Omnibearing safety protection and fault early warning system for cooling tower

Abstract

The invention relates to the technical field of industrial management, in particular to a cooling tower all-dimensional safety protection and fault early warning system, which comprises a sound-chemical data acquisition module for acquiring a sound-chemical data set; the corrosion risk identification module is used for establishing a sound-chemical coupling model, performing corrosion risk assessment on the target area and outputting spatial positioning information; the responsibility node matching module is used for automatically matching responsibility nodes and constructing a'corrosion point-responsibility node 'initial incidence relation; the cross responsibility identification and optimal response path generation module is used for calculating the responsibility coverage conflict degree of each responsibility node by utilizing a responsibility coverage conflict measurement model and judging whether a cross node exists or not; if cross nodes exist, a comprehensive scheduling scoring model is constructed, and an optimal response path is generated based on task response time delay, a load balancing index and a node priority weight; and the task scheduling module is used for distributing the maintenance tasks to the corresponding responsibility nodes according to the priority order and the optimal response path to carry out hierarchical collaborative response management.

Description

Technical Field

[0001] The present invention relates to the field of industrial management technology, and in particular to an all-round safety protection and fault early warning system for a cooling tower. Background Art

[0002] In large-scale industrial production scenarios, cooling towers are often deployed across multiple production processes. As energy-intensive facilities, their operational stability has an indirect but significant impact on a company's overall operational efficiency. Due to their long operating times and high environmental exposure, cooling towers and their associated structures often suffer from corrosion, aging, and material fatigue, necessitating periodic monitoring and maintenance across multiple departments.

[0003] At present, enterprises usually use local monitoring equipment (such as noise, vibration, and water quality recorders) to collect cooling tower operation data. However, this data is mostly in a state of "distributed storage and passive analysis" at the system level, making it difficult to effectively integrate it into the enterprise's industrial data management platform, and it is also unable to efficiently support cross-departmental task decision-making and response coordination.

[0004] Furthermore, in actual management, addressing cooling tower corrosion hazards often involves multiple responsible entities, including operations, equipment, water treatment, and outsourced maintenance. The lack of a comprehensive mechanism for identifying responsibilities, determining conflicts, and allocating tasks can easily lead to the following management bottlenecks: Unclear overlapping responsibilities: Multiple departments respond repeatedly to or pass the buck to the same potential hazard area, resulting in task conflicts and a lack of resolution; Rigid scheduling mechanisms: The lack of a dynamic optimization path based on risk levels, resource availability, and task relevance can easily lead to resource mismatches and delayed responses; Inefficient data utilization: Acoustic and water quality data are used only to determine equipment status, failing to serve as a basis for triggering organizational tasks and making responsibility decisions, making it difficult to achieve an intelligent closed-loop process across the entire process.

[0005] While some industrial systems have deployed operations and maintenance platforms or repair work order systems, most remain at the static dispatch and manual review stage, lacking the ability to implement task conflict analysis, priority calculation, and coordinated resource allocation for the "corrosion risk + multiple responsibility nodes" scenario. Therefore, there is an urgent need to build a multi-faceted collaborative mechanism based on intelligent analysis of industrial data to achieve responsibility allocation, conflict identification, and optimized task scheduling for corrosion early warning, providing industrial organizations with a dynamic, controllable, and explainable management solution.

[0006] Therefore, a comprehensive safety protection and fault warning system for cooling towers is proposed. Summary of the Invention

[0007] The present invention aims to provide a comprehensive cooling tower safety protection and fault warning system that implements dynamic hierarchical management of corrosion information, identifies conflicting responsibility nodes, and adaptively schedules response tasks, thereby promoting the transformation of industrial data management from "perception islands" to "closed-loop control." The system includes an acoustic-chemical data acquisition module that collects acoustic-chemical data sets; a corrosion risk identification module that establishes an acoustic-chemical coupling model, performs corrosion risk assessments on target areas, and outputs spatial positioning information; a responsibility node matching module that automatically matches responsibility nodes and establishes an initial "corrosion point-responsibility node" relationship; a cross-responsibility identification and optimal response path generation module that uses a responsibility coverage conflict measurement model to calculate the responsibility coverage conflict degree of each responsibility node and determine whether there are cross-nodes. If there are cross-nodes, a comprehensive scheduling scoring model is constructed to generate an optimal response path based on task response delay, load balancing index, and node priority weights; and a task scheduling module that, based on the optimal response path, dispatches maintenance tasks to corresponding responsibility nodes in order of priority for hierarchical collaborative response management.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A comprehensive safety protection and fault warning system for cooling towers, comprising:

[0010] "Acoustic-chemical" data acquisition module, used to collect "acoustic-chemical" data sets during the operation of the cooling tower;

[0011] The corrosion risk identification module is used to establish an acoustic-chemical coupling model based on the acoustic-chemical data set, conduct corrosion risk assessment on the target area, and output the corresponding spatial positioning information;

[0012] Responsibility node matching module, which is used to automatically match responsibility nodes and build the initial "corrosion point-responsibility node" association relationship based on corrosion risk and spatial positioning information;

[0013] The cross-responsibility identification and optimal response path generation module is used to calculate the responsibility coverage conflict degree of each responsibility node based on the initial "corruption point-responsibility node" relationship using the responsibility coverage conflict measurement model, and determine whether there are cross-nodes. If there are cross-nodes, a comprehensive scheduling scoring model is constructed to generate the optimal response path based on the task response delay, load balancing index, and node priority weight of the responsibility node.

[0014] The task scheduling module is used to dispatch maintenance tasks to corresponding responsibility nodes in order of priority based on the optimal response path for hierarchical collaborative response management.

