CN-121993991-A - Cooling water intelligent brain decision and execution system based on multi-source sensing and automatic regulation

CN121993991ACN 121993991 ACN121993991 ACN 121993991ACN-121993991-A

Abstract

The invention relates to the technical field of intelligent regulation, in particular to a cooling water intelligent brain decision and execution system based on multi-source perception and automatic regulation, which comprises a data preprocessing module, a fusion perception evaluation module, an operation mode identification module, an optimization deduction and decision module, a control instruction generation module and an execution monitoring and learning module, wherein the system is used for cleaning multi-source perception data of cooling water regulation equipment to obtain multi-source standard operation parameters; the method comprises the steps of carrying out weight distribution evaluation on multisource standard operation parameters to obtain fusion perception values, carrying out threshold comparison on the fusion perception values to obtain an operation mode, carrying out forward simulation deduction by taking the fusion perception values as initial conditions and safety control target requirements as optimization targets to obtain an optimization control strategy, carrying out analysis coding on the optimization control strategy to obtain specific control instructions, and updating monitored response data to historical operation data.

Inventors

MA MINGLIANG
XU CHAO
YANG LI
LI PENG
Xu Fapeng
LU GUANGYANG

Assignees

甘肃鹏发环保节能科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260317

Claims (7)

1. The cooling water intelligent brain decision and execution system based on multi-source perception and automatic regulation is characterized by comprising a data preprocessing module, a fusion perception evaluation module, an operation mode identification module, an optimization deduction and decision module, a control instruction generation module and an execution monitoring and learning module, wherein: The data preprocessing module is used for cleaning the multi-source sensing data of the cooling water adjusting equipment to obtain multi-source standard operation parameters of the cooling water adjusting equipment; The fusion perception evaluation module is used for carrying out weight distribution evaluation on the multi-source standard operation parameters based on the historical operation data of the cooling water adjusting equipment and an expert knowledge base so as to obtain fusion perception values of the multi-source standard operation parameters, and is specifically used for: Performing collaborative analysis on historical operation parameters and system operation effects in historical operation data of the cooling water regulating equipment to obtain an operation effect association relation of the historical operation data; Determining the control influence priority of the historical operation parameters on the cooling water regulating equipment according to an expert knowledge base of the cooling water regulating equipment; Combining the operation effect association relation with the control influence priority rule, and carrying out dynamic weight distribution on the multi-source standard operation parameters to obtain weight coefficients of the multi-source standard operation parameters; Weighting calculation is carried out on the weight coefficient and the multi-source standard operation parameter to obtain a fusion perception value of the multi-source standard operation parameter; The operation mode identification module is used for comparing the threshold values of the fusion perception values to obtain an operation mode of the multi-source standard operation parameters; The optimization deduction and decision module is used for carrying out forward simulation deduction by taking the fusion perception value as an initial condition and the corresponding safety control target requirement in the operation mode as an optimization target, and carrying out analysis decision on an optimal parameter set in a deduction result to obtain an optimization control strategy of the multi-source standard operation parameters, and is specifically used for: carrying out demand analysis on specific safety control parameters in the operation mode to obtain target demand parameter items of the operation mode; taking the fusion perception value as an initial state of simulation, and setting a boundary condition and an iteration step length of forward simulation in the cooling water adjusting equipment by combining the current working condition constraint of the cooling water adjusting equipment; Based on the boundary condition and the iteration step length, taking the initial state as a starting point, performing traversal simulation screening on the multi-source standard operation parameters to obtain a core controlled state evolution track of the cooling water regulating equipment; And carrying out satisfaction evaluation on the evolution track of the core controlled state based on the safety control target demand parameter item, wherein a calculation formula of a satisfaction evaluation value in the satisfaction evaluation is as follows: ; in the formula, For the satisfaction evaluation value of the above-mentioned condition, For the total number of time steps deduced for the forward simulation, In order to represent the current time step index, Deriving a mid-time step for the forward simulation Is used for the time weighting factor of (a), Is the first Time step in the evolution track of the controlled state of the bar core Is set to be a key state parameter value of (c), For a predetermined deviation sensitivity coefficient, To be in a time step Is set to the target demand parameter term expected value of (1), As a function of the natural index of refraction, Calculating for absolute value; determining a candidate parameter set of the multi-source standard operation parameters according to the satisfaction degree evaluation result; the optimizing deduction and decision module is specifically used for performing analysis decision on the optimal parameter set in the deduction result to obtain the optimizing control strategy of the multi-source standard operation parameters when executing the optimizing control strategy: taking the candidate parameter set as an optimal parameter set of a deduction result in the forward simulation deduction; performing executable verification on the optimal parameter set based on equipment operation constraint conditions in the expert knowledge base to obtain a feasible parameter sequence of the optimal parameter set; Mapping the feasible parameter sequence into a preliminary control action sequence according to the corresponding relation between the feasible parameter sequence and the control action in the cooling water regulating equipment; Filtering and smoothing the preliminary control action sequence according to a preset control stability rule to obtain a smooth control action sequence of the preliminary control action sequence; Integrating and packaging the smooth control action sequence and the current operation mode to obtain an optimized control strategy of the multi-source standard operation parameters; The control instruction generation module is used for carrying out analysis and coding on the optimized control strategy to obtain a specific control instruction of the cooling water regulating equipment; The execution monitoring and learning module is used for carrying out response monitoring on the specific control instruction and updating the monitored response data to the historical operation data so as to realize iterative correction of the optimal control strategy.
