116 results about "Parallel running" patented technology

The invention relates to a hot update control method and system for an industrial PLC (Programmable Logic Controller) of a microkernel operating system, belonging to the field of industrial controllers. A shadow instance is created by an isolated address space and is bound with a shadow I / O (Input / Output) mirror image and a shadow communication stack; capturing a containment point in the scanning period, wherein the containment point is located between input sampling completion and output submission; acquiring running state information from the current network instance, converting the running state information, and transmitting the converted running state information to the shadow instance; pre-operating the shadow instance in the isolation channel in an accelerated or constant-speed manner, and switching the I / O channel from the current network instance to a shadow I / O mirror image of the shadow instance at the next restraint point if the verification is passed; and continuing to run in parallel for a certain period, unloading the old instance if the continuous running is qualified, and executing rollback if an exception occurs. Depending on a microkernel operating system, shadow instances are drilled and switched under the controlled priority, the real-time performance and certainty are met, the time delay defect of periodic gap switching is overcome, and updating and switching are completed in a single scanning period or a plurality of scanning periods in an atomic mode.

The invention discloses a robot application secondary development method supporting user behavior pattern self-learning, and relates to the field of robot application development, and the method specifically comprises the following steps: S1, data collection and treatment; s2, performing behavior abstraction; s3, mode learning; s4, performing double-domain scheduling during operation; s5, performing mode capitalization; and S6, low code reuse. According to the robot application secondary development method supporting user behavior mode self-learning, during operation, a double-domain isolation framework physically isolates a safety domain from a learning domain, and the situation that key operation of a robot is disturbed in the learning process is avoided; the scheduling algorithm combining the SMP and the EDF and a real-time monitoring mechanism can accurately control core indexes such as thread scheduling delay and interrupt response time, ensure that key tasks such as robot motion control and emergency fault processing are executed preferentially, meanwhile, parallel operation of behavior learning and execution is achieved, the strict requirement for real-time performance of an industrial scene is met, and the real-time performance of the industrial scene is improved. And the operation safety of the robot is ensured.

The invention discloses an intelligent main plan scheduling system oriented to the chemical industry, and the system comprises a data center layer which is used for aggregating order, equipment, materials, inventory and process constraint data; the MPS optimizer operates the tabu search module, the step-counting hill-climbing module and the flood algorithm module in sequence or in parallel in the same optimization period, and automatically switches algorithms under the following conditions: when the continuous iteration improvement rate is lower than a set threshold value, the tabu search is switched to step-counting hill-climbing, and the step-counting hill-climbing is switched to step-counting hill-climbing; when the local search step number is not improved, starting the flood algorithm to re-initialize the candidate solution; and the process scheduler is used for splitting the coarse-grained plan output by the MPS optimizer into minute-level process schedules, and transmitting the execution deviation back to the data center layer in real time through a bidirectional feedback loop so as to trigger incremental rearrangement. According to the intelligent main plan scheduling system for the chemical industry, through a hybrid algorithm dynamic switching mode, the thousand-level order rearrangement time is greatly shortened, and the plan response speed is remarkably improved.

The invention discloses a DCS system IO module channel testing device, and relates to the technical field of industrial automation equipment detection. The device comprises an IO module point inspection instrument and an upper computer, the IO module point inspection instrument is in communication connection with the upper computer, a plurality of sets of independent test modules capable of running in parallel are arranged in the IO module point inspection instrument, and each test module comprises a controller, an excitation source circuit, a signal acquisition circuit and a matrix switch circuit and corresponds to one slot. And the test module establishes a signal path with the IO module connected to the slot through the matrix switch circuit. Wherein a controller in the test module can automatically identify the type of the IO module to be tested, dynamically configure an excitation source circuit and a signal acquisition circuit which are matched with the IO module to be tested, and realize automatic and high-precision detection of various types of IO modules. According to the device, the point inspection efficiency of the IO module of the DCS system can be greatly improved by integrating a multi-channel signal output function and a signal detection function.

