Dynamic updating method and system for pesticide production wastewater treatment combined with component analysis

Abstract

The application provides a pesticide production wastewater treatment dynamic updating method and system combined with component analysis, relates to the technical field of wastewater treatment, and the method comprises the following steps: rough detection is performed by using a non-specific high-dimensional sensing array; a dynamic planner of a micro-processing resource pool deployment is triggered to determine a first unit route, control a first multi-stage route automatic valve, and perform water treatment under dynamic flow concentration and flow distribution; directional detection is performed by using a multi-modal sensing array, a second unit route is determined, water treatment under the control of a second multi-stage route automatic valve is performed, and output is performed from a second pipeline end. The application solves the technical problem of low pesticide production wastewater treatment efficiency caused by the mismatch between a fixed treatment process flow and highly fluctuating and complex water quality characteristics in the prior art, and improves the pesticide production wastewater treatment efficiency and safety by means of hierarchical detection and route planning.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, and in particular to a dynamic updating method and system for pesticide production wastewater treatment that incorporates component analysis. Background Technology

[0002] In pesticide production, wastewater quality is influenced by various factors, including production processes, raw material types, and the external environment, resulting in a wide variety of pollutants and significant concentration fluctuations. Traditional wastewater treatment systems are typically based on fixed process flows, meaning their treatment paths are fixed after design. This lack of adaptability to dynamic changes in influent water quality leads to low treatment efficiency and, in some cases, overtreatment or undertreatment. For highly toxic and recalcitrant wastewater, fixed processes cannot enhance pretreatment intensity, easily causing inhibitory impacts on subsequent treatment units and even paralyzing the entire wastewater treatment system. Even when the influent load is low or the biodegradability is good, a complete and complex process still needs to be executed, resulting in huge energy consumption and pesticide waste, and high treatment costs. Because they cannot be adapted to different water conditions, fixed processes often tend to over-treat or allow excessively long retention times in certain units to ensure that the final effluent meets standards, leading to high overall energy, material, and time costs, thus affecting treatment efficiency.

[0003] In summary, existing technologies suffer from low efficiency in treating pesticide production wastewater due to the large fluctuations in water quality and the variation in pollutant concentrations over time and with changes in production processes. Fixed treatment processes are unable to cope with these variations in water quality. Summary of the Invention

[0004] The purpose of this application is to provide a dynamic updating method and system for pesticide production wastewater treatment that incorporates component analysis, in order to solve the technical problem of low treatment efficiency of pesticide production wastewater due to the large fluctuations in water quality and the changes in pollutant concentration with time and production process in the existing technology, which makes it difficult for fixed treatment processes to cope with different water quality changes.

[0005] In view of the above problems, this application provides a method and system for dynamic updating of pesticide production wastewater treatment by combining component analysis.

[0006] In a first aspect, this application provides a dynamic updating method for pesticide production wastewater treatment incorporating component analysis. This method is implemented through a dynamic updating system for pesticide production wastewater treatment incorporating component analysis. The method includes: using a non-specific high-dimensional sensor array to coarsely probe a water sample at the end of a first pipeline to determine a chemical fingerprint based on a high-dimensional vector; triggering a dynamic planner deployed in a microprocessor resource pool to perform a first high-dimensional route planning for the chemical fingerprint using entropy flow path dynamic planning, determining a first unit route; and controlling a first multi-level routing automatic valve to execute water treatment under dynamic convergence-diversion based on the first unit route. The microprocessor resource pool consists of distributed modular microprocessor units. Finally, a multi-modal sensor array is used to perform directional probes to determine a directional chemical fingerprint, triggering a second directional route planning based on the dynamic planner, determining a second unit route, and executing water treatment under the control of a second multi-level routing automatic valve, outputting the water from the second pipeline end.

[0007] Optionally, for pesticide production wastewater, historical treatment records are retrieved; based on the historical treatment records, a first high-dimensional component is mined and treatment resource is located to determine a first component map; based on the historical treatment records, a second directional component is mined and treatment resource is located to determine a second component map; and a treatment database is generated based on the first component map and the second component map.

[0008] Optionally, a dynamic programming architecture is constructed by embedding a microprocessor resource pool, with the initial state of the first chemical information fingerprint and the final state of the second standard information fingerprint as the planning objectives and the minimum entropy increase resistance as the decision-making guide. For the dynamic programming architecture, a first high-dimensional coarse training is performed to determine the first planning branch, and a second directional training is performed to determine the second planning branch. The dynamic planner is generated using the first planning branch and the second planning branch.

[0009] Optionally, the chemical information fingerprint is input into the first planning branch of the dynamic planner, and the first component spectrum is called to match the components with the processing resources through interaction with the processing database to determine the first processing conditions; for the first processing conditions, the optimal route planning decision is made with the minimum entropy increase resistance to determine the route of the first unit.

[0010] Optionally, based on the first unit routing, a first microprocessor unit is identified; the state of the first microprocessor unit is determined, and water sample is fed into the first microprocessor unit through a pipeline via automatic valve opening and closing control to perform one-step processing; a second microprocessor unit is identified and its state is determined and automatic valve diversion control is performed to perform microprocessor unit round-robin processing based on the first unit routing.

[0011] Optionally, the first microprocessor unit is set to a standard state; it is checked whether the first microprocessor unit is in an idle state; if not, time-delay control is performed on the automatic valve; it is checked whether the first microprocessor unit is in a standard state; if not, the restoration process of the unit's standard state is executed.

[0012] Optionally, after completing the processing of the first unit route, the detection of directional chemical information fingerprint is performed; the directional chemical information fingerprint is imported into the second planning branch of the dynamic planner, and the second component spectrum is retrieved by interacting with the processing database to match the components with the processing resources and determine the second processing conditions; for the second processing conditions, the integration of the microprocessor unit is performed to generate the second unit route.

[0013] Optionally, after completing the second unit routing process, water component quality inspection is performed to determine the treatment efficiency; a wastewater treatment chain is generated based on the first unit routing and the second unit routing; it is determined whether the treatment efficiency meets the standard, and if it does, the wastewater treatment chain is stored in the database.

[0014] Optionally, if the standard is not met, the non-compliant components are traced and located in the wastewater treatment chain to determine the cause of the abnormality; based on the cause of the abnormality, water recirculation and reprocessing are performed, and the cause of the abnormality in the wastewater treatment chain is identified and stored.

