module controls the grab bucket to move preferentially to these areas to perform grabbing operations. Based on the physical characteristics of the high-density difference areas, specific grabbing parameters are configured for this independent sub-path: grab bucket sinking depth, controlling the grab bucket to penetrate to the bottom of the pool or the bottom of the high-density layer to ensure that compacted silt is grabbed; grab bucket closing speed, adopting a low-speed closing mode (e.g., reducing the rated speed). (20%-30%), allowing the grab bucket teeth to slowly and powerfully insert into the compacted sludge, avoiding slippage or incomplete grabbing, and ensuring the amount grabbed in a single operation; walking speed, appropriately reducing the walking speed when passing through this area to coordinate with the low-speed closing operation, ensuring the grabbing effect; allocation and execution of conventional grabbing sub-paths: for areas with gentle density changes at the bottom of the sedimentation tank (i.e., areas where the density gradient difference between all adjacent detection points does not exceed the preset threshold), conventional grabbing sub-paths are assigned to them; conventional grabbing sub-paths are usually planned according to a preset standard pattern that covers the entire bottom of the tank (such as an "S" shaped or "bow" shaped path); standard grabbing parameters are configured for this type of path, such as the grab bucket sinking to the preset reference depth, closing and walking at the rated speed, etc.; integration and optimization of sub-paths: the path planning algorithm of the intelligent dredging module ultimately seamlessly integrates all independent grabbing sub-paths and conventional grabbing sub-paths to form a globally optimal, coherent, and complete dredging path.

[0096] Specifically, the dynamic adjustment capture path planning method based on the fluctuation coefficient includes:

[0097] The area where the detection point has a sludge fluctuation coefficient greater than a preset fluctuation coefficient threshold is identified as a high fluctuation area;

[0098] Dynamically adjusted grabbing sub-paths are assigned to the high-fluctuation areas, and the grabbing frequency and / or grabbing depth of these sub-paths are dynamically adjusted based on real-time monitored silt density data.

[0099] In this embodiment of the invention, based on the sludge density data sequence collected from each detection point within a preset time period, the fluctuation coefficient of each detection point is calculated; the fluctuation coefficient of each detection point is compared with a preset fluctuation coefficient threshold; all detection points with fluctuation coefficients greater than the threshold are identified and marked; taking these detection points as the core, and combining their spatial location (e.g., using the K-nearest neighbor algorithm or defining a circular area with radius R centered on the point, where R can be set to 0.5m-1.0m according to the size of the tank), one or more high fluctuation areas are defined on the bottom plan of the sedimentation tank; the generation and attribute allocation of the grasping sub-path are dynamically adjusted: an independent, non-fixed grasping sub-path is assigned to each identified high fluctuation area; the core feature of this sub-path is its main parameter (grasping...). The frequency and grabbing depth are not preset statically, but dynamically adjusted based on real-time monitored sludge density data. Dynamic adjustment of grabbing frequency: Above the high-fluctuation area, the intelligent sludge removal module (such as the grab bucket installed on the gantry crane) will increase the inspection frequency; shorten the collection interval of density data in this area (such as adjusting from the usual 10 minutes / time to 2 minutes / time); once the real-time density value indicates that the sludge in this area has accumulated to a grabbable concentration, a grabbing operation will be triggered immediately; after the grabbing is completed, the frequency will return to the monitoring state, waiting for the next triggering condition to be met; dynamic adjustment of grabbing depth: The lowering depth of the grab bucket (h_drop) is linked to the real-time measured sludge layer thickness (h_sludge) or the depth of the density peak in this area. The calculation formula can be simplified to: h_drop = k × h_sludge, where k is a safety factor (usually taken as 0.8-0.9) to ensure that the grab bucket can fully grab the sludge without going too deep to avoid disturbing the bottom of the pool or damaging the equipment; the sludge layer thickness h_sludge is determined by the laser level sensor and the ultrasonic density profile; the priority and avoidance logic of the path execution: in the global grab path planning, these dynamically adjusted sub-paths are given a higher execution priority. When the system detects that the sludge density in a high-fluctuation area reaches the grab condition, the current standard path operation can be paused or postponed, and the dredging of the area can be performed first to prevent excessive accumulation of sludge or re-suspension and diffusion; at the same time, this method includes avoidance logic; if the real-time density of a high-fluctuation area is extremely low and the fluctuation is violent, it indicates that strong suspension may be occurring there. At this time, the grab sub-path will instruct the grab bucket to temporarily avoid this area and enter it after it stabilizes, so as to avoid ineffective grabbing and secondary disturbance to the water body.

[0100] Specifically, the preset standard crawling path planning method includes:

[0101] Control the dredging equipment to traverse the bottom of the sedimentation tank along a preset path;

[0102] The lowering depth of the grab bucket of the dredging equipment is set based on the average thickness of the silt layer obtained by the silt detection module.