The invention discloses a multi-agent parallel planning and execution-oriented dynamic task cooperation method and system. The method comprises the following steps of: constructing agents containing planning threads and execution threads which are independent and run in parallel, configuring a callable large language model and a readable and writable centralized memory system for the planning threads, and configuring a callable tool library for the execution threads; a planning thread and an execution thread of each agent establish an exclusive communication link, and a centralized memory system configures a file lock mechanism; the planning thread obtains latest environment information, team communication logs and execution history from the centralized memory system at each time step, calls a large language model to generate a formatted tool library API instruction as a next plan, independently judges whether an interrupt mechanism is triggered or not by the large language model, generates an interrupt signal if the interrupt mechanism is triggered, and sends the interrupt signal to the centralized memory system; writing the tool library API instruction into an action buffer area; the execution thread continuously obtains the latest tool library API instruction from the action buffer area and calls a tool library to execute a corresponding task; if an interrupt signal sent by the planning thread is received through the exclusive communication link, the current task is terminated immediately, and after occupied resources are released, the latest instruction of the buffer area is loaded for execution; and the centralized memory system updates contents at each time step, including an environment observation result returned by the tool library, a communication log between the intelligent agents and a task execution history of each intelligent agent, for all planning threads to access.

The invention provides a BMC optimization method and system of an AI server, electronic equipment and a storage medium, and relates to the technical field of computers. The BMC optimization method of the AI server is applied to a BMC optimization system of the AI server and comprises the steps that a first kernel and a second kernel are started in response to a starting instruction for the AI server, after the first kernel is started, a basic monitoring subsystem of the AI server is taken over, and after the second kernel is taken over, a BMC optimization subsystem of the AI server is started; continuously collecting operation data of a fan and a power supply in the AI server and controlling the fan and the power supply; after the second kernel is started, taking over a global monitoring subsystem of the AI server, performing global monitoring on the AI server, and performing strategy optimization based on a monitoring result; in the parallel running process of the first kernel and the second kernel, if the target kernel detects that the other kernel is abnormal, the target kernel takes over the monitoring task of the abnormal kernel. Therefore, the starting window period of a traditional single-kernel AI server can be eliminated.

A computer program product provides program instructions executable by a processor to cause the processor to perform operations. The operations include identifying a first inference workload instance using a first inference model and a second inference workload instance using a second inference model and identifying whether the first and second inference models have affinity for being run concurrently on a core processing unit. The first and second inference models have affinity if the first and second inference workload instances can run concurrently on the core processing unit without causing either of the first and second inference workload instances to experience latency above a predetermined limit. The operations further include causing the first and second inference workload instances to be run concurrently on the core processing unit if the first and second inference models have been identified to have affinity for being run concurrently on the core processing unit.

The invention discloses a front-end system reconfiguration version control method and device, equipment, a storage medium and a product, and relates to the technical field of system reconfiguration, and the method comprises the steps: receiving a first access request of a user for a target menu in a parallel operation stage of an old system and a new system, and obtaining the identity information of the user; judging whether an access system corresponding to the first access request is the new system or not based on the identity information and a version mapping table; when it is judged that the access system is the new system, a micro-front-end frame is called to load sub-application static resources, corresponding to the target menu, in the new system to a browser for rendering, and a special area page is obtained, and forwarding an interface calling request triggered by the user in the special area page to a new back-end service through a new system gateway. According to the invention, the technical risk and integration complexity of large-scale front-end system reconstruction can be reduced.

According to the electroencephalogram acquisition and signal processing system and the information processing method provided by the invention, the control module is arranged, so that an upper computer is prevented from directly interacting with massive electroencephalogram acquisition data, and a data processing transfer station and a temporary control core are provided, so that the processing efficiency of electroencephalogram signals is improved. Furthermore, the control module comprises a processing unit and a programmable logic unit, the programmable logic unit can control a plurality of acquisition front ends in parallel to realize high-sampling-rate and high-passband signal acquisition, and the processing unit plays a role in connection in the whole electroencephalogram acquisition and signal processing system and can help the upper computer to control and manage the programmable logic unit, so that the accuracy of the electroencephalogram acquisition and signal processing system is improved. And meanwhile, a part of functions which can be realized without continuous data transmission are shared. The control module is provided with a programmable logic unit and a processing unit to realize parallel operation of two independent threads, namely a control flow thread and a data flow thread, so that the control flow and the data flow of the whole system are separated, and the data processing efficiency and the stability of the system are improved.