[0015] Secondly, this application also provides a dynamic update system for pesticide production wastewater treatment incorporating component analysis, used to execute the dynamic update method for pesticide production wastewater treatment incorporating component analysis as described in the first aspect. The dynamic update system for pesticide production wastewater treatment incorporating component analysis includes: a coarse detection module, used to perform coarse detection on a water sample at the end of a first pipeline using a non-specific high-dimensional sensor array to determine a chemical information fingerprint based on a high-dimensional vector; a first water treatment module, used to trigger a dynamic planner deployed in a microprocessor resource pool to perform a first high-dimensional route planning on the chemical information fingerprint using entropy flow path dynamic planning, determine a first unit route, and execute water treatment under dynamic convergence-diversion based on the first unit route by controlling a first multi-level routing automatic valve, wherein the microprocessor resource pool is a distributed modular microprocessor unit; and a directional detection module, used to perform directional detection through a multi-modal sensor array to determine a directional chemical information fingerprint, trigger a second directional route planning based on the dynamic planner, determine a second unit route, and execute water treatment under the control of a second multi-level routing automatic valve, outputting from the end of the second pipeline.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects: By employing a nonspecific high-dimensional sensor array, coarse detection of water samples is performed at the first pipe end to determine a chemical fingerprint based on a high-dimensional vector. This triggers a dynamic planner deployed in a microprocessor resource pool to perform a first high-dimensional route planning based on the chemical fingerprint using entropy flow path dynamic planning. This determines a first unit route, and water treatment is executed under dynamic convergence-diversion based on the first unit route by controlling a first multi-level routing automatic valve. The microprocessor resource pool consists of distributed modular microprocessor units. Then, directional detection is performed using a multimodal sensor array to determine a directional chemical fingerprint. This triggers a second directional route planning based on the dynamic planner, determining a second unit route and executing water treatment under the control of a second multi-level routing automatic valve, with output from the second pipe end. In other words, a nonspecific high-dimensional sensor array performs coarse detection of water samples, and a dynamic planner adjusts the water treatment strategy based on the chemical fingerprint. A multimodal sensor array performs directional detection to determine the directional chemical fingerprint, and a dynamic planner is used again to achieve more precise wastewater treatment, improving the efficiency and safety of pesticide production wastewater treatment.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the dynamic updating method for pesticide production wastewater treatment based on component analysis, as described in this application.

[0020] Figure 2 This is a schematic diagram of the dynamic update system for pesticide production wastewater treatment based on component analysis, as described in this application.

[0021] Explanation of reference numerals in the attached drawings: coarse detection module 11, first water treatment module 12, directional detection module 13. Detailed Implementation

[0022] This application provides a dynamic updating method and system for pesticide production wastewater treatment that incorporates component analysis. This addresses the technical problem of low treatment efficiency in existing pesticide production wastewater treatment processes due to significant fluctuations in water quality and changes in pollutant concentrations over time and with the production process. Fixed treatment processes struggle to cope with these variations. The method utilizes a non-specific high-dimensional sensor array for coarse detection of water samples and employs a dynamic planner to adjust the water treatment strategy based on the chemical fingerprint. Furthermore, a multi-modal sensor array performs directional detection to determine the directional chemical fingerprint, and the dynamic planner then enables more precise wastewater treatment, thus improving both the efficiency and safety of pesticide production wastewater treatment.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a dynamic updating method for pesticide production wastewater treatment incorporating component analysis. The method is executed through a dynamic updating system for pesticide production wastewater treatment incorporating component analysis. The specific steps of the dynamic updating method include: A nonspecific high-dimensional sensor array is used to perform coarse detection on water samples at the first pipe end to determine chemical information fingerprints based on high-dimensional vectors.

[0025] Specifically, a nonspecific high-dimensional sensing array is an array of multiple sensors used to simultaneously acquire different physical and chemical properties of water samples from multiple dimensions. Unlike sensors that target specific chemical components, nonspecific high-dimensional sensing arrays do not identify specific chemical substances in the water, but rather recognize the overall characteristics of the water sample through response patterns. They integrate various types of sensors, such as UV-Vis spectroscopy sensors, fluorescence spectroscopy sensors, pH sensors, redox potential sensors, conductivity sensors, and online total organic carbon analyzers.

[0026] When a stream of pesticide production wastewater flows in, a non-specific high-dimensional sensor array installed on the pipeline is immediately activated to perform real-time, online scanning detection of the water flow. Multiple sensors in the array work synchronously, rapidly collecting the physicochemical parameters of the current water sample in different dimensions within tens of seconds, generating a raw, high-dimensional dataset.

[0027] The first pipe end is the inlet pipe at the very front of the entire wastewater treatment system, serving as the entry point for all wastewater to be treated. Coarse detection involves rapidly scanning the overall chemical characteristics of a water sample using a non-specific sensor array, without detailed component analysis or detecting specific components. Instead, it determines the sample's type, such as its overall toxicity and biodegradability. The data from the non-specific sensor array is analyzed and processed, merging into a high-dimensional vector to form the water sample's chemical fingerprint. This fingerprint reflects the overall chemical characteristics of the water sample but does not directly identify specific compounds. For example, if a water sample has high redox properties or strong toxicity, its fingerprint will exhibit a different characteristic pattern than other water samples.

[0028] A high-dimensional vector refers to a set of data comprised of the readings from each sensor in a sensing array. The chemical fingerprint of each water sample is typically represented as a high-dimensional vector. For example, a nonspecific high-dimensional sensing array completes a full detection cycle within 30 seconds and generates the following set of example values: Sensor 1 outputs strong absorption peaks at both 254 nm and 280 nm wavelengths; Sensor 2 outputs a distinct characteristic fluorescence peak observed at excitation / emission wavelengths of 278 / 360 nm; Sensor 3 outputs 18500 mg / L; Sensor 4 outputs 12000 μS / cm, indicating extremely high salinity; and Sensor 5 outputs -125 mV. This coarse-grained detection by the nonspecific high-dimensional sensing array allows for rapid assessment of the overall chemical characteristics of a water sample without the need for detailed identification of each component, thus improving the efficiency of water quality monitoring.

[0029] Furthermore, this application also includes the following steps: for pesticide production wastewater, retrieving historical treatment records; based on the historical treatment records, performing first high-dimensional component mining and treatment resource positioning to determine a first component map; based on the historical treatment records, performing second directional component mining and treatment resource positioning to determine a second component map; and generating a treatment database based on the first component map and the second component map.

[0030] Specifically, pesticide production wastewater refers to wastewater generated during pesticide production. This wastewater typically contains various pesticide chemicals, solvents, and other pollutants, and its quality fluctuates significantly. Pesticide production wastewater encompasses multiple types of pesticide production wastewater. Historical treatment records for each type of wastewater are reviewed, including the composition of previously treated water samples, the treatment methods used, the time required, the resources consumed, and the final treatment results. This can be summarized as the influent chemical fingerprint, the routing sequence of the treatment units used, and the final effluent quality results, such as COD and toxicity removal rate.