[0103] The preset path described in this embodiment of the invention is the optimal path pre-calculated and stored in the control system based on the geometry of the sedimentation tank (usually rectangular) and historical dredging efficiency data; it typically adopts an "S"-shaped reciprocating path or a "bow"-shaped path to ensure that the movement trajectory of the grab bucket can fully cover the bottom area of ​​the tank without any blind spots; the walking motor and electric hoist of the dredging equipment (such as a gantry crane equipped with a four-rope grab bucket) receive instructions from the control system and drive the grab bucket to move at a constant speed strictly along the preset path. The walking speed is set according to the pool length and dredging requirements (e.g., 0.3-0.5 m / s) to ensure smooth movement and avoid resuspension of settled sludge due to sudden speed changes. This preset path is a baseline path determined by simulation calculations and actual tests during the system's commissioning phase and is applicable to most stable operating conditions. The lowering depth of the grab bucket of the dredging equipment is set based on the average thickness of the sludge layer obtained by the sludge detection module. The lowering depth of the grab bucket is a key parameter for dredging operations; lowering it too deeply will grab the bottom cushion layer of the pool and damage the equipment; lowering it too shallowly will result in incomplete dredging. Before starting the standard path dredging, the intelligent dredging module obtains the sludge thickness data measured at each detection point in the current sedimentation tank from the sludge detection module and calculates the average thickness of the sludge layer at the entire bottom of the pool (H). avg The calculation formula is: H avg = ; where H i Let H be the silt thickness measured at the i-th detection point, and n be the number of valid detection points; based on the calculated average thickness H... avg The control system sets the lowering depth of the grab (D). drop Typically, the drop-down depth is set to: D drop =H avg +C; where C is a safety margin constant, generally taken as 0.1-0.3 meters. This margin is used to ensure that the grab bucket can be completely submerged in the silt layer for effective grabbing, while avoiding collision with the hard pool bottom structure. During the entire standard path dredging process, the lowering depth of the grab bucket remains constant at this set value unless the system detects a significant change in the average thickness of the silt (such as a reduction in thickness of more than 20% after a single dredging operation), in which case the lowering depth will be recalculated and adjusted at the beginning of the next dredging cycle.

[0104] This invention identifies areas with density gradient differences exceeding a threshold, precisely pinpointing sludge stratification interfaces and localized accumulation zones. This ensures that these critical areas, often overlooked by traditional dredging methods, receive priority treatment. Independent grabbing sub-paths and dedicated parameters are configured for high-density difference areas to specifically address the difficulty in removing compacted sludge. Prioritizing dredging operations in these areas avoids increased processing difficulty due to continuous sludge accumulation, reducing subsequent rework costs. Seamless integration of independent sub-paths with conventional paths ensures both adequate treatment of abnormal areas and overall coverage of the pool bottom, avoiding the contradiction between localized over-treatment and global omission. Differentiated operations are employed based on the physical characteristics of high-density difference areas, minimizing the impact on dredging processes. Sludge disturbance and suspended solids diffusion ensure stable effluent quality from the sedimentation tank. High-fluctuation areas are accurately identified using the K-nearest neighbor algorithm or radius delineation, and the grabbing frequency and depth are dynamically adjusted based on real-time density monitoring data, ensuring the dredging strategy remains synchronized with the sludge condition. High-fluctuation areas are given high priority, allowing for the suspension of routine operations to prioritize the treatment of risky areas and prevent excessive sludge accumulation. Simultaneously, an avoidance strategy is adopted for areas with extremely low density and drastic fluctuations to prevent ineffective grabbing and secondary disturbance. An S-shaped or bow-shaped path designed based on the tank's geometry ensures complete coverage of the tank bottom without blind spots, significantly improving dredging efficiency. The lowering depth is calculated based on the average sludge thickness, combined with a safety margin, ensuring sufficient grabbing while avoiding damage caused by the grab bucket touching the tank bottom.

[0105] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. 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 self-circulating sedimentation and reuse system for flushing wastewater, characterized in that, include: The wastewater collection module, located in the vehicle washing area and on both sides of the vehicle driving road, includes a water collection tank and a screen for collecting wastewater; A multi-stage sedimentation module, which is connected to the wastewater collection module, includes a primary sedimentation tank, a secondary sedimentation tank and a tertiary sedimentation tank connected in sequence, for removing and separating impurities from the wastewater; The data acquisition module is connected to the wastewater collection module and the multi-level control sedimentation module, and is used to collect water volume data and impurity data of the wastewater at the outlet of the water collection tank and in the sedimentation tank. The data analysis module, which is connected to the data acquisition module, is used to calculate the wastewater impurity load tendency value based on the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the weight of the screen intercepted within a preset time period, and to determine the wastewater type based on the wastewater impurity load tendency value. A sludge detection module, which is connected to the data analysis module, is used to determine the distribution type of sludge detection points based on the wastewater type and / or the particle size distribution type of the screen residue. The intelligent dredging module is connected to the sludge detection module and the multi-level joint control sedimentation module respectively, and is used to determine the sludge grabbing path planning method based on the sludge density gradient difference and sludge fluctuation coefficient. A composite filtration module, which is connected to the multi-stage controlled sedimentation module, includes a pretreatment filtration unit, a fine filtration unit, and an auxiliary function unit, for removing impurities that are not completely separated by the multi-stage controlled sedimentation module; The water resource reuse module is connected to the composite filtration module and is used to deliver the filtered clean water to the flushing system via a booster pump. The flushed wastewater is then reintroduced into the primary sedimentation tank.

2. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 1, characterized in that, The data analysis module calculates the wastewater impurity load tendency value based on the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the weight of the material trapped by the screen within a preset time period. The wastewater impurity load tendency value is the ratio of the weight of the material trapped by the screen within a preset time period to the product of the average wastewater flow rate at several detection points at the outlet of the water collection tank within a preset time period and the preset time period.

3. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 2, characterized in that, The data analysis module determines the wastewater type based on a comparison between the wastewater impurity load tendency value and the preset wastewater impurity load tendency value. If the wastewater impurity load tendency value is greater than or equal to the preset wastewater impurity load tendency value, the data analysis module determines that the wastewater type is a high-load wastewater type. If the wastewater impurity load tendency value is less than the preset wastewater impurity load tendency value, the data analysis module determines that the wastewater type is a low-load wastewater type.

4. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 3, characterized in that, The sludge detection module determines the distribution type of sludge detection points based on the wastewater type and / or the particle size distribution type of the screen residue, wherein, If the wastewater type is high-load wastewater or the particle size distribution of the screen residue is predominantly coarse particles, the sludge detection module determines the distribution type of the sludge detection points to be a dense gradient stratified distribution type. If the wastewater type is low-load wastewater and the particle size distribution of the screen residue is a mixed distribution of multiple particles, the sludge detection module determines that the distribution type of the sludge detection points is a balanced gradient stratified distribution.

5. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 4, characterized in that, The data analysis module is also used to determine the particle size distribution type of the screen-retained material based on the particle size data of the screen-retained material, wherein, Obtain particle size data of the material trapped by the grid within a preset time period, and calculate the cumulative weight percentage of the trapped material with a particle size larger than the preset particle size; If the cumulative weight percentage of the retained material with a particle size larger than the preset particle size is greater than the preset percentage, the data analysis module determines that the particle size distribution type of the grid retained material is a coarse particle-dominated distribution type.

6. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 5, characterized in that, The encrypted gradient layered distribution type arranges silt detection points based on the principle of associating the density of detection points with the silt density gradient change value; the balanced gradient layered distribution type arranges silt detection points based on the principle of equally spaced coverage of the full thickness of the silt layer.

7. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 6, characterized in that, The intelligent dredging module determines the dredging path planning method based on the dredging density gradient difference and the dredging fluctuation coefficient. The intelligent dredging module calculates the silt density gradient difference based on the silt density at adjacent detection points, and calculates the silt fluctuation coefficient based on the silt density data at the same detection point within a preset time period. If any silt density gradient difference is greater than a preset gradient difference threshold, the intelligent dredging module determines to adopt a partitioned grasping path planning method based on density gradient difference. If all silt density gradient differences are less than or equal to a preset gradient difference threshold, and any silt fluctuation coefficient is greater than a preset fluctuation coefficient threshold, then the intelligent dredging module determines to adopt a dynamic adjustment grasping path planning method based on the fluctuation coefficient. If all silt density gradient differences are less than or equal to a preset gradient difference threshold, and all silt fluctuation coefficients are less than or equal to a preset fluctuation coefficient threshold, then the intelligent dredging module determines to adopt a preset standard grasping path planning method.

8. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 7, characterized in that, The partitioned crawling path planning method based on density gradient difference includes: Regions where the density gradient difference of silt is greater than a preset gradient difference threshold are identified as high density difference regions. An independent grabbing sub-path is assigned to the high density difference area, and the silt in the area is grabbed first. Assign regular crawling sub-paths to areas with gradual density changes.

9. The self-circulating sedimentation and reuse system for flushing wastewater according to claim 7, characterized in that, The dynamic adjustment capture path planning method based on the volatility coefficient includes: The area where the detection point has a sludge fluctuation coefficient greater than a preset fluctuation coefficient threshold is identified as a high fluctuation area; A dynamically adjusted grabbing sub-path is assigned to the high-fluctuation region, and the grabbing frequency and / or grabbing depth of the sub-path are dynamically adjusted based on real-time monitored silt density data.

10. The flushing wastewater self-circulation sedimentation and reuse system according to claim 7, characterized in that, The preset standard crawling path planning method includes: Control the dredging equipment to traverse the bottom of the sedimentation tank along a preset path; The lowering depth of the grab bucket of the dredging equipment is set based on the average thickness of the silt layer obtained by the silt detection module.

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