2. The cooling water intelligent brain decision and execution system based on multi-source sensing and automatic adjustment according to claim 1, wherein the data preprocessing module is specifically configured to, when performing data cleaning on multi-source sensing data of a cooling water adjustment device to obtain multi-source standard operation parameters of the cooling water adjustment device: removing abnormal mutation values of multi-source sensing data in cooling water adjusting equipment to obtain a data sequence to be complemented of the cooling water adjusting equipment; Filling the missing values in the data sequence to be complemented with the average value to obtain a complete data sequence of the cooling water regulating equipment; Time sequence alignment is carried out on the complete data sequence, so that a time synchronization data sequence of the cooling water adjusting device is obtained; and dimension format unification is carried out on the parameters in the time synchronization data sequence, so as to obtain the multi-source standard operation parameters of the cooling water regulating equipment.
3. The cooling water brain decision and execution system based on multi-source perception and automatic adjustment according to claim 1, wherein the fusion perception evaluation module is specifically configured to, when executing the control influence priority rule in combination with the operation effect association relation, dynamically allocate weights to the multi-source standard operation parameters to obtain the weight coefficients of the multi-source standard operation parameters: Judging an operation state interval to which the current value belongs according to the current value of the multi-source standard operation parameter, wherein the operation state interval comprises a high-efficiency stable interval, a normal fluctuation interval and an interval needing attention; According to the association weight adjustment reference between the expert knowledge base and the operation state interval, performing self-adaptive weight adjustment on the operation parameters in the operation state interval to obtain initial basic weights of the operation parameters; Performing deviation recognition on the variation amplitude of the multi-source standard operation parameter in the adjacent sampling period and the normal fluctuation range of the parameter recorded in the historical operation data to obtain a variation trend abnormal parameter of the multi-source standard operation parameter; And carrying out temporary strengthening adjustment on the abnormal parameters of the variation trend and the initial basic weight according to a weight adjustment strategy of abnormal fluctuation of the parameters in the expert knowledge base, and taking the generated real-time weight set of the multi-source standard operation parameters as a weight coefficient.
4. The cooling water brain decision and execution system based on multi-source sensing and automatic regulation as claimed in claim 3, wherein the calculation formula of said fusion sensing value is as follows: ; in the formula, For the purpose of the fusion of the perceptual values, For the total number of multi-source standard operating parameters, Is the first The number of said multi-source standard operating parameters, Is the first Operational effect association of each of the multi-source standard operational parameters, Is the first Control of each of the multisource standard operating parameters affects priority, Adjusting an index for the degree of association in the expert knowledge base, Adjusting an index for priority in the expert knowledge base, Is the first Operational effect association of each of the multi-source standard operational parameters, Is the first Control of each of the multisource standard operating parameters affects priority.
5. The cooling water brain decision and execution system based on multi-source sensing and automatic adjustment according to claim 1, wherein the operation mode identification module is specifically configured to, when executing the operation mode of obtaining the multi-source standard operation parameter by performing the threshold comparison on the fusion sensing value: acquiring an operation mode threshold interval and an associated operation mode label of the cooling water adjusting device, wherein the associated operation mode label comprises a high-efficiency operation mode, a conventional load mode and a high-load guarantee mode; Sequentially carrying out matching judgment on the fusion perception value and the operation mode threshold interval; According to the matching judgment result, determining the corresponding operation mode label of the fusion perception value as the operation mode of the multi-source standard operation parameter; and when the fusion perception value exceeds the operation mode threshold interval, marking the operation mode as a transition adjustment mode, and starting an adaptive calibration flow for the operation mode threshold interval.
6. The cooling water intelligent brain decision and execution system based on multi-source sensing and automatic regulation according to claim 1, wherein the control instruction generation module is specifically configured to, when executing the analysis encoding of the optimized control strategy to obtain a specific control instruction of the cooling water regulation device: Carrying out data packet analysis on the optimized control strategy to obtain an adjustable parameter target value of the optimized control strategy; according to a preset equipment control protocol and signal mapping relation, converting the adjustable parameter target value into a standardized instruction data frame of the cooling water adjusting equipment; distributing the standardized instruction data frame to a corresponding target executing mechanism according to the physical address and the logic identifier of the executing mechanism in the cooling water regulating equipment to obtain control instruction related information of the cooling water regulating equipment; And analyzing and encoding the control instruction related information according to the distribution time sequence of the standardized instruction data frame to obtain a specific control instruction of the cooling water regulating equipment.
7. The cooling water brain decision and execution system based on multi-source sensing and automatic adjustment according to claim 1, wherein the execution monitoring and learning module is configured to, when executing response monitoring on the specific control command and updating the monitored response data to the historical operation data to implement iterative correction of the optimized control strategy: Executing the specific control instruction, and synchronously monitoring the actual running state and key running parameters of related execution components in the cooling water regulating equipment; after a preset control response period is completed, carrying out statistics integration on feedback data of the actual running state and the key running parameters to obtain a response data set of the cooling water regulating equipment; comparing and analyzing the actual effect data in the response data set with the ideal effect of the optimal control strategy to obtain the effect deviation completion degree of the optimal control strategy; And updating the response data set and the complete closed-loop data record of the effect deviation completion degree to the historical operation database to serve as an iteration correction basis for subsequent weight distribution evaluation and optimization control strategy generation.