[0031] First-dimensional component mining was performed on historical treatment records to extract the main pollutant components in wastewater samples. Cluster analysis was conducted by extracting data from the influent chemical fingerprints of historical treatment records to identify which fingerprints clustered together in the feature space, thus classifying the diverse wastewater into multiple wastewater types. Simultaneously, treatment resource localization was performed, i.e., tracing back the most frequently and successfully treated preferred pathways for each type of wastewater, including the selection of different treatment methods, equipment, and required reagents, thereby forming the first-dimensional component profile. First-dimensional component mining is used to uncover the mapping relationship between different wastewater types and macroscopic treatment requirements. For example, cluster analysis of over 1000 wastewater records revealed that type A wastewater exhibited highly similar characteristics: total organic carbon concentration >10000 mg / L, a strong absorption peak at 280 nm ultraviolet wavelength, and conductivity >5000 μS / cm. Retrospective analysis revealed that in treating this type of wastewater, 95% of successful cases first reached oxidation unit 1, with optimal operating parameters: a hydrogen peroxide to chemical oxygen demand (COD) ratio of 1.8, a ferrous iron to hydrogen peroxide molar ratio of 0.15, and a reaction pH of 3.2. A rule was established in the treatment database: if the wastewater characteristics ∈ Class A, then the first recommended unit is oxidation unit 1, with parameters including a hydrogen peroxide / COD ratio of 1.8, a ferrous iron / hydrogen peroxide ratio of 0.15, and a pH of 3.2. This first component profile serves as an introductory guide, clearly defining which treatment unit should be sent to first for each type of wastewater.

[0032] A second-stage component mining process is conducted on historical treatment records to perform deeper data analysis. This process involves more targeted component mining based on the properties of the target pollutants and the actual needs of the wastewater. Simultaneously, treatment resource localization is performed, identifying the most frequently successfully treated pathways, including selecting different treatment methods, equipment, and required reagents, thereby forming a second-stage component profile.

[0033] Based on the first and second component maps, a treatment database is generated, including various types of wastewater and corresponding treatment resources. According to real-time water quality changes, the most suitable treatment scheme and resource allocation are automatically selected. By accessing historical data and performing high-dimensional component mining, the main pollutants and their characteristics in pesticide wastewater are accurately identified. Resource allocation is optimized based on the component maps, avoiding overtreatment and resource waste, making the treatment method more precise and efficient.

[0034] A dynamic planner that triggers the deployment of the microprocessor resource pool performs a first high-dimensional route planning on the chemical information fingerprint using entropy flow path dynamic planning, determines the first unit route, and executes water treatment based on the first unit route under dynamic aggregation-diversion by controlling the first multi-level routing automatic valve. The microprocessor resource pool is a distributed modular microprocessor unit.

[0035] Furthermore, this application also includes the following steps: taking the initial state of the first chemical information fingerprint and the final state of the second standard information fingerprint as planning objectives, and taking the minimum entropy increase resistance as the decision guide, a dynamic planning architecture is constructed by embedding a microprocessor resource pool; for the dynamic planning architecture, a first high-dimensional coarse training is performed to determine the first planning branch, and a second directional training is performed to determine the second planning branch; the dynamic planner is generated using the first planning branch and the second planning branch.

[0036] Specifically, the first chemical fingerprint, the initial state, is the chemical fingerprint of the pesticide production wastewater sample detected at the pipeline inlet by a non-specific high-dimensional sensing array. It represents the original, untreated state and is the starting point of the entire treatment process. The second standard fingerprint, the final state, is the target chemical fingerprint that the effluent must achieve at the discharge outlet after a series of treatments, meeting emission standards. This is typically clean, harmless, or compliant with emission standards, representing the endpoint and goal of the treatment process. The treatment objective is determined based on the initial chemical fingerprint and the desired final state of the water sample. By analyzing the difference between the initial and target states, optimized treatment objectives are set to guide the entire treatment process, ensuring that the final effluent meets environmental or emission standards.

[0037] Minimum entropy resistance is the core decision-making principle. Entropy increase is a concept borrowed from physics, referring to the natural process of degrading ordered, complex, high-molecular-weight toxic pollutants in wastewater (low-entropy state) into disordered, simple, harmless small molecules (high-entropy state). Minimum resistance means that when selecting a treatment path, the dynamic planner will prioritize the path with the lowest energy consumption, fastest reaction, and highest efficiency, i.e., the steepest descent line that achieves the pollutant degradation process.

[0038] By constructing a dynamic programming architecture and embedding a microprocessor resource pool, the system computes the optimal solution among all possible paths from the initial state to the final state. The microprocessor resource pool provides flexible computational and processing capabilities, enabling the selection of the most suitable treatment unit based on the specific characteristics of the wastewater.

[0039] For the dynamic programming architecture, a two-stage training process is employed using a vast database of historical processing records. The first stage is a high-dimensional coarse-grained training, which teaches the dynamic planner to immediately match the most likely successful initial treatment route (the first planning branch) upon seeing a macroscopic fingerprint of the influent. The second stage is a targeted training, which teaches the dynamic planner to flexibly adjust subsequent routes based on intermediate changes in water quality during the treatment process (the second planning branch). The first high-dimensional coarse-grained training, conducted during the planner construction phase, uses historical data to train the architecture in the first stage. It learns the relationship between the initial chemical fingerprint of the influent and the final successful path, aiming to establish macroscopic initial path selection rules for different influent types. The first planning branch, formed by the macroscopic decision-making module in the dynamic planner after the first high-dimensional coarse-grained training, is responsible for determining which treatment units(s) the wastewater should be routed to in the first step based on the initial state of the initial chemical fingerprint. The second targeted training, conducted during the planner construction phase, uses historical data for a second stage of refined training. Through more precise training, the treatment path and parameters are optimized to achieve the best treatment effect.

[0040] A dynamic planner is generated using the first and second planned branches. The optimal treatment path is selected based on the characteristics of different water samples. During wastewater flow, the treatment path is flexibly configured through distributed microprocessor units at the connections between pipes and automatic valves. These microprocessor units control diversion and convergence via valves to dynamically adjust the water flow. For example, if a portion of the wastewater is more difficult to treat, such as containing recalcitrant toxic substances, it is diverted to a more advanced treatment unit, while other easily biodegradable wastewater is treated through an aerobic treatment path.