[0015] Preferably, the "acoustic-chemical" data set includes: vibration noise spectrum data and chemical parameter data;

[0016] The vibration noise spectrum data includes vibration signals and acoustic signals of different frequency bands during the operation of the cooling tower; the chemical parameter data includes Cl - Ion concentration, SO4 2- Ion concentration, pH, conductivity and dissolved oxygen content.

[0017] Preferably, the "acoustic-chemical" coupling model includes: an acoustic feature extraction layer, a chemical feature mapping layer, a multimodal feature fusion layer, and a spatial positioning and risk assessment output layer;

[0018] The acoustic feature extraction layer performs frequency domain analysis on the vibration noise spectrum data and extracts the acoustic feature vector; the chemical feature mapping layer calculates the corrosion factor based on the chemical parameter data, generates the chemical feature vector, and establishes a nonlinear mapping relationship between the chemical parameter data and the corrosion factor; the multimodal feature fusion layer fuses the acoustic feature vector and the chemical feature vector to generate a comprehensive corrosion risk feature vector; the spatial positioning and risk assessment output layer divides the cooling tower into N target areas, and performs corrosion risk assessment on the comprehensive feature vector of each target area through a pre-trained multi-layer neural network, and outputs the corrosion risk level of the N target areas and the corresponding spatial positioning information.

[0019] Preferably, the process of constructing the initial association relationship of "corruption point - responsibility node" is:

[0020] A region-responsibility mapping rule base is established, and M responsibility nodes are set for the N target areas of the cooling tower; the responsibility nodes include professional field identification, skill level, equipment configuration and personnel number attributes; an automatic matching algorithm is used to automatically match the corresponding responsibility nodes in the preset region-responsibility mapping rules based on the corrosion risk points in each target area, and the response priority weight is calculated according to the risk level; the corrosion risk points are obtained based on the corrosion risk level and spatial positioning information; an association relationship matrix is ​​constructed based on the unique identification of the corrosion point, the target area to which it belongs, the responsibility node identification, the corrosion risk level, the spatial positioning information, the response priority and the expected processing time, and the initial "corrosion point-responsibility node" association relationship is obtained.

[0021] Preferably, the responsibility coverage conflict measurement model includes: a spatial overlap calculation layer, a temporal conflict analysis layer, a resource contention evaluation layer and a comprehensive conflict measurement layer;

[0022] The spatial overlap calculation layer calculates the proportion of overlapping areas of responsibility coverage of corrosion risk points of different responsibility nodes in the same target area based on the initial association relationship of "corrosion point-responsibility node" to obtain the spatial overlap; the temporal conflict analysis layer calculates the temporal conflict probability based on the task execution time window of each responsibility node and the estimated processing time in the initial association relationship of "corrosion point-responsibility node" to obtain the temporal conflict degree; the resource competition assessment layer analyzes the degree of conflict in the demands of different responsibility nodes for the same maintenance resources, equipment configuration and personnel deployment to obtain the resource competition degree; the comprehensive conflict measurement layer synthesizes the responsibility coverage overlap, temporal conflict and resource competition to obtain the responsibility coverage conflict degree, and judges whether there is an intersection node based on the responsibility coverage conflict threshold.

[0023] Preferably, the comprehensive scheduling scoring model includes: a task response delay evaluation layer, a load balancing calculation layer, a node priority weight allocation layer and a path efficiency optimization layer;

[0024] The task response delay assessment layer predicts the task response time of each responsible node and calculates the task response delay based on historical execution data and the current task queue length, combined with the expected processing time in the initial association relationship of "corrosion point-responsibility node". The load balancing calculation layer monitors the current workload rate of each responsible node in real time, and calculates the load balancing index based on the distribution of corrosion risk levels in the target area. The node priority weight allocation layer allocates priority weights based on the professional capabilities, equipment configuration, and personnel number attributes of the responsible nodes, combined with the corrosion risk level of each target area. The path efficiency optimization layer comprehensively considers the task response delay, load balancing index, and priority weight to generate the optimal response path and execution order for the target area with cross nodes.

[0025] Preferably, the process of hierarchical collaborative response management is:

[0026] The urgency of maintenance tasks is graded according to the corrosion risk level of each target area. Based on the urgency of the maintenance tasks, the corrosion risk level and the priority of the responsible nodes, the maintenance tasks are pushed to the corresponding responsible nodes using the optimal response path according to the task urgency classification, and an execution schedule is automatically generated. The system provides maintenance task receipt confirmation, processing progress feedback and completion status update functions, tracks and records the entire maintenance task execution process, and realizes closed-loop management of maintenance tasks.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. By building a multimodal acoustic-chemical coupling model that integrates acoustic vibration signals with water chemical parameters, the system enables early identification and spatial localization of corrosion risks, forming a coordinated response foundation encompassing "risk source, spatial location, and responsible node." This acoustic-chemical coupling model significantly improves the confidence level of corrosion point detection, provides stable input for subsequent responsible node matching and scheduling, and avoids response delays in downstream processes due to data bias.

[0029] 2. This solution automatically matches responsible nodes by combining corrosion risk with spatial positioning information, achieving precise location and efficient tracing of corrosion responsibility. This automated initial "corrosion point-responsibility node" relationship is fast and highly accurate, avoiding subjective biases in human judgment. Once corrosion occurs, the system quickly identifies the responsible party, shortening the time it takes to initiate fault response. This clear chain of responsibility also provides a reliable basis for performance evaluation, maintenance history tracing, and the development of preventive measures, forming a closed-loop management system that ensures continuous improvement in maintenance quality and efficiency.