Description

Cooling water intelligent brain decision and execution system based on multi-source sensing and automatic regulation Technical Field The invention relates to the technical field of intelligent regulation, in particular to a cooling water intelligent brain decision and execution system based on multi-source perception and automatic regulation. Background The traditional cooling water regulating system lacks a perfect standardized mechanism in a multi-source perception data processing link, is difficult to effectively remove abnormal mutation values in data, has a simpler complement mode for the missing data, causes deviation of generated operation parameters, and cannot provide reliable data support for subsequent decisions. Meanwhile, the existing system mostly adopts fixed rules for weight distribution of multisource operation parameters, and cannot be dynamically adjusted by combining historical operation effects and real-time operation states of equipment, so that parameter fusion evaluation results are disjointed from actual operation requirements of the equipment, and the operation situation of the system is difficult to accurately reflect. The operation mode identification of the existing cooling water regulating system depends on a fixed threshold interval, lacks self-adaptive calibration capability, and faces to the fact that the mode misjudgment is easy to occur due to parameter fluctuation under complex working conditions, so that the accurate positioning of a control target is affected. In an optimization decision link, the existing system cannot fully integrate the current working condition constraint and the safety control requirement to carry out refined simulation deduction, so that the suitability and the performability of the generated control strategy are insufficient. In addition, most systems lack complete closed-loop monitoring and learning mechanisms, response data after control instruction execution cannot be effectively fed back to a decision module, iterative optimization of a control strategy is difficult to realize, a control effect is easy to fade gradually under long-term operation, and the requirement of high-efficiency stable operation of cooling water regulating equipment cannot be met. Disclosure of Invention In order to achieve the above purpose, the cooling water intelligent brain decision and execution system based on multi-source perception and automatic regulation provided by the invention is characterized by comprising a data preprocessing module, a fusion perception evaluation module, an operation mode identification module, an optimization deduction and decision module, a control instruction generation module and an execution monitoring and learning module, wherein: The data preprocessing module is used for cleaning the multi-source sensing data of the cooling water adjusting equipment to obtain multi-source standard operation parameters of the cooling water adjusting equipment; The fusion perception evaluation module is used for carrying out weight distribution evaluation on the multi-source standard operation parameters based on the historical operation data of the cooling water adjusting equipment and an expert knowledge base so as to obtain fusion perception values of the multi-source standard operation parameters; The operation mode identification module is used for comparing the threshold values of the fusion perception values to obtain an operation mode of the multi-source standard operation parameters; The optimization deduction and decision module is used for carrying out forward simulation deduction by taking the fusion perception value as an initial condition and the corresponding safety control target requirement in the operation mode as an optimization target, and carrying out analysis decision on an optimal parameter set in a deduction result to obtain an optimal control strategy of the multi-source standard operation parameters; The control instruction generation module is used for carrying out analysis and coding on the optimized control strategy to obtain a specific control instruction of the cooling water regulating equipment; The execution monitoring and learning module is used for carrying out response monitoring on the specific control instruction and updating the monitored response data to the historical operation data so as to realize iterative correction of the optimal control strategy. In a preferred embodiment, the data preprocessing module is specifically configured to, when performing data cleaning on multi-source sensing data of the cooling water conditioning device to obtain multi-source standard operation parameters of the cooling water conditioning device: removing abnormal mutation values of multi-source sensing data in cooling water adjusting equipment to obtain a data sequence to be complemented of the cooling water adjusting equipment; Filling the missing values in the data sequence to be complemented with the average value to obta