[0041] By elevating path selection from manual experience-based judgment to scientific calculations based on historical big data and clearly defined optimization objectives—namely, minimizing entropy and resistance—the chosen route is ensured to be the optimal or near-optimal solution under current conditions. Through the coordination of the first and second planning branches, the dynamic planner not only initiates wastewater treatment effectively but also monitors the process, dynamically adjusting subsequent plans based on intermediate results, achieving true self-adaptation and precise processing.

[0042] Furthermore, this application also includes the following steps: inputting the chemical information fingerprint into the first planning branch of the dynamic planner, interacting with the processing database, calling the first component spectrum to match components with processing resources, and determining the first processing conditions; for the first processing conditions, making an optimal route planning decision with the minimum entropy increase resistance, and determining the route of the first unit.

[0043] Specifically, the dynamic planner, deployed in the microprocessor resource pool at the connection between the pipeline and the automatic valve, is triggered to input the chemical information fingerprint into the first planning branch. Entropy flow path dynamic planning is the core algorithm used by the dynamic planner. It treats the process of pollutant degradation as an entropy-increasing flow process. It processes the complex molecular structure of harmful pollutants, which has a high degree of order (i.e., low information entropy), into simple molecules in a disordered, high-entropy state, and plans a path that minimizes the resistance and maximizes the efficiency of this flow process.

[0044] The system interacts with the processing database on the first planning branch, calls the first component spectrum, and quickly matches the chemical information fingerprint with all historical fingerprint types stored in the first component spectrum. Once a match is successful, the type of wastewater is determined, and then the most effective first treatment conditions for this type of wastewater are obtained from the first component spectrum, including the preferred treatment unit and its initial operating parameters.

[0045] After the initial treatment conditions are determined, the dynamic planner makes optimization decisions based on the principle of minimizing entropy increase resistance, selecting the most energy-efficient and effective treatment path while minimizing resource waste and energy loss during the treatment process. For example, for highly toxic wastewater in small volumes, an advanced oxidation-anaerobic pathway is used to minimize energy and reagent consumption. For instance, comparing routes to unit A or unit B, the route that offers the best overall energy and time cost is selected as the first unit route. Essentially, this involves controlling which automatic valve opens to guide the wastewater to which designated modular microprocessor unit. The first unit route is the final, determined identifier of the first treatment unit the wastewater should go to, along with the control commands for its inlet valves.

[0046] During treatment, the wastewater status is continuously monitored, and the flow path is adjusted based on real-time feedback. Because the pollution characteristics of the water sample may change over time, a dynamic adjustment process is implemented, similar to converging within the same treatment unit and diverting the same stream to different treatment units. For example, if the concentration of certain pollutants in the water sample changes, the flow path is automatically adjusted to guide the wastewater to different treatment units, ensuring that the treatment process always follows the optimal path.

[0047] For example, the chemical fingerprint of wastewater A, measured by a non-specific high-dimensional sensing array, is: total organic carbon 22000 mg / L, 254 nm UV absorption, conductivity 15000 μS / cm, and a five-day biochemical oxygen demand (BOD) to chemical oxygen demand (COD) ratio of 0.18. The first planning branch of the dynamic planner compares this fingerprint with the first component spectrum in the treatment database and finds that the fingerprint has a 95% similarity to the high-load, recalcitrant, toxic wastewater category defined in the spectrum. The spectrum records show that the historically most successful first treatment condition for this type of wastewater is: routing it to treatment unit A1, setting the reaction temperature to 250°C, and the pressure to 7 MPa. Optimal routing planning based on minimizing entropy increase resistance indicates that although unit A1 has slightly higher start-up energy consumption, its treatment rate and thoroughness for this type of wastewater are far superior to unit A2. Considering the total entropy increase resistance of the entire degradation path, directly using A1 is the better choice, i.e., minimizing total resistance.

[0048] Through adaptive adjustment by a dynamic planner, water sample characteristics are identified in real time, and the optimal treatment path is selected. Optimization based on the principle of minimizing entropy and resistance reduces energy consumption and reagent dosage during treatment, saving resources and improving treatment efficiency.

[0049] Furthermore, this application also includes the following steps: identifying a first microprocessor unit based on the first unit routing; determining the state of the first microprocessor unit, and controlling the opening and closing of an automatic valve to allow water samples to flow into the first microprocessor unit through a pipeline for one-step processing; identifying a second microprocessor unit and determining its state and controlling the flow through an automatic valve, and performing microprocessor unit cyclic processing based on the first unit routing.

[0050] Furthermore, this application also includes the following steps: setting the standard state of the first microprocessor unit; checking whether the first microprocessor unit is in an idle state, and if not, performing time-delay control on the automatic valve; checking whether the first microprocessor unit is in a standard state, and if not, performing the restoration process of the unit's standard state.

[0051] Specifically, based on the selected first unit route, the first microprocessor unit that needs to process the water sample is identified. The water flow path is determined according to the first unit route, and the operating status of the first microprocessor unit is confirmed. The first microprocessor unit is the specific, distributed, modular processing unit specified by the first unit route determined by the dynamic planner. A status determination is performed on the first microprocessor unit, i.e., a remote diagnostic of the target microprocessor unit's current operating status, including idle state and standard state. The standard state is when the microprocessor unit is in its optimal standby state, and its internal environmental parameters have been pre-adjusted to standard values ​​suitable for processing a certain type of wastewater. The idle state is when the microprocessor unit is not currently performing a processing task, and its inlet automatic valve is closed, ready to immediately accept new processing tasks.

[0052] Define the standard state of the first microprocessor unit, which is the operating state that the microprocessor unit should achieve under ideal working conditions. For example, the standard state of the oxidation unit is a specific oxidant concentration and temperature to ensure the efficiency and effectiveness of the oxidation reaction. For instance, the standard state of the oxidation unit might require an oxygen concentration of 2 mg / L, a temperature of 25°C, and a specific chemical oxidant concentration.

[0053] The system determines whether the first microprocessor unit is idle. If not, time-delay control is activated, suspending the flow and waiting to prevent the mixing of different water qualities within the unit, which could reduce treatment efficiency. If it is idle, its internal environment is further checked to ensure it is in a standard state. If the first microprocessor unit is not in a standard state, a restoration process to the standard state is performed, such as adjusting temperature, pH, and adding reagents, to quickly restore it to its optimal ready state. Only when the unit simultaneously meets both the idle and standard state conditions is a command issued to open the corresponding automatic valve, precisely directing wastewater into the first microprocessor unit for one-step treatment. One-step treatment is the physical, chemical, or biological treatment process completed within the first microprocessor unit. The second microprocessor unit is the next treatment unit the wastewater needs to proceed to, following the complete path planned by the first unit.