[0030] 3. This invention introduces a cross-responsibility node identification module and utilizes a responsibility coverage conflict measurement model to effectively address issues such as overlapping task responses, ambiguous responsibilities, and resource conflicts among multiple nodes. By constructing a comprehensive scheduling scoring model, the system can combine task response latency, load balancing, and node priority to achieve optimal response path planning with clear responsibilities and reasonable scheduling. This cross-responsibility node identification module is tightly coupled with the "corruption point-responsibility node" initial association relationship module, making the flow logic of maintenance tasks clearer and significantly improving overall scheduling efficiency and execution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic structural diagram of a cooling tower all-round safety protection and fault warning system provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of the structure of the "acoustic-chemical" coupling model provided in an embodiment of the present invention;

[0033] Figure 3 This is a diagram of the working principle of the responsibility coverage conflict measurement model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] This invention proposes a comprehensive cooling tower safety protection and fault warning system that enables dynamic hierarchical management of corrosion information, identification of conflicting responsibility nodes, and adaptive scheduling of response tasks, thereby promoting the transformation of industrial data management from "islands of perception" to "closed-loop control." To illustrate the effectiveness of the present invention's method in achieving dynamic hierarchical management of corrosion information, identification of conflicting responsibility nodes, and adaptive scheduling of response tasks, the following two examples illustrate the effectiveness of the invention.

[0036] Example 1

[0037] In the embodiments of the present application, the system proposed in the present invention is used to implement dynamic hierarchical management of corrosion information, identification of conflict of responsible nodes, and adaptive scheduling of response tasks, thereby promoting the transformation of industrial data management from "perception islands" to "closed-loop control". Figure 1 This is a specific structural diagram of the system of the present invention, including: "acoustic-chemical" data acquisition module, corrosion risk identification module, responsibility node matching module, cross-responsibility identification and optimal response path generation module and task scheduling module; wherein, the "acoustic-chemical" data acquisition module collects the "acoustic-chemical" data set during the operation of the cooling tower; the corrosion risk identification module establishes an "acoustic-chemical" coupling model, performs corrosion risk assessment on the target area, and outputs spatial positioning information; the responsibility node matching module automatically matches the responsibility nodes and constructs the initial association relationship of "corrosion point-responsibility node"; the cross-responsibility identification and optimal response path generation module uses the responsibility coverage conflict measurement model to calculate the responsibility coverage conflict degree of each responsibility node and determine whether there is a cross node; if there is a cross node, a comprehensive scheduling scoring model is constructed to generate the optimal response path based on the task response delay, load balancing index and node priority weight; the task scheduling module distributes the maintenance tasks to the corresponding responsibility nodes in order of priority according to the optimal response path for hierarchical collaborative response management. The following is based on Figure 1 The following content is described:

[0038] "Acoustic-chemical" data acquisition module, used to collect "acoustic-chemical" data sets during the operation of the cooling tower;

[0039] The "acoustic-chemical" data set includes: vibration noise spectrum data and chemical parameter data;

[0040] The vibration noise spectrum data includes vibration signals and acoustic signals of different frequency bands during the operation of the cooling tower; the chemical parameter data includes Cl - Ion concentration, SO4 2- Ion concentration, pH, conductivity and dissolved oxygen content.

[0041] Specifically, vibration acceleration sensors are installed at corrosion-prone parts of the cooling tower, such as the fan bracket, filler support beam, and tower main frame. The sampling frequency is set to 10kHz, covering the frequency range of 0.1Hz-5kHz.

[0042] An acoustic sensor array is placed at different heights inside the tower to capture acoustic signals during cooling tower operation, with a particular focus on abnormal sounds in the mid-frequency range of 100Hz-2kHz.

[0043] The collected vibration signals include but are not limited to: bearing vibration characteristics (10-1000Hz), structural component resonance frequency (50-500Hz), and filler collision noise (500-2000Hz);

[0044] A multi-parameter water quality monitor is installed in the cooling tower water collection tank to collect Cl- ion concentration, SO4 2- Ion concentration, pH, conductivity and dissolved oxygen content.

[0045] The acoustic-chemical data acquisition module of this embodiment achieves coordinated monitoring of physical signals and chemical indicators by synchronously collecting vibration and noise spectrum data and chemical parameter data, providing multi-dimensional, high-quality basic data support for the entire system. The vibration and noise spectrum data can reflect the dynamic health status of cooling tower structural components in real time. When corrosion causes a decrease in structural stiffness, the vibration spectrum will exhibit characteristic shifts. The chemical parameter data provides direct evidence from the corrosion mechanism level. - 、SO4 2- Ion concentration is directly related to corrosion rate, pH and conductivity reflect the aggressiveness of the corrosive environment, and dissolved oxygen content influences the electrochemical corrosion process. This collaborative acoustic-chemical data collection method can detect corrosion risks 15-20 days earlier than traditional single-monitoring methods, providing a reliable data foundation for subsequent risk identification modules.

[0046] Preferably, the corrosion risk identification module is used to establish an "acoustic-chemical" coupling model based on the "acoustic-chemical" data set, perform corrosion risk assessment on the target area, and output corresponding spatial positioning information;

[0047] The "acoustic-chemical" coupling model includes: acoustic feature extraction layer, chemical feature mapping layer, multimodal feature fusion layer and spatial positioning and risk assessment output layer; Figure 2 ;

[0048] The acoustic feature extraction layer performs frequency domain analysis on the vibration noise spectrum data and extracts the acoustic feature vector; the chemical feature mapping layer calculates the corrosion factor based on the chemical parameter data, generates the chemical feature vector, and establishes a nonlinear mapping relationship between the chemical parameter data and the corrosion factor; the multimodal feature fusion layer fuses the acoustic feature vector and the chemical feature vector to generate a comprehensive corrosion risk feature vector; the spatial positioning and risk assessment output layer divides the cooling tower into N target areas, and performs corrosion risk assessment on the comprehensive feature vector of each target area through a pre-trained multi-layer neural network, and outputs the corrosion risk level of the N target areas and the corresponding spatial positioning information.

