A VOCs activated carbon adsorption data processing efficiency monitoring method and system

Abstract

This invention proposes a method and system for monitoring the efficiency of VOCs activated carbon adsorption data processing. The method includes: collecting adsorption operating data of a VOCs activated carbon adsorption system and monitoring its flow in the data processing link to generate monitoring data packets; wherein each monitoring data packet includes VOCs concentration, adsorption bed temperature, adsorption bed differential pressure signal, and corresponding processing time sequence characterization obtained within the same acquisition cycle; based on the continuously acquired monitoring data packets, calculating the change amplitude of adsorption operating conditions in adjacent cycles to generate a demand intensity quantity for data processing timeliness requirements under the current adsorption state; dynamically selecting a corresponding monitoring strategy based on the demand intensity quantity, and combining the change amplitude of the adsorption bed differential pressure signal to determine the efficiency of the actual data processing process, generating a monitoring judgment quantity to trigger corresponding monitoring actions. This invention provides a practical and feasible technical path for improving the operational reliability and management refinement level of VOCs treatment facilities.

Description

Technical Field

[0001] This invention belongs to the field of VOCs activated carbon adsorption data processing efficiency monitoring, and particularly relates to a method and system for monitoring VOCs activated carbon adsorption data processing efficiency. Background Technology

[0002] In volatile organic compound (VOCs) treatment devices, activated carbon adsorption technology is widely used in industrial waste gas treatment due to its strong adaptability, stable operation, and high engineering maturity. During actual operation, adsorption devices typically use gas concentration, temperature, and pressure difference sensors installed at the inlet, outlet, and key locations on the adsorption bed to continuously monitor the adsorption process. The collected data is then sent to a monitoring system to determine adsorption effectiveness, operating status, and replacement timing. With increasingly stringent environmental regulations and the large-scale deployment of treatment facilities, the operation and management of adsorption devices are gradually shifting from manual inspections to centralized monitoring based on online data. Whether data can be processed promptly and completely and transformed into usable information directly impacts the safety of adsorption operation and the effectiveness of maintenance decisions. However, current technologies for monitoring adsorption systems still primarily focus on the process parameters themselves or the final adsorption effect, lacking systematic monitoring methods for the processing efficiency of data during acquisition, processing, and analysis, specifically tailored to the adsorption operating conditions. Existing monitoring schemes often employ fixed data processing rhythms and static efficiency evaluation standards, assuming that the data processing process has the same timeliness requirements under different operating conditions, failing to fully reflect the significant stage-specific and abrupt changes inherent in the activated carbon adsorption process. During periods of drastic change in adsorption conditions or when nearing saturation, data processing delays can directly impact critical judgments. Conversely, during periods of stable operation, excessive data processing intensity can lead to unnecessary resource consumption. Furthermore, when monitoring systems detect data delays or anomalies, they often only provide general alerts, failing to assess whether the delay constitutes an actual risk in light of the current adsorption operating status. They also lack the ability to formulate monitoring and response logic tailored to the specific adsorption conditions, resulting in a disconnect between monitoring results and actual operational needs. Therefore, how to conduct targeted efficiency monitoring of the data processing process while adhering to the evolutionary patterns of activated carbon adsorption conditions, and how to ensure that the monitoring results accurately reflect their support for adsorption operation and maintenance decisions, has become a pressing issue in current technologies. Summary of the Invention

[0003] The purpose of this invention is to propose a method and system for monitoring the efficiency of VOCs activated carbon adsorption data processing, thereby solving the above-mentioned problems.

[0004] To achieve the above objectives, a method for monitoring the efficiency of VOCs activated carbon adsorption data processing is provided in a first aspect of the present invention, the method comprising the following steps: The adsorption operating data of the VOCs activated carbon adsorption system is collected and its flow process in the data processing link is monitored to generate monitoring data packets; wherein, each monitoring data packet includes the VOCs concentration, adsorption bed temperature, adsorption bed pressure difference signal and corresponding processing time sequence characterization obtained in the same acquisition cycle. Based on the continuously collected monitoring data packets, the change range of adsorption conditions within adjacent periods is calculated. Combined with the processing timing characteristics in the current monitoring data packets, a demand intensity quantity for data processing timeliness under the current adsorption state is generated. The demand intensity quantity is used to comprehensively represent the relationship between the rate of change of the current adsorption state and the rhythm of the data processing process. Based on the required intensity, the corresponding monitoring strategy is dynamically selected, and the efficiency of the actual data processing process is determined by combining the change amplitude of the adsorption bed pressure difference signal, generating a monitoring judgment quantity to trigger the corresponding monitoring action.

[0005] Furthermore, the adsorption operating data includes at least the VOCs concentration at the inlet and outlet, the adsorption bed temperature, and the adsorption bed pressure difference.

[0006] Furthermore, the process of collecting adsorption condition data of the VOCs activated carbon adsorption system and monitoring its flow in the data processing link to generate monitoring data packets specifically includes: The starting time of each set of adsorption condition data entering the data processing link and the ending time of its completion of processing to form usable results are obtained. The difference between the end time and the start time is calculated to obtain the processing timing characterization. The set of adsorption operating conditions data and its corresponding processing time sequence parameters are combined to form a monitoring data package.

[0007] Furthermore, the demand intensity is calculated based on the variation amplitude of VOCs concentration, the variation amplitude of adsorption bed temperature, the variation amplitude of adsorption bed differential pressure signal, and the processing time sequence characterization within adjacent periods.

[0008] Furthermore, the amplitude of the change in the adsorption bed pressure difference signal is used to adjust the sensitivity of the demand intensity to the processing time sequence when the pressure difference changes significantly.