[0054] When the first step of processing is nearing completion, the second microprocessor unit in the route is identified in advance, and the above-mentioned state determination and valve diversion control process is repeated for it. This cycle is repeated until the wastewater completes all the cycle treatments according to the planned path. Cyclic treatment refers to the process in which wastewater flows through each microprocessor unit in sequence according to the route sequence pre-planned by the dynamic planner, repeating the cycle of identification, state determination, and valve control for each unit until the entire treatment chain is completed. For example, if the first unit route is unit A1, its built-in sensor returns data showing a current temperature of 80℃, a current pressure of atmospheric pressure, and an inlet valve status of closed. Based on the closed inlet valve and no water flow, it is checked whether the unit is in an idle state. It is then checked whether the unit is in the standard state required for treating high-concentration wastewater, with a preset standard of 250℃ temperature and 7.0MPa pressure. The current state is far from meeting the standard. Immediately, a restoration process is executed: the heating system and booster pump are started to gradually increase the internal temperature and pressure of the reactor to 250℃ and 7.0MPa. Once the temperature and pressure reach standard conditions, the automatic valve connected to the unit's inlet is remotely opened, pumping in wastewater and initiating wet oxidation treatment. By real-time monitoring of the microprocessor unit's status and implementing time-delay control, water sample mixing issues are avoided, ensuring the high efficiency of the treatment process. Each microprocessor unit adaptively adjusts according to the characteristics of the water sample, ensuring the treatment process meets optimal operating conditions.

[0055] In summary, the dynamic planner initiates its entropy flow path dynamic planning algorithm, with the core objective of finding the optimal path with the least entropy resistance. It rapidly calculates and analyzes the received chemical fingerprint information, completes the first high-dimensional route planning, and outputs the decision result, i.e., the first unit route. This result is then sent to the execution unit, which controls the opening and closing combinations of the corresponding first multi-stage routing automatic valves in the pipeline network, much like switching tracks on a train, precisely guiding the water flow to the planned path. A dynamic convergence-diversion strategy is implemented, converging wastewater from different sources but with similar water quality characteristics into the same high-efficiency treatment unit for centralized treatment at a given time. Diversion has two meanings: first, distributing the same stream of wastewater sequentially to different units according to its treatment needs; second, distributing a stream of wastewater to different units for parallel treatment based on different pollutant characteristics. For example, other wastewater that also requires electrocatalytic oxidation at this time can be converged (convergence) and sent to the more powerful third unit; or easily degradable and difficult-to-degrade components in the current wastewater can be separated in subsequent steps (diversion) and sent to different units for specialized treatment. All these operations are performed on a resource pool composed of distributed modular microprocessor units, realizing a fundamental shift in the processing flow from a fixed pipeline to a flexible resource network.

[0056] Directional detection is performed using a multimodal sensor array to determine directional chemical information fingerprints, triggering a second directional routing plan based on a dynamic planner, determining the second unit route, and executing the second multi-level routing automatic valve-controlled water treatment, with output from the second pipeline end.

[0057] Furthermore, this application also includes the following steps: after completing the processing of the first unit route, performing the detection of directional chemical information fingerprint; importing the directional chemical information fingerprint into the second planning branch of the dynamic planner, and through interaction with the processing database, retrieving the second component spectrum to match the components with processing resources and determine the second processing conditions; and for the second processing conditions, performing the integration of the microprocessor unit to generate the second unit route.

[0058] Specifically, after the water sample undergoes preliminary treatment in the first processing unit, it is subjected to directional detection to generate a directional chemical fingerprint. After being processed by the first unit, the wastewater's water quality characteristics have changed. The directional chemical fingerprint, obtained through re-detection by a multimodal sensor array, represents its current intermediate state. The multimodal sensor array is a multifunctional sensing device capable of simultaneously detecting water samples from multiple dimensions. It can detect different pollutants and water quality characteristics, generating multimodal data and providing more comprehensive water quality information. The directional chemical fingerprint is the characteristic fingerprint of the water sample after processing by the first processing unit, reflecting the changes in the water sample after treatment, including the main chemical components, toxicity changes, and biodegradability.

[0059] The directed chemical fingerprint is imported into the second planning branch of the dynamic planner, and then interacts with the processing database to retrieve the second component spectrum. The directed chemical fingerprint is matched with various intermediate state fingerprints recorded in the second component spectrum to determine the most suitable second treatment condition for the current water quality, i.e., the next treatment plan for the current wastewater state, including which unit to route to and its operating parameters. For example, if the water sample still contains high levels of nitrogen and phosphorus, a suitable hydrolysis unit or aerobic unit is selected for further treatment.

[0060] Based on the second set of data mapping and the second treatment conditions, the microprocessor units are integrated. A microprocessor unit is a collection of multiple processing units, each performing a specific processing function. The processing unit most suitable for the current water sample's needs is selected, and resource pools are rationally allocated to form a second-unit route, determining the water sample's path in subsequent treatment stages. The water sample enters the appropriate microprocessor unit according to its characteristics to complete its specific treatment task. Entropy increase decisions are not performed here; instead, trajectory planning based on the processing order is directly applied to the resource pools corresponding to the processing resources. At this stage, the decision logic is relatively straightforward, requiring no complex entropy increase decisions, as the macroscopic path has already been determined in the first-unit route. The focus here is on precise fine-tuning based on the current state. Based on the determined treatment conditions, the microprocessor units are integrated, i.e., specific units that meet the requirements and are in a ready state are selected from the resource pool. A clear second-unit route instruction is generated, guiding the wastewater to the next treatment station.

[0061] By generating directional chemical fingerprints and making decisions using a dynamic planner, the most suitable treatment path is selected based on the different characteristics of the water sample, thus improving processing efficiency. By retrieving the second component spectrum and rationally integrating microprocessor units, the characteristics of processing resources and water samples are precisely matched, avoiding resource waste and ensuring treatment effectiveness.

[0062] Furthermore, this application also includes the following steps: after completing the processing of the second unit route, perform water component quality inspection to determine the treatment efficiency; generate a wastewater treatment chain based on the first unit route and the second unit route; determine whether the treatment efficiency meets the standard, and if it does, store the wastewater treatment chain in a database.

[0063] Furthermore, this application also includes the following steps: if the standard is not met, the non-compliant components are traced and located in the wastewater treatment chain to determine the cause of the abnormality; based on the cause of the abnormality, water recirculation and reprocessing are performed, and the cause of the abnormality in the wastewater treatment chain is identified and stored.