[0049] Specifically, the acoustic feature extraction layer performs fast Fourier transform on the collected vibration noise spectrum data to extract the characteristic frequency; calculates the amplitude peak and spectrum energy distribution parameters of each frequency band, and constructs the acoustic feature vector;

[0050] The corrosion factors in the chemical feature mapping layer include corrosion activity index, ion concentration ratio, pH deviation and conductivity change rate corrosion activity index; the corrosion activity index is based on Cl - Ion concentration ratio, SO4 2- The ion concentration ratio, pH deviation (|pH-7|) and conductivity change rate are weighted and fused; the fusion weight is obtained based on historical data training;

[0051] The multimodal feature fusion layer uses an attention mechanism to perform weighted fusion of acoustic and chemical features, and the fusion weights are obtained through adaptive learning;

[0052] The spatial positioning and risk assessment output layer divides each cooling tower into 20 target areas (N=20), including: 4 tower top fan areas, 8 filling areas, 4 water sprinkling areas and 4 water collection pool areas; a pre-trained 5-layer fully connected neural network (number of nodes: 100-64-32-16-5) is used for risk assessment, outputting 5 levels of corrosion risk, including level 1 very low risk, level 2 low risk, level 3 medium risk, level 4 high risk and level 5 very high risk, and also outputting spatial positioning information: (tower number, area number, 3D coordinates).

[0053] This embodiment proposes an acoustic-chemical coupling model. Through a four-layer architecture, it achieves deep fusion and intelligent analysis of heterogeneous data. It converts raw physical signals and chemical parameters into quantifiable corrosion risk assessment results and accurately outputs spatial positioning information, laying the technical foundation for precise management of the entire system. Acoustic features capture abnormal structural vibrations caused by corrosion (such as resonant frequency shifts and increased abnormal noise), while chemical features quantify the electrochemical driving force of corrosion. By deeply integrating these two features through a neural network, the system can issue early warnings for corrosion development, achieving a synergistic effect of "1+1>2". Furthermore, the spatial positioning function divides the cooling tower into N target areas and assesses each one individually, eliminating the need for a general, quantified assessment of corrosion risk. This refined risk assessment and positioning supports the precise matching of the responsible node matching module, enabling the most appropriate responsible party for each corrosion point to be identified, significantly reducing response time. Table 1 compares the efficiency of different data detection methods for early corrosion risk identification.

[0054] Table 1 Comparison of early corrosion risk identification efficiency

[0055] Monitoring methods Corrosion occurrence time System warning time Early warning lead time False alarm rate Chemical parameters 30 days 25 days 5 days 8％ Acoustic vibration 30 days 22 days 8 days 10％ "Acoustic-chemical" coupling model 30 days 16 days 14 days 3％

[0056] Preferably, a responsibility node matching module is used to automatically match responsibility nodes and build an initial association relationship of "corrosion point-responsibility node" based on corrosion risk and spatial positioning information;

[0057] The process of constructing the initial association relationship of "corruption point-responsibility node" is as follows:

[0058] A region-responsibility mapping rule base is established, and M responsibility nodes are set for the N target areas of the cooling tower; the responsibility nodes include professional field identification, skill level, equipment configuration and personnel number attributes; an automatic matching algorithm is used to automatically match the corresponding responsibility nodes in the preset region-responsibility mapping rules based on the corrosion risk points in each target area, and the response priority weight is calculated according to the risk level; the corrosion risk points are obtained based on the corrosion risk level and spatial positioning information; an association relationship matrix is ​​constructed based on the unique identification of the corrosion point, the target area to which it belongs, the responsibility node identification, the corrosion risk level, the spatial positioning information, the response priority and the expected processing time, and the initial "corrosion point-responsibility node" association relationship is obtained.

[0059] Specifically, six responsibility nodes were set for the 20 target areas, including the Mechanical Maintenance Group of the Equipment Department, the Electrical Maintenance Group of the Equipment Department, the Operation Management Group of the Production Department, the Water Treatment Workshop, Outsourced Maintenance Unit A, and Outsourced Maintenance Unit B;

[0060] The priority weight P=0.5×risk level / 5+0.3×regional importance+0.2×historical failure frequency; wherein the regional importance is: fan area (1.0)>filling area (0.8)>sprinkling area (0.6)>water collection pool area (0.4).

[0061] Table 2 provides a comparison of the efficiency of corrosion responsibility location and tracing in different management models.

[0062] Table 2 Comparison of corrosion responsibility location and traceability efficiency

[0063]

[0064] The responsibility node matching module of this embodiment establishes an initial "corruption point-responsibility node" relationship, automating the transition from risk identification to responsibility implementation. This thoroughly resolves the issue of unclear responsibilities in traditional management and provides organizational support for the efficient operation of the overall system. This responsibility node matching module transforms the technical issues of corrosion management into executable management solutions. Using a pre-set region-responsibility mapping rule base, each corrosion point is automatically matched to the most appropriate responsibility node based on its characteristics (location, risk level, and professional requirements). This matching process considers multiple dimensions, including professional field, skill level, equipment configuration, and personnel number, ensuring the scientific nature of the matching. Furthermore, the calculation of response priority weights prioritizes resources to high-risk areas, avoiding inefficient "all-encompassing" management. This automated matching mechanism significantly improves the efficiency of protection management and provides a clear initial relationship for the subsequent cross-responsibility identification module, laying the foundation for multi-departmental collaboration.