The embodiment of the invention provides an agent task processing method and system. According to the method, an execution module and a verification module are arranged in an intelligent agent. Wherein the execution module runs in the main execution link so as to reasone and execute an operation sequence according to an instruction input by a user, and the verification module runs in parallel in a bypass monitoring thread or process independent of the main execution link so as to monitor interface state data in the process that the execution module executes the operation sequence in real time. In addition, the verification module performs verification evaluation based on the interface state data and the user input instruction to verify whether the evaluation operation sequence is correctly evolved along the target task direction corresponding to the user input instruction. And when the verification evaluation indication is passed, the execution module performs normal reasoning and executes the operation sequence. And when the verification evaluation instruction is not passed, the verification module generates a correction instruction and triggers the execution module to correct the operation sequence based on the correction instruction.

The invention provides a power grid micro-front-end construction method and architecture based on a qiankun framework. The method comprises the following steps: starting a main application of the qiankun micro-front-end framework; registering a global state in the main application, and monitoring a global state change event; when one sub-application loaded through routing matching receives a service request corresponding to the other sub-application, triggering a global state change event, and transmitting parameters about calling the service request of the other sub-application into the global state change event; after monitoring the global state change event, the main application judges whether the other sub-application is mounted in a hidden container or not, and if not, manual mounting is carried out; and the other sub-application acquires the global state change event from the main application and then executes a corresponding service function. According to the method, parallel operation of the sub-applications and cross-sub-application communication can be realized in the power grid micro-front end based on the qiankun framework, and service multiplexing is realized while the independence of the sub-applications is kept.

The invention belongs to the technical field of spiking neural networks. The invention provides a construction method of a reversible pulse neural network supporting parallel computing. According to the embodiment of the invention, the reversible calculation structure is introduced, the neuron state of an intermediate layer does not need to be explicitly stored in the forward propagation process of the network, and the reconstruction of the activation value can be realized through the reversible mapping relation in the backward propagation stage. In the design stage of the reversible structure, hardware mapping characteristics are fully considered, and forward calculation and reverse reconstruction processes of reversible mapping are simultaneously incorporated into a parallel execution framework of a configurable calculation array. Through collaborative design of a data path and control logic, forward propagation and reverse reversible reconstruction can be efficiently and parallelly operated in the same array, and inter-stage calculation idle time and data waiting time are remarkably shortened. On the premise of ensuring reversibility, streamlined and parallel execution of forward and reverse calculation is realized, and the throughput rate and the training speed of the system are integrally improved.

The embodiment of the invention discloses a vehicle and an upgrading method thereof, a server and an upgrading file processing method thereof, a medium and a program product, and the method comprises the steps: obtaining an upgrading file package under the condition of receiving an upgrading instruction, and building a first thread and at least one second thread based on grouping information; upgrading the vehicle based on the first compressed upgrade package in the first thread; the vehicle is subjected to serial upgrading based on the second compressed upgrading package in the corresponding second group in each second thread, and the first thread and at least one second thread run in parallel. According to the embodiment of the invention, the longest upgrading task in the system is ensured not to block the execution of other upgrading tasks, the longest upgrading task is continuously operated as an independent parallel unit, and other threads can synchronously promote intra-group upgrading by fully utilizing calculation and I / O (Input / Output) resources, so that the total time consumption of remote online upgrading is greatly shortened globally, and the upgrading efficiency is improved. And the upgrading efficiency is improved.

The invention relates to a hydrogen production system 4E multi-objective optimization method based on an NSGA-II-MOGWO parallel mixing mechanism. According to the method, a primary population is generated firstly, and global operator parameters and corresponding strategies of NSGA-II and MOGWO are set. System design and operation decision variables are formalized into vectors, and a feasible region is defined by inequality / equality constraints. Then simulating each candidate solution by calling the system hourly to obtain a 4E fitness function value; in the optimization process, NSGA-II and MOGWO are operated in parallel in each generation, and excellent individuals are exchanged according to a preset period to give consideration to exploration and utilization; and after the parent and the two branch filial generations are combined, a GPR is adopted to establish a proxy model for batch screening of candidates for real evaluation and proxy retraining. And finally, through rapid non-dominated sorting and congestion degree selection and external file maintenance, outputting a high-quality Pareto frontier solution and a plurality of representative configuration schemes, and visualizing the high-quality Pareto frontier solution and the representative configuration schemes.