[0064] Specifically, after the water sample is treated in the second treatment unit, a water component quality test is performed to detect the concentration and toxicity of various pollutants in the water sample, thereby evaluating the effectiveness of the water treatment. For example, the concentrations of pollutants such as COD, nitrogen, phosphorus, and heavy metals, as well as water quality parameters such as pH and dissolved oxygen, are checked. The treatment efficiency is determined by detecting the concentration of residual pollutants in the water sample. The water component quality test involves analyzing the quality of the treated water sample, primarily focusing on various pollutants (such as organic matter, nitrogen, phosphorus, and heavy metals) and water quality indicators (such as pH and dissolved oxygen). Treatment efficiency refers to the efficiency of pollutant removal per unit of energy or reagent consumption during wastewater treatment.

[0065] Based on the various units the water sample traverses throughout the entire treatment process—namely, all units along the first and second unit routes—a wastewater treatment chain is generated. This wastewater treatment chain is a complete process encompassing all treatment units, recording information such as the operating status, treatment conditions, path, and operation time of each unit, including all treatment units, connecting pipes, and automatic valves.

[0066] If the treatment efficiency meets the predetermined standard, it means that the wastewater meets water quality standards after passing through each treatment unit, and the treatment effect is good. Relevant data from the entire wastewater treatment chain is saved in a database for subsequent querying and analysis. If the treated water sample does not meet the predetermined standard, it means that the treated water sample has not achieved the expected treatment effect, and the wastewater treatment chain is traced back. By tracing the wastewater treatment chain, it is possible to identify which treatment units failed to effectively remove the target pollutants or caused the water quality to fail to meet the standards, pinpoint the specific location of the problem with the non-compliant components, and find the cause. Anomalies are the reasons why the treatment effect fails to meet the standards during the wastewater treatment process, including treatment unit malfunctions, incorrect settings of treatment conditions, or changes in the characteristics of certain pollutants in the water sample.

[0067] Once the non-compliant components and the underlying causes of the anomaly are identified, a water recirculation and reprocessing measure is implemented. Untreated water samples are returned to upstream treatment units for secondary treatment or adjustment of treatment conditions. Water recirculation and reprocessing refers to the practice of returning untreated water samples to upstream units for further treatment when a treatment unit fails to meet water quality requirements. This improves treatment efficiency and prevents the direct discharge of substandard wastewater. Simultaneously, once the underlying cause of the anomaly is identified, the non-compliant components and the cause are recorded, identified, and stored in a database.

[0068] For example, in a wastewater treatment experiment, after wastewater sample M undergoes treatment and quality inspection, the total organic carbon (TOC) is reduced to 1000 mg / L, a removal rate of approximately 77.8%; the nitrogen concentration decreases from 80 mg / L to 20 mg / L; the phosphorus concentration decreases from 30 mg / L to 5 mg / L; and the five-day biochemical oxygen demand (BOD5) decreases from 200 mg / L to 40 mg / L. After performing quality inspection of the water components, each indicator of the water sample is evaluated, and the treatment efficiency is calculated. Based on these data, the treatment efficiency is calculated as follows: the TOC removal rate decreases from 4500 mg / L to 1000 mg / L, with a treatment efficiency of 77.8%; the nitrogen and phosphorus removal rates are 75% and 83.3%, respectively; and the ratio of five-day BOD5 to COD5 improves from 0.42 to 0.3. This indicates that the main pollutants in the water sample have been removed to a high degree. Based on the treated water quality data, the treatment efficiency was determined, assuming the predetermined efficiency standards included a total organic carbon removal rate of 70% and a five-day biochemical oxygen demand (BOD5) removal rate of at least 80%. According to the treatment results of wastewater A: the total organic carbon removal rate was 77.8%, meeting the standards; the five-day BOD5 removal rate was 80%, meeting the standards; and the nitrogen and phosphorus removal rates were 75% and 83.3% respectively, also meeting the standards. The treatment effect met the standards, and the data of this wastewater treatment chain was stored in a database, including the operating status, path selection, and treatment conditions of each treatment unit, to ensure subsequent querying and optimization. Suppose that in a certain treatment, the water sample's treatment efficiency did not meet the standards; for example, the phosphorus concentration failed to reach the expected removal rate (target removal rate 90%, actual phosphorus removal rate 83.3%, lower than the standard). Source tracing was performed to locate the non-compliant component, such as phosphorus, and the entire wastewater treatment chain was traced back to analyze possible causes. Analysis revealed that the residence time setting of the No. 3 upflow anaerobic sludge bed reactor was too short, leading to incomplete reaction. When the underlying cause of the anomaly was identified—insufficient reaction time in the anaerobic digestion unit—a water recirculation and reprocessing measure was implemented. Incompletely treated wastewater was recirculated to the upstream anaerobic digestion unit, and the hydraulic retention time was adjusted, for example, increasing it from 36 hours to 48 hours, to improve phosphorus removal. This anomaly was stored in a database, and the adjusted treatment conditions were recorded to provide a reference for subsequent optimization and to prevent similar problems from recurring. Through water component quality testing, treatment efficiency assessment, and anomaly identification, the efficiency of each treatment unit in the entire treatment chain was ensured, and measures were taken to improve treatment efficiency for non-compliant water samples.

[0069] In summary, the dynamic updating method for pesticide production wastewater treatment combined with component analysis provided in this application has the following beneficial effects: By employing a nonspecific high-dimensional sensor array, coarse detection of water samples is performed at the first pipe end to determine a chemical fingerprint based on a high-dimensional vector. This triggers a dynamic planner deployed in a microprocessor resource pool to perform a first high-dimensional route planning based on the chemical fingerprint using entropy flow path dynamic planning. This determines a first unit route, and water treatment is executed under dynamic convergence-diversion based on the first unit route by controlling a first multi-level routing automatic valve. The microprocessor resource pool consists of distributed modular microprocessor units. Then, directional detection is performed using a multimodal sensor array to determine a directional chemical fingerprint. This triggers a second directional route planning based on the dynamic planner, determining a second unit route and executing water treatment under the control of a second multi-level routing automatic valve, with output from the second pipe end. In other words, a nonspecific high-dimensional sensor array performs coarse detection of water samples, and a dynamic planner adjusts the water treatment strategy based on the chemical fingerprint. A multimodal sensor array performs directional detection to determine the directional chemical fingerprint, and a dynamic planner is used again to achieve more precise wastewater treatment, improving the efficiency and safety of pesticide production wastewater treatment.