[0065] Preferably, the cross-responsibility identification and optimal response path generation module is used to calculate the responsibility coverage conflict degree of each responsibility node based on the initial association relationship of "corruption point-responsibility node" using the responsibility coverage conflict measurement model, and determine whether there is a cross node;

[0066] The responsibility coverage conflict measurement model includes: a spatial overlap calculation layer, a temporal conflict analysis layer, a resource competition evaluation layer and a comprehensive conflict measurement layer; Figure 3 ;

[0067] The spatial overlap calculation layer calculates the proportion of overlapping areas of responsibility coverage of corrosion risk points of different responsibility nodes in the same target area based on the initial association relationship of "corrosion point-responsibility node" to obtain the spatial overlap; the temporal conflict analysis layer calculates the temporal conflict probability based on the task execution time window of each responsibility node and the estimated processing time in the initial association relationship of "corrosion point-responsibility node" to obtain the temporal conflict degree; the resource competition assessment layer analyzes the degree of conflict in the demands of different responsibility nodes for the same maintenance resources, equipment configuration and personnel deployment to obtain the resource competition degree; the comprehensive conflict measurement layer synthesizes the responsibility coverage overlap, temporal conflict and resource competition to obtain the responsibility coverage conflict degree, and judges whether there is an intersection node based on the responsibility coverage conflict threshold.

[0068] Specifically, the spatial overlap calculation layer calculates the coverage overlap of different responsible nodes for the corrosion points in the same target area; the spatial overlap is obtained based on the ratio of the overlapping area to the total coverage area;

[0069] The time conflict analysis layer analyzes the overlap of task time windows based on the Gantt chart principle and calculates the time conflict based on the ratio of overlapping time to total task time;

[0070] The resource competition assessment layer analyzes the competition for the same maintenance resources, equipment configuration, and personnel deployment, and calculates resource competition based on the ratio of resource demand overlap to total resource capacity.

[0071] The cross-responsibility identification module of this embodiment actively discovers and quantifies potential conflicts between responsibility nodes through multi-dimensional responsibility coverage conflict measurement, provides a decision-making basis for the coordinated optimization of the overall system, and effectively avoids the management dilemma of repeated responses from multiple departments or responsibility vacuum. The cross-responsibility identification module comprehensively evaluates responsibility conflicts from three dimensions: space, time, and resources: spatial overlap identifies the overlapping responsibilities of multiple departments in the same area to avoid duplicate construction; temporal conflict analyzes the timing conflicts of task execution to prevent the accumulation of tasks during critical periods; resource competition evaluates the use conflicts of equipment, personnel and other resources to ensure the rational allocation of resources. Through comprehensive conflict measurement, the system can accurately identify cross-nodes that need to be coordinated, significantly reducing responsibility conflicts. This proactive conflict identification mechanism not only improves the work efficiency of each responsibility node, but also provides an optimized entry point for the comprehensive scheduling scoring model, and is a key link in achieving global optimal scheduling.

[0072] Preferably, if there are cross-nodes, a comprehensive scheduling scoring model is constructed to generate the optimal response path based on the task response delay, load balancing index and node priority weight of the responsible node;

[0073] The comprehensive scheduling scoring model includes: a task response delay evaluation layer, a load balancing calculation layer, a node priority weight allocation layer and a path efficiency optimization layer;

[0074] The task response delay assessment layer predicts the task response time of each responsible node and calculates the task response delay based on historical execution data and the current task queue length, combined with the expected processing time in the initial association relationship of "corrosion point-responsibility node". The load balancing calculation layer monitors the current workload rate of each responsible node in real time, and calculates the load balancing index based on the distribution of corrosion risk levels in the target area. The node priority weight allocation layer allocates priority weights based on the professional capabilities, equipment configuration, and personnel number attributes of the responsible nodes, combined with the corrosion risk level of each target area. The path efficiency optimization layer comprehensively considers the task response delay, load balancing index, and priority weight to generate the optimal response path and execution order for the target area with cross nodes.

[0075] Table 3 gives the efficiency comparison table of introducing the cross-responsibility identification mechanism.

[0076] Table 3 Efficiency comparison of the introduction of the cross-responsibility identification mechanism

[0077]

[0078] The comprehensive scheduling scoring model of this embodiment generates the optimal response path through a multi-objective optimization algorithm, realizes intelligent scheduling under complex constraints, elevates the system's collaborative management capabilities to a new level, and ensures the maximum utilization of maintenance resources. The comprehensive scheduling scoring model balances the three key elements of response speed, load balancing, and professional matching. Among them, the task response delay evaluation ensures a rapid response to emergency tasks and shortens the average response time; the load balancing calculation avoids the waste of resources when some nodes are overloaded while other nodes are idle, thereby improving resource utilization efficiency; the node priority weight ensures that professionals do professional things and improves the quality of maintenance. Through path efficiency optimization, the system of the present invention can automatically generate a fast and efficient execution plan in complex situations where there are cross-nodes. This intelligent scheduling capability works closely with the aforementioned conflict identification module to jointly maximize the efficiency of multi-department collaboration.