Enhancing data processing is provided. A plurality of derivative application instances is generated to run a plurality of parallel jobs based on an image of an instance of an application providing a service corresponding to a data processing request. One derivative application instance is generated for each respective job of the plurality of parallel jobs to run the plurality of parallel jobs to meet defined data processing performance objectives. The plurality of parallel jobs is run on the plurality of derivative application instances at the same time in parallel to increase data processing throughput and decrease an amount of time and resources needed to fulfill the data processing request. Each job of the plurality of parallel jobs retrieves a particular chunk of a dataset corresponding to the data processing request from a database to process that particular chunk of the dataset to generate a sub-result of the data processing request.

The invention discloses a container trusted measurement and verification method in a confidential environment, and belongs to the technical field of information security. According to the method, a dual-system architecture in which calculation and protection components run in parallel is designed, TCMTPCM and vTPCM management modules and a trusted software base TSB are constructed and deployed in the protection components, protection and hardware trusted roots are provided for running containers in a confidential virtual machine in a confidential environment, and integrity measurement judgment of key content is carried out in the starting and running processes of the containers. Confidentiality, integrity and credibility of a user service container provided in the cloud service are realized. And finally, a dual verification mechanism for providing a service container is introduced, and a remote verification platform not only needs to verify the identity and integrity of a confidential virtual machine where the container is located, but also needs to verify the integrity of the container and the integrity of the container during operation based on the vTPCM in the virtual machine. The method can get rid of absolute dependence of a user on single hardware trust of a hardware trust provider, and integrity measurement and verification are carried out during container operation in a confidential environment.

The invention relates to the technical field of computers, and discloses an intelligent broadcast parallel control method and system, and the method comprises the steps: carrying out the broadcast instance creation and exclusive parameter configuration operation based on a broadcast instance array and an instance creation demand; according to the first demand application scene information, determining second target broadcast instances exclusively running in the first demand application scene from the broadcast instance array, and controlling each second target broadcast instance to execute activation, running and stop operations in parallel; and determining a third target broadcast instance meeting a preset expired recovery condition from the broadcast instance array, and performing destruction and resource release operation on the third target broadcast instance. It can be seen that by implementing the method and the system, broadcast instance parallel management and broadcast instance life cycle control in multiple scenes can be realized, the accuracy, pertinence and reliability of parallel operation control of multiple broadcast instances are improved, the equipment discovery efficiency is improved, effective utilization and timely recovery of resources are ensured, and the system stability is improved.

The invention discloses a multi-scenario operation method based on a single-node memory database and multi-Agent calculation, and belongs to the field of combat simulation, and the method comprises the following steps: 1, starting a scenario editing tool, operating a multi-scenario editing process, and creating a memory database; secondly, the scenario is edited, and scenario entity information is stored in the memory database; thirdly, storing a multi-scenario file; 4, starting a scenario operation monitoring tool, operating a plurality of scenario operation monitoring processes, and creating a memory database; 5, loading the scenario file, and creating a data blackboard by the memory database based on the scenario file; sixthly, advancing each set of simulation time according to the step length, and managing the data blackboard; seventhly, in the step length period, multi-Agent simulation calculation is executed, and a calculation result is updated to the data blackboard; and 8, judging whether the scenario is ended or not, and if not, turning to the step 6. According to the method, single-node multi-scenario parallel operation is realized by adopting a memory database technology and a multi-Agent technology, and the method has the advantages of simplicity and high efficiency.

This application provides a method, apparatus, and system for downloading control strategies for coal-fired power plants. The method includes collecting multi-source production data from the power plant, performing heterogeneous adaptation and standardization processing to generate standard data frames; loading the control strategy to be implemented into a shadow running unit, running it in parallel based on the standard data frames, and obtaining verification results; performing gray-scale downloading of the control strategy to be implemented under preset gating conditions; and real-time monitoring and execution of tiered rollback operations in case of abnormal situations. This application solves the problem of differences between offline simulation and the real operating environment by setting up a shadow running unit to run the control strategy to be implemented in parallel; by performing gray-scale downloading, it avoids the sudden operational risks caused by full write; furthermore, by real-time monitoring of the operating status and execution of tiered rollback operations, it can automatically and quickly handle abnormal situations, improving the safety and reliability of downloading control strategies for coal-fired power plants and reducing operational risks.