[0070] Example 2: Based on the same inventive concept as the pesticide production wastewater treatment dynamic update method incorporating component analysis in Example 1, this application also provides a pesticide production wastewater treatment dynamic update system incorporating component analysis. Please refer to the appendix. Figure 2 The pesticide production wastewater treatment dynamic update system incorporating component analysis includes: The coarse detection module 11 is used to perform coarse detection on the water sample at the first pipe end using a non-specific high-dimensional sensing array to determine the chemical information fingerprint based on the high-dimensional vector. The first water treatment module 12 is used to trigger the dynamic planner deployed in the microprocessor resource pool to perform a first high-dimensional route planning on the chemical information fingerprint using entropy flow path dynamic planning, determine the first unit route, and perform water treatment under dynamic convergence-divergence based on the first unit route by controlling the first multi-level routing automatic valve. The microprocessor resource pool is a distributed modular microprocessor unit. The directional detection module 13 is used to perform directional detection through a multimodal sensing array to determine the directional chemical information fingerprint, trigger a second directional route planning based on the dynamic planner, determine the second unit route, and perform water treatment under the control of the second multi-level routing automatic valve, outputting from the second pipe end.

[0071] Furthermore, the dynamic update system for pesticide production wastewater treatment combined with component analysis also includes a processing database construction module. This module is further used for: retrieving historical processing records for pesticide production wastewater; performing first high-dimensional component mining and processing resource location based on the historical processing records to determine a first component map; performing second directional component mining and processing resource location based on the historical processing records to determine a second component map; and generating a processing database based on the first and second component maps.

[0072] Furthermore, the first water treatment module 12 in the pesticide production wastewater treatment dynamic update system combining component analysis is also used to: construct a dynamic planning architecture by embedding a microprocessor resource pool, taking the initial state of the first chemical information fingerprint and the final state of the second standard information fingerprint as planning objectives and the minimum entropy increase resistance as the decision guide; perform a first high-dimensional coarse training for the dynamic planning architecture to determine the first planning branch, perform a second directional training to determine the second planning branch; and generate the dynamic planner using the first planning branch and the second planning branch.

[0073] Furthermore, the first water treatment module 12 in the pesticide production wastewater treatment dynamic update system combined with component analysis is also used to: input the chemical information fingerprint into the first planning branch of the dynamic planner; interact with the processing database; call the first component map to match components with processing resources and determine the first processing conditions; and, for the first processing conditions, make an optimal route planning decision with the minimum entropy increase resistance to determine the first unit route.

[0074] Furthermore, the first water treatment module 12 in the pesticide production wastewater treatment dynamic update system combined with component analysis is also used to: identify the first microprocessor unit according to the first unit route; determine the status of the first microprocessor unit, and through automatic valve opening and closing control, allow water samples to flow into the first microprocessor unit through a pipeline to perform one-step processing; identify the second microprocessor unit and perform status determination and automatic valve diversion control to perform microprocessor unit cyclic processing based on the first unit route.

[0075] Furthermore, the first water treatment module 12 in the pesticide production wastewater treatment dynamic update system combined with component analysis is also used to: set the standard state of the first microprocessor unit; check whether the first microprocessor unit is in an idle state, and if not, perform time-delay control on the automatic valve; check whether the first microprocessor unit is in a standard state, and if not, perform the restoration process of the unit's standard state.

[0076] Furthermore, the directional detection module 13 in the pesticide production wastewater treatment dynamic update system combined with component analysis is also used for: after completing the processing of the first unit route, performing directional chemical information fingerprint detection; importing the directional chemical information fingerprint into the second planning branch of the dynamic planner, and through interaction with the processing database, retrieving the second component spectrum to match components with processing resources and determine the second processing conditions; and for the second processing conditions, performing microprocessor integration to generate the second unit route.

[0077] Furthermore, the directional detection module 13 in the pesticide production wastewater treatment dynamic update system combined with component analysis is also used for: after completing the processing of the second unit route, performing water component quality inspection to determine the treatment efficiency; generating a wastewater treatment chain based on the first unit route and the second unit route; determining whether the treatment efficiency meets the standard, and if it does, storing the wastewater treatment chain in a database.

[0078] Furthermore, the directional detection module 13 in the pesticide production wastewater treatment dynamic update system combined with component analysis is also used for: if the standard is not met, tracing and locating the non-compliant components in the wastewater treatment chain to determine the abnormal cause; and, based on the abnormal cause, performing water recirculation and reprocessing treatment, and storing the abnormal cause identification of the wastewater treatment chain.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The method and specific examples of dynamic updating of pesticide production wastewater treatment combined with component analysis in Example 1 are also applicable to the dynamic updating system of pesticide production wastewater treatment combined with component analysis in this embodiment. Through the foregoing detailed description of the method of dynamic updating of pesticide production wastewater treatment combined with component analysis, those skilled in the art can clearly understand the dynamic updating system of pesticide production wastewater treatment combined with component analysis in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0081] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A dynamic updating method for pesticide production wastewater treatment based on component analysis, characterized in that, include: A nonspecific high-dimensional sensing array is used to perform coarse detection on the water sample at the first pipe end to determine the chemical information fingerprint based on the high-dimensional vector. The nonspecific high-dimensional sensing array is an array composed of multiple sensors, which is used to simultaneously collect different physical and chemical properties in the water sample from multiple dimensions. A dynamic planner that triggers the deployment of the microprocessor resource pool performs a first high-dimensional route planning on the chemical information fingerprint using entropy flow path dynamic planning, determines the first unit route, and executes water treatment based on the first unit route under dynamic convergence-divergence by controlling the first multi-level routing automatic valve. The microprocessor resource pool is a distributed modular microprocessor unit that can select the most suitable treatment unit according to the different characteristics of the wastewater. The entropy flow path dynamic planning treats the pollutant degradation process as an entropy-increasing flow process. It transforms the complex and highly ordered molecular structure of harmful pollutants into simple molecules in a disordered and high-entropy state, and plans the path that minimizes the resistance and maximizes the efficiency of this flow process. The dynamic convergence-diversion strategy involves collecting wastewater from different sources but with similar water quality characteristics into the same efficient treatment unit for centralized treatment at a certain time. The multimodal sensor array performs directional detection to determine the directional chemical information fingerprint, triggers the second directional routing planning based on the dynamic planner, determines the second unit route and executes the second multi-level routing automatic valve control water treatment, and outputs from the second pipeline end. The multimodal sensor array is a multifunctional sensing device that can detect water samples from multiple dimensions simultaneously and generate multimodal data. The directional chemical information fingerprint is the water sample feature fingerprint after being processed by the first processing unit. Before triggering the deployment of the microprocessor resource pool, the construction of the dynamic planner includes: With the initial state of the first chemical information fingerprint and the final state of the second standard information fingerprint as the planning objectives, and with the minimum entropy increase resistance as the decision guide, a dynamic planning architecture is constructed by embedding a microprocessor resource pool. The minimum entropy increase resistance means that when the dynamic planner selects a processing path, it will prioritize the path with the lowest energy consumption, the fastest response, and the highest efficiency. For the dynamic programming architecture, a first high-dimensional coarse training is performed to determine a first planning branch, and a second directional training is performed to determine a second planning branch. The first high-dimensional coarse training is a first-stage training of the architecture using historical data when building the planner, and the second directional training is a second-stage fine training using historical data when building the planner. The dynamic planner is generated using the first planning branch and the second planning branch.