[0079] Preferably, the task scheduling module is used to dispatch maintenance tasks to corresponding responsible nodes in order of priority based on the optimal response path for hierarchical collaborative response management. The hierarchical collaborative response management process is as follows:

[0080] The urgency of maintenance tasks is graded according to the corrosion risk level of each target area. Based on the urgency of the maintenance tasks, the corrosion risk level and the priority of the responsible nodes, the maintenance tasks are pushed to the corresponding responsible nodes using the optimal response path according to the task urgency classification, and an execution schedule is automatically generated. The system provides maintenance task receipt confirmation, processing progress feedback and completion status update functions, tracks and records the entire maintenance task execution process, and realizes closed-loop management of maintenance tasks.

[0081] The task scheduling module of this embodiment realizes digital management and control of the entire process of maintenance tasks from generation to completion through hierarchical collaborative response management, provides execution guarantee and closed-loop feedback mechanism for the overall system, and ensures that each corrosion hazard is dealt with in a timely and effective manner. The task scheduling module converts the analysis results of all the aforementioned modules into executable maintenance actions. By grading the urgency of tasks, the rational allocation of resources and the timeliness of response are ensured; the automatically generated execution schedule eliminates the arbitrariness and inefficiency of manual scheduling; and the tracking records of the entire task process provide valuable historical data to support the continuous optimization of the system. Among them, the closed-loop management function ensures the controllability and traceability of task execution through receiving receipts, progress feedback, completion confirmation and other links, thereby improving the operational reliability of the cooling tower.

[0082] The comprehensive cooling tower safety protection and fault warning system provided by this invention integrates an acoustic-chemical data acquisition module, a corrosion risk identification module, a responsibility node matching module, a cross-responsibility identification and optimal response path generation module, and a task scheduling module. This system achieves a fundamental shift in cooling tower corrosion management from passive response to active prevention, from decentralized management to systemic control, and from empirical decision-making to data-driven management. The system innovatively integrates acoustic vibration monitoring with chemical parameter analysis, breaking through the limitations of single monitoring methods. This system improves corrosion warning accuracy to 92.3%, with warning times 15-20 days earlier. Through intelligent automatic responsibility node matching and multi-dimensional conflict identification mechanisms, it shortens responsibility confirmation time from one hour to five minutes and reduces the incidence of responsibility conflicts by 86.4%. A comprehensive scheduling scoring model based on task response latency, load balancing, and priority weights achieves globally optimal resource allocation, improving resource utilization efficiency by 67.5% and shortening average response time by 62.8%. Through hierarchical coordinated response and full-process closed-loop control, it forms a complete "monitoring-assessment-decision-execution-feedback" chain, reducing maintenance costs by 32.0%. The coherent data flow and progressive intelligent decision-making architecture formed by these five modules not only solve the pain points of traditional management such as data silos, unclear responsibilities, and waste of resources, but also promote the systematic transformation of the industrial equipment maintenance management model, providing enterprises with a replicable paradigm of technology-driven management innovation, which has important technical value and broad application prospects.

[0083] Example 2

[0084] In Example 1, the proposed method successfully implemented dynamic hierarchical management of corrosion information, identification of conflicting responsibility nodes, and adaptive scheduling of response tasks, thereby promoting the transformation of industrial data management from "islands of perception" to "closed-loop control." To further verify the effectiveness of the present invention, safety protection management was also performed on another cooling tower in this example.

[0085] "Acoustic-chemical" data acquisition module, used to collect "acoustic-chemical" data sets during the operation of the cooling tower;

[0086] The "acoustic-chemical" data set includes: vibration noise spectrum data and chemical parameter data;

[0087] The vibration noise spectrum data includes vibration signals and acoustic signals of different frequency bands during the operation of the cooling tower; the chemical parameter data includes Cl - Ion concentration, SO4 2- Ion concentration, pH, conductivity and dissolved oxygen content.

[0088] This embodiment provides a cross-domain perception approach that shifts from "fault data fusion" to "risk factor collaborative modeling." Traditional cooling tower fault monitoring primarily focuses on mechanical indicators such as vibration and temperature rise, lacking early detection of potential corrosion factors. Inspired by the coupling of acoustic spectrum evolution and chemical environments, this solution proposes a "sound-chemical" dual-domain data collaborative modeling approach that fuses microscopic ion corrosion trends with macroscopic structural acoustic responses to form a comprehensive risk detection mechanism for corrosion precursors. This "cross-domain data collaborative perception" approach provides earlier trigger points and clearer paths to corrosion root causes for predictive maintenance.

[0089] Preferably, the corrosion risk identification module is used to establish an "acoustic-chemical" coupling model based on the "acoustic-chemical" data set, perform corrosion risk assessment on the target area, and output corresponding spatial positioning information;

[0090] The "acoustic-chemical" coupling model includes: an acoustic feature extraction layer, a chemical feature mapping layer, a multimodal feature fusion layer, and a spatial positioning and risk assessment output layer;

[0091] The acoustic feature extraction layer performs frequency domain analysis on the vibration noise spectrum data and extracts the acoustic feature vector; the chemical feature mapping layer calculates the corrosion factor based on the chemical parameter data, generates the chemical feature vector, and establishes a nonlinear mapping relationship between the chemical parameter data and the corrosion factor; the multimodal feature fusion layer fuses the acoustic feature vector and the chemical feature vector to generate a comprehensive corrosion risk feature vector; the spatial positioning and risk assessment output layer divides the cooling tower into N target areas, and performs corrosion risk assessment on the comprehensive feature vector of each target area through a pre-trained multi-layer neural network, and outputs the corrosion risk level of the N target areas and the corresponding spatial positioning information.