The invention relates to a method (400) for parallel running of at least one first quantum circuit and a second quantum circuit on a quantum computer, wherein the quantum computer comprises a qubit arrangement which is functionally divided into at least a first qubit group and a second qubit group, wherein the first qubit group and the second qubit group are spatially separated from one another, wherein the method (400) comprises the following steps: providing (406) a list including temporal delay values; Providing (401) a mapping of the first quantum circuit to the first qubit group and providing a mapping of the second quantum circuit to the second qubit group, wherein the first quantum circuit and / or the second quantum circuit are time-delayed by means of the delay values; initializing (402) the quantum computer, comprising: initialising the first qubit group and initialising the second qubit group; parallel running (403) of the first quantum circuit with the first qubit group to generate (404) first measurement results of the first qubit group and parallel operation of the second quantum circuit with the second qubit group to generate (404) second measurement results of the second qubit group; providing (405) the first measurement results and the second measurement results.

The invention relates to the technical field of computers, and discloses a sparse representation driven large-scale data integration clustering method and system, a terminal and a storage medium. The method comprises the steps that in a distributed computing environment, a plurality of basic clustering devices run in parallel, a plurality of basic clustering results are generated, and each basic clustering device conducts sparse representation clustering according to a representative point set selected from an original data set; and fusing the plurality of basic clustering results to generate a final clustering result. Through distributed parallel processing, the computing efficiency and expandability are improved, and efficient processing of mass data is supported; the accuracy and robustness of a clustering result are enhanced by utilizing a group wisdom effect; and resource utilization is optimized through localization processing and non-iterative fusion, so that energy consumption is reduced. The method realizes collaborative improvement in efficiency, accuracy and resource optimization, and is suitable for big data application scenes such as anomaly detection, image segmentation and the like.

This disclosure provides a BMC optimization method, system, electronic device, and storage medium for an AI server, relating to the field of computer technology. The BMC optimization method for an AI server, applied to a BMC optimization system for an AI server, includes: responding to a startup command for the AI ​​server, a first kernel and a second kernel start; after the first kernel completes startup, it takes over the basic monitoring subsystem of the AI ​​server, continuously collecting operating data of the fans and power supply in the AI ​​server and controlling the fans and power supply; after the second kernel completes startup, it takes over the global monitoring subsystem of the AI ​​server, performs global monitoring of the AI ​​server, and optimizes strategies based on the monitoring results; during the parallel operation of the first and second kernels, if the target kernel detects an anomaly in the other kernel, the target kernel takes over the monitoring tasks of the anomaly kernel. This eliminates the startup idle period of traditional single-core AI servers.

The application belongs to the technical field of processors, and provides a verification method and system of a RISC-V out-of-order superscalar processor, a test program is generated based on a random instruction generator of a RISC-V instruction set, and the test program is run in parallel by an out-of-order superscalar processor and an instruction set simulator; in the running process of the out-of-order superscalar processor, retired instruction information is sequentially extracted from a reorder buffer thereof, and corresponding instruction execution results are read from a physical register file according to the information of the retired instruction, a check information queue table item of sequential execution is generated, and is stored into a check information queue; through a preset interface, the instruction set simulator is controlled to run in single step, and sequential submission results corresponding to the check information queue table item are extracted and stored into a submission result queue; the table items in the check information queue and the submission result queue are compared one by one, if the comparison is consistent, the verification is continued, and if the comparison is inconsistent, an error instruction is located. The efficiency of verification is improved.

The invention relates to the technical field of computers, and discloses a task processing method, a readable medium, electronic equipment and a program product. In the method, core characteristic parameter values of different physical cores in a processor are pre-determined. Then, when the affinity of the physical core running two tasks in parallel through two threads is calculated, the core characteristics of the physical core are quantified into core characteristic parameter values, and the core characteristic parameter values participate in affinity calculation; and determining a corresponding execution strategy based on an affinity calculation result. When the affinity of the same task group on other physical cores needs to be calculated, the operation overhead of each task in a single-thread mode does not need to be calculated again, and only the core characteristic parameter value in the calculation formula needs to be modified and then the operation overhead is calculated, so that the calculation resources can be reduced.