2. The method for dynamically updating pesticide production wastewater treatment combined with component analysis as described in claim 1, characterized in that, The method further includes: For pesticide production wastewater, historical treatment records can be retrieved; Based on the historical processing records, the first high-dimensional component is mined and processed to locate resources and determine the first component map. Based on the historical processing records, the second directional component mining and processing resource location is performed to determine the second component map; A processing database is generated based on the first component spectrum and the second component spectrum.

3. The method for dynamically updating pesticide production wastewater treatment combined with component analysis as described in claim 2, characterized in that, Performing a first high-dimensional routing plan on the chemical information fingerprint includes: The chemical information fingerprint is input into the first planning branch of the dynamic planner. By interacting with the processing database, the first component spectrum is called to match the components with processing resources and determine the first processing conditions. Based on the first processing condition, the optimal route planning decision is made to minimize entropy increase resistance, and the route of the first unit is determined.

4. The method for dynamically updating pesticide production wastewater treatment based on component analysis as described in claim 3, characterized in that, Performing water treatment based on dynamic convergence-diversion using the first unit route includes: Based on the routing of the first unit, identify the first microprocessor unit; The state of the first microprocessor unit is determined, and the water sample is fed into the first microprocessor unit through the pipeline by controlling the opening and closing of the automatic valve for one-step processing. The second microprocessor unit is identified and its status is determined and automatic valve flow control is performed. The microprocessor unit cyclic processing based on the routing of the first unit is executed. The cyclic processing refers to the process in which wastewater flows through each microprocessor unit in sequence according to the routing sequence pre-planned by the dynamic planner. The cycle of identification, status determination and valve control is repeated for each unit until the entire processing chain is completed.

5. The method for dynamically updating pesticide production wastewater treatment combined with component analysis as described in claim 4, characterized in that, Determining the state of the first microprocessor unit includes: The first microprocessor unit is set to a standard state, which is the microprocessor unit in the optimal standby state, and its internal environmental parameters have been pre-adjusted to standard values ​​suitable for treating a certain type of wastewater. Check whether the first microprocessor unit is in an idle state; if not, perform time-delay control on the automatic valve. Check whether the first microprocessor unit is in standard state; if not, perform the restoration process of the unit in standard state.

6. The method for dynamically updating pesticide production wastewater treatment combined with component analysis as described in claim 2, characterized in that, Triggering a second directional route planning based on a dynamic planner includes: After completing the routing of the first unit, the detection of directional chemical information fingerprints is performed; The directional chemical information fingerprint is imported into the second planning branch of the dynamic planner. By interacting with the processing database, the second component spectrum is retrieved to match the components with processing resources and determine the second processing conditions. For the second processing condition, the microprocessor units are integrated to generate the second unit route.

7. The method for dynamically updating pesticide production wastewater treatment combined with component analysis as described in claim 1, characterized in that, After output from the second pipe end, it includes: After completing the routing of the second unit, water components are tested to determine the treatment efficiency. A wastewater treatment chain is generated based on the first unit route and the second unit route. Determine whether the treatment efficiency meets the standard. If it does, store the wastewater treatment chain in a database.

8. The method for dynamically updating pesticide production wastewater treatment combined with component analysis as described in claim 7, characterized in that, If the standards are not met, the non-compliant components will be traced and located in the wastewater treatment chain to determine the cause of the abnormality. Based on the aforementioned abnormal causes, the water recirculation process is restarted, and the abnormal causes are identified and stored in the wastewater treatment chain.

9. A dynamic updating system for pesticide production wastewater treatment incorporating component analysis, characterized in that: The step of implementing the pesticide production wastewater treatment dynamic update method combining component analysis according to any one of claims 1 to 8, wherein the pesticide production wastewater treatment dynamic update system combining component analysis comprises: The coarse detection module is used to perform coarse detection on the water sample at the first pipe end using a non-specific high-dimensional sensing array to determine the chemical information fingerprint based on the high-dimensional vector. The non-specific high-dimensional sensing array is an array composed of multiple sensors, used to simultaneously collect different physical and chemical properties in the water sample from multiple dimensions. The first water treatment module is used to trigger the dynamic planner deployed by the microprocessor resource pool, and to perform a first high-dimensional route planning on the chemical information fingerprint using entropy flow path dynamic planning, determine the first unit route, and perform water treatment based on the first unit route under dynamic convergence-divergence by controlling the first multi-level routing automatic valve. The microprocessor resource pool is a distributed modular microprocessor unit that can select the most suitable treatment unit according to the different characteristics of the wastewater. The entropy flow path dynamic planning treats the pollutant degradation process as an entropy-increasing flow process. It transforms the complex and highly ordered molecular structure of harmful pollutants into simple molecules in a disordered and high-entropy state, and plans the path that minimizes the resistance and maximizes the efficiency of this flow process. The dynamic convergence-diversion strategy involves collecting wastewater from different sources but with similar water quality characteristics into the same efficient treatment unit for centralized treatment at a certain time. The directional detection module is used to perform directional detection through a multimodal sensor array, determine the directional chemical information fingerprint, trigger the second directional routing planning based on a dynamic planner, determine the second unit route and execute the second multi-level routing automatic valve control water treatment, and output from the second pipeline end. The multimodal sensor array is a multifunctional sensing device that can detect water samples from multiple dimensions simultaneously and generate multimodal data. The directional chemical information fingerprint is the water sample feature fingerprint after being processed by the first processing unit. The first water treatment module is also used for: With the initial state of the first chemical information fingerprint and the final state of the second standard information fingerprint as the planning objectives, and with the minimum entropy increase resistance as the decision guide, a dynamic planning architecture is constructed by embedding a microprocessor resource pool. The minimum entropy increase resistance means that when the dynamic planner selects a processing path, it will prioritize the path with the lowest energy consumption, the fastest response, and the highest efficiency. For the dynamic programming architecture, a first high-dimensional coarse training is performed to determine a first planning branch, and a second directional training is performed to determine a second planning branch. The first high-dimensional coarse training is a first-stage training of the architecture using historical data when building the planner, and the second directional training is a second-stage fine training using historical data when building the planner. The dynamic planner is generated using the first planning branch and the second planning branch.

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