[0092] Preferably, the responsibility node matching module is used to automatically match the responsibility nodes and build the initial association relationship of "corrosion point-responsibility node" based on the corrosion risk and spatial positioning information; the specific process is as follows:

[0093] A region-responsibility mapping rule base is established, and M responsibility nodes are set for the N target areas of the cooling tower; the responsibility nodes include professional field identification, skill level, equipment configuration and personnel number attributes; an automatic matching algorithm is used to automatically match the corresponding responsibility nodes in the preset region-responsibility mapping rules based on the corrosion risk points in each target area, and the response priority weight is calculated according to the risk level; the corrosion risk points are obtained based on the corrosion risk level and spatial positioning information; an association relationship matrix is ​​constructed based on the unique identification of the corrosion point, the target area to which it belongs, the responsibility node identification, the corrosion risk level, the spatial positioning information, the response priority and the expected processing time, and the initial "corrosion point-responsibility node" association relationship is obtained.

[0094] Preferably, the cross-responsibility identification and optimal response path generation module is used to calculate the responsibility coverage conflict degree of each responsibility node based on the initial association relationship of "corruption point-responsibility node" using the responsibility coverage conflict measurement model, and determine whether there is a cross node;

[0095] The responsibility coverage conflict measurement model includes: a spatial overlap calculation layer, a temporal conflict analysis layer, a resource competition evaluation layer and a comprehensive conflict measurement layer;

[0096] The spatial overlap calculation layer calculates the proportion of overlapping areas of responsibility coverage of corrosion risk points of different responsibility nodes in the same target area based on the initial association relationship of "corrosion point-responsibility node" to obtain the spatial overlap; the temporal conflict analysis layer calculates the temporal conflict probability based on the task execution time window of each responsibility node and the estimated processing time in the initial association relationship of "corrosion point-responsibility node" to obtain the temporal conflict degree; the resource competition assessment layer analyzes the degree of conflict in the demands of different responsibility nodes for the same maintenance resources, equipment configuration and personnel deployment to obtain the resource competition degree; the comprehensive conflict measurement layer synthesizes the responsibility coverage overlap, temporal conflict and resource competition to obtain the responsibility coverage conflict degree, and judges whether there is an intersection node based on the responsibility coverage conflict threshold.

[0097] Preferably, if there are cross-nodes, a comprehensive scheduling scoring model is constructed to generate the optimal response path based on the task response delay, load balancing index and node priority weight of the responsible node; the comprehensive scheduling scoring model includes: a task response delay evaluation layer, a load balancing calculation layer, a node priority weight allocation layer and a path efficiency optimization layer;

[0098] The task response delay assessment layer predicts the task response time of each responsible node and calculates the task response delay based on historical execution data and the current task queue length, combined with the expected processing time in the initial association relationship of "corrosion point-responsibility node". The load balancing calculation layer monitors the current workload rate of each responsible node in real time, and calculates the load balancing index based on the distribution of corrosion risk levels in the target area. The node priority weight allocation layer allocates priority weights based on the professional capabilities, equipment configuration, and personnel number attributes of the responsible nodes, combined with the corrosion risk level of each target area. The path efficiency optimization layer comprehensively considers the task response delay, load balancing index, and priority weight to generate the optimal response path and execution order for the target area with cross nodes.

[0099] Inspired by the common pain points of overlapping responsibilities and buck-passing in multi-department collaborative management, this implementation moves beyond simply assigning inspection tasks to multiple candidate responsible nodes. Instead, it introduces a "responsibility coverage conflict measurement model," which for the first time treats the responsibility-response relationship as a measurable and hierarchical network graph. By calculating conflict degrees and classifying cross-level responsibilities, it accurately identifies and intervenes in overlapping responsibility assignments, providing an intelligent, conflict-free path optimization strategy for cross-organizational collaborative task dispatching. This embodies the shift in organizational game modeling thinking from "responsibility assignment" to "responsibility conflict identification."

[0100] Preferably, the task scheduling module is used to dispatch maintenance tasks to corresponding responsible nodes in order of priority based on the optimal response path for hierarchical collaborative response management. The hierarchical collaborative response management process is as follows:

[0101] The urgency of maintenance tasks is graded according to the corrosion risk level of each target area. Based on the urgency of the maintenance tasks, the corrosion risk level and the priority of the responsible nodes, the maintenance tasks are pushed to the corresponding responsible nodes using the optimal response path according to the task urgency classification, and an execution schedule is automatically generated. The system provides maintenance task receipt confirmation, processing progress feedback and completion status update functions, tracks and records the entire maintenance task execution process, and realizes closed-loop management of maintenance tasks.

[0102] Drawing on dynamic scheduling theories such as "bottleneck-priority scheduling" and "resource-preemptive optimization" in the manufacturing sector, this implementation proposes a comprehensive scoring mechanism based on cross-node risk priority, task load, and response time. This upgrades traditional manual dispatching decisions to "adaptive task collaborative path planning" powered by real-time computing power. This not only improves task execution efficiency but also enables schedulable optimization for multiple objectives (cost, timeliness, and response quality). This creates a closed-loop data-responsibility-resource system for equipment lifecycle management, achieving a paradigm shift in intelligent scheduling from "static task triggering" to "dynamic collaborative scheduling."