This invention relates to the field of new energy grid-connected control technology, and particularly to a method and apparatus for identifying control modes and parameters of a doubly-fed induction generator (DFIG) converter. The method involves applying small-amplitude step or pseudo-random voltage disturbances at the turbine terminal or grid connection point, collecting three-phase voltage and three-phase current data at the grid connection point, and obtaining the input vector and measurement output vector in a synchronous rotating coordinate system through Park transformation. An augmented state-space model library containing multiple potential control modes is established, and the PI parameters to be identified are incorporated into the augmented state. Unscented Kalman filters are run in parallel for each model to estimate the state / parameters, calculating the innovation residuals and their covariance. Based on Bayesian inference, the posterior probabilities of each model are recursively updated and normalized. The model with the highest probability is selected to determine the control mode, and the corresponding parameter estimates are output. This method does not require preset modes, improves identification accuracy and parameter physical consistency during mode switching or critical operating conditions, and facilitates stability analysis and operating condition migration. It has wide applicability.

The application discloses a multi-core inter-core real-time communication system and method, which comprises a hardware layer, a driver layer and a communication layer. Firstly, the hardware layer is used for managing and classifying a plurality of processors, and corresponding memory spaces are divided according to the classification results, and the shared memory is used to realize the access of the processors to the shared resources. Then, the driver layer is used for loading and managing an RPMsg driver program on a virtual hardware platform provided by the hardware layer, registering a driver device to provide data storage services, creating a driver component to provide data transmission services, and providing driver services for the communication layer through a driver calling interface. Finally, the communication layer is used for calling a driver interface function to manipulate an RPMsg driver module, realizing the receiving and transmitting operations of data, and establishing an inter-core logical connection through an RPMsg channel, so as to realize the inter-core communication of the multi-core. The inter-core communication technology based on the RPMsg channel is adopted, and a solution is provided for solving the parallel running and collaborative scheduling of the plurality of processors.

A method and apparatus for adjusting computing resources and an intelligent driving device are provided. The computing resources include a dedicated computing unit and b shared computing units that run in parallel, and the a dedicated computing units are allocated to m service domains. The method includes the steps of: acquiring first usage data for each of the k service domains (S310); determining a first number of computing units required by a target service domain within the k service domains based on the first usage data for each of the k service domains (S320), where the k service domains are one or more of the m service domains; determining a third number of shared computing units to be allocated to the target service domain based on the first number and a second number of dedicated computing units allocated to the target service domain among the a dedicated computing units (S330); and allocating the third number of shared computing units to the target service domain (S340). According to this method, the computing resources within the target service domain can be adjusted based on user usage habits, thereby ensuring the service processing capacity of the target service domain.

Disclosed is a data processing apparatus (10) for enhancing, in particular optimizing, a machine learning (ML) model to run in parallel on a plurality of processing devices (20), in which each processing device (20) comprises a plurality of processing units (PEs) (21a to 21d) for executing one or more ML model tasks of a plurality of ML model tasks. The data processing device (10) is configured to generate a Computational Graph (CG) representation (30) of the ML model from the ML model, where the CG representation (30) comprises a plurality of nodes (31) and a plurality of edges (32), each node of the plurality of nodes (31) is associated with one or more ML model tasks of the plurality of ML model tasks, and each edge (32) is associated with one or more of the plurality of ML model tasks. The plurality of edges (32) define a plurality of dependencies for the plurality of nodes (32) of the CG representation (30). Furthermore, the data processing device (10) is configured to generate an enhanced CG representation of the ML model by adding one or more other dependencies of the plurality of nodes (31) of the CG representation (30) of the ML model to the CG representation (30) of the ML model. The data processing apparatus (10) is also configured to compile the enhanced CG representation of the ML model in order to generate code for each of the plurality of processing devices (20).

The invention discloses an intelligent dosing control system based on multivariate information, and relates to the technical field of intelligent dosing, and the system comprises a multivariate fusion module which collects real-time water quality parameters, process operation parameters, external environment parameters and historical operation parameters, fuses the parameters into multivariate information data, carries out the time reference synchronization of the multivariate information data, and generates a dynamic heterogeneous data set; and the execution optimization module is used for executing the control strategy switching instruction to drive a chemical adding execution mechanism, and optimizing a stability quantitative index and an anti-fact deduction decision model according to the change trend of the actual water outlet parameters. According to the method, the main control mechanism and the shadow mechanism which run in parallel are constructed, the water quality sudden change working condition is simulated in the virtual environment, and the regulation and control performance deviation of the two control mechanisms on the water quality parameters is quantitatively compared in real time.