[0103] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cooling tower all-round safety protection and fault warning system, characterized in that: include: "Acoustic-chemical" data acquisition module, used to collect "acoustic-chemical" data sets during the operation of the cooling tower; The corrosion risk identification module is used to establish an acoustic-chemical coupling model based on the acoustic-chemical dataset, conduct corrosion risk assessment on the target area, and output the corresponding spatial positioning information; The responsibility node matching module is used to automatically match responsibility nodes and establish the initial "corrosion point-responsibility node" association relationship based on corrosion risk and spatial positioning information; The cross-responsibility identification and optimal response path generation module is used to calculate the responsibility coverage conflict degree of each responsibility node based on the initial "corruption point-responsibility node" relationship using the responsibility coverage conflict measurement model, and determine whether there are cross-nodes. If there are cross-nodes, a comprehensive scheduling scoring model is constructed to generate the optimal response path based on the task response delay, load balancing index, and node priority weight of the responsible node. The task scheduling module is used to dispatch maintenance tasks to corresponding responsibility nodes in order of priority based on the optimal response path for hierarchical collaborative response management.

2. A cooling tower all-round safety protection and fault warning system according to claim 1, characterized in that: The "acoustic-chemical" data set includes: vibration noise spectrum data and chemical parameter data; The vibration noise spectrum data includes vibration signals and acoustic signals of different frequency bands during the operation of the cooling tower; the chemical parameter data includes Cl - Ion concentration, SO4 2- Ion concentration, pH, conductivity and dissolved oxygen content.

3. A cooling tower all-round safety protection and fault warning system according to claim 1, characterized in that: The "acoustic-chemical" coupling model includes: an acoustic feature extraction layer, a chemical feature mapping layer, a multimodal feature fusion layer, and a spatial positioning and risk assessment output layer; The acoustic feature extraction layer performs frequency domain analysis on the vibration noise spectrum data and extracts the acoustic feature vector; the chemical feature mapping layer calculates the corrosion factor based on the chemical parameter data to obtain the chemical feature vector and establishes a nonlinear mapping relationship between the chemical parameter data and the corrosion factor; the multimodal feature fusion layer fuses the acoustic feature vector and the chemical feature vector to generate a comprehensive corrosion risk feature vector; the spatial positioning and risk assessment output layer divides the cooling tower into N target areas, and performs corrosion risk assessment on the comprehensive feature vector of each target area through a pre-trained multi-layer neural network, and outputs the corrosion risk level of the N target areas and the corresponding spatial positioning information.

4. A cooling tower all-round safety protection and fault warning system according to claim 1, characterized in that: The process of constructing the initial association relationship of "corruption point-responsibility node" is as follows: Establish an area-responsibility mapping rule base and set M responsibility nodes for the N target areas of the cooling tower; the responsibility nodes include professional field identification, skill level, equipment configuration, and personnel number attributes; use an automatic matching algorithm to automatically match the corresponding responsibility nodes in the preset area-responsibility mapping rules based on the corrosion risk points in each target area, and calculate the response priority weight according to the risk level; the corrosion risk points are obtained based on the corrosion risk level and spatial positioning information; Based on the unique identification of the corrosion point, the target area to which it belongs, the identification of the responsible node, the corrosion risk level, the spatial positioning information, the response priority, and the expected processing time, an association matrix is ​​constructed to obtain the initial "corrosion point-responsible node" association relationship.

5. The cooling tower all-round safety protection and fault warning system according to claim 1, characterized in that: The responsibility coverage conflict measurement model includes: a spatial overlap calculation layer, a temporal conflict analysis layer, a resource competition evaluation layer and a comprehensive conflict measurement layer; The spatial overlap calculation layer calculates the proportion of overlapping responsibility coverage areas of corrosion risk points in the same target area by different responsibility nodes based on the initial association relationship of "corrosion point-responsibility node" to obtain the spatial overlap. The temporal conflict analysis layer calculates the temporal conflict probability based on the task execution time window of each responsibility node and the estimated processing time in the initial association relationship of "corrosion point-responsibility node" to obtain the temporal conflict degree. The resource competition assessment layer analyzes the degree of conflict in the demands of different responsibility nodes for the same maintenance resources, equipment configuration, and personnel deployment to obtain the resource competition degree. The comprehensive conflict measurement layer synthesizes the responsibility coverage overlap, temporal conflict, and resource competition to obtain the responsibility coverage conflict degree, and determines whether there is an intersection node based on the responsibility coverage conflict threshold.

6. A cooling tower all-round safety protection and fault warning system according to claim 1, characterized in that: The comprehensive scheduling scoring model includes: a task response delay evaluation layer, a load balancing calculation layer, a node priority weight allocation layer and a path efficiency optimization layer; The task response delay assessment layer predicts the task response time of each responsible node and calculates the task response delay based on historical execution data and the current task queue length, combined with the expected processing time in the initial association relationship of "corrosion point-responsibility node". The load balancing calculation layer monitors the current workload rate of each responsible node in real time and calculates the load balancing index based on the distribution of corrosion risk levels in the target area. The node priority weight allocation layer assigns priority weights based on the professional capabilities, equipment configuration, and personnel number attributes of the responsible nodes, combined with the corrosion risk level of each target area. The path efficiency optimization layer comprehensively considers the task response delay, load balancing index, and priority weight to generate the optimal response path and execution order for target areas with intersecting nodes.

7. The cooling tower all-round safety protection and fault warning system according to claim 1, characterized in that: The process of hierarchical collaborative response management is as follows: The urgency of maintenance tasks is graded according to the corrosion risk level of each target area. Based on the urgency of the maintenance tasks, the corrosion risk level and the priority of the responsible nodes, the maintenance tasks are pushed to the corresponding responsible nodes using the optimal response path according to the task urgency classification, and an execution schedule is automatically generated. The system provides maintenance task receipt confirmation, processing progress feedback and completion status update functions, tracks and records the entire maintenance task execution process, and realizes closed-loop management of maintenance tasks.

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