[0009] Furthermore, the dynamic selection of the corresponding monitoring strategy specifically includes: A monitoring strategy table indexed by the demand intensity range is pre-defined. Based on the range where the currently calculated demand intensity is located, the corresponding target processing level parameters and allowable fluctuation range parameters are retrieved.

[0010] Furthermore, the efficiency of the actual data processing process is determined by combining the change amplitude of the adsorption bed pressure difference signal, and a monitoring judgment quantity is generated, specifically as follows: The monitoring and judgment quantity is obtained by multiplying the demand intensity quantity by the current processing time sequence characterization quantity and adding a penalty term related to the change amplitude of the adsorption bed pressure difference; The monitoring judgment quantity is compared with the judgment boundary determined according to the selected monitoring strategy to obtain the monitoring status.

[0011] Furthermore, the penalty term is proportional to the logarithm of the change in the differential pressure signal of the adsorption bed, which causes the penalty intensity to increase rapidly during the stage of sudden increase in differential pressure.

[0012] Furthermore, the step of triggering the monitoring action may, based on the monitoring status, perform at least one action, including refreshing the interface status, pushing alarm messages, or recording abnormal events.

[0013] In a second aspect of the present invention, a VOCs activated carbon adsorption data processing efficiency monitoring system is provided, the system comprising: The adsorption condition acquisition unit is used to acquire adsorption condition data of the VOCs activated carbon adsorption system and monitor its flow process in the data processing link to generate monitoring data packets; wherein, each set of monitoring data packets includes the VOCs concentration, adsorption bed temperature, adsorption bed pressure difference signal and corresponding processing time sequence characterization obtained in the same acquisition cycle. The data analysis unit is used to calculate the change range of adsorption conditions in adjacent periods based on the continuously collected monitoring data packets, and generate a demand intensity quantity for data processing timeliness requirements under the current adsorption state by combining the processing time sequence characterization quantity in the current monitoring data packets; the demand intensity quantity is used to comprehensively represent the relationship between the rate of change of the current adsorption state and the rhythm of the data processing process. The action generation unit is used to dynamically select the corresponding monitoring strategy based on the required intensity, combine the change amplitude of the adsorption bed pressure difference signal to determine the efficiency of the actual data processing process, generate a monitoring judgment quantity, and trigger the corresponding monitoring action.

[0014] The beneficial technical effects of the present invention are at least as follows: This invention focuses on the intrinsic correlation between operating conditions and data processing during VOCs activated carbon adsorption. It proposes a data processing efficiency monitoring scheme driven by adsorption conditions. The core of this scheme is to transform the evaluation of data processing efficiency from static performance indicators into an expression of operational requirements that matches the current adsorption state, thereby dynamically monitoring and handling the actual data processing process. This invention unifies the organization of adsorption condition data within the same acquisition cycle with its processing characteristics in the data processing chain, forming a monitoring data foundation that simultaneously reflects the evolution of operating conditions and the processing rhythm. Based on this, it further extracts the intensity of adsorption state changes, constructing a requirement expression that reflects the timeliness requirements of data processing under the current adsorption operating conditions. This allows efficiency monitoring to adjust naturally with changes in adsorption conditions, rather than relying on fixed thresholds. Furthermore, this invention couples the above requirement expression with the actual data processing process and introduces a constraint mechanism related to changes in adsorption bed resistance. This enables data processing efficiency monitoring to maintain higher sensitivity to key risk stages in the adsorption process while maintaining reasonable monitoring intensity during stable operating phases, thus forming a monitoring logic highly matched to the actual activated carbon adsorption engineering. Through the above-mentioned technical concept, this invention realizes data processing efficiency monitoring for adsorption operation needs, so that the monitoring results have clear engineering significance and can directly support operation and maintenance management and risk warning, providing a practical and feasible technical path for improving the operational reliability and management refinement of VOCs treatment facilities. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for monitoring the efficiency of VOCs activated carbon adsorption data processing. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] like Figure 1 As shown in the embodiment of the present invention, a method for monitoring the data processing efficiency of VOCs activated carbon adsorption is provided. The method includes: S1. Collect adsorption operating data of the VOCs activated carbon adsorption system and monitor its flow in the data processing link to generate monitoring data packets; wherein, each monitoring data packet includes the VOCs concentration, adsorption bed temperature, adsorption bed pressure difference signal and corresponding processing time sequence characterization obtained in the same acquisition cycle.

[0019] Specifically, this step revolves around the actual operation process of VOCs activated carbon adsorption treatment facilities. Its core lies in constructing a basic data object that can stably reflect the evolution of adsorption conditions and the characteristics of data processing, without altering the existing device structure or introducing additional data types. In typical engineering sites, VOCs activated carbon adsorption systems usually perform data acquisition tasks at fixed cycles. For example, the on-site acquisition module reads concentration signals from VOCs concentration detection devices at the inlet and outlet according to a preset sampling cycle, and simultaneously reads corresponding temperature and differential pressure signals from the temperature and differential pressure measurement devices of the adsorption bed. These three types of signals are considered as adsorption conditions within the same acquisition cycle and are combined in chronological order in the acquisition module to form a complete set of adsorption condition data. This combination process does not rely on complex calculation logic but is achieved by the acquisition module synchronously reading multiple signals under the same acquisition trigger condition, ensuring that subsequent processing can clearly identify that these signals belong to the same adsorption condition description.

[0020] Furthermore, at the data processing system level, the collected adsorption condition data is sent to a data processing path determined during the system deployment phase. This path typically consists of several sequentially connected processing nodes, such as edge processing nodes for data access, intermediate processing nodes for processing and forwarding, and data processing nodes for generating monitoring results. This processing path maintains structural stability during system operation; its role is to carry out the processing flow of adsorption condition data, rather than to perform additional data analysis. When a set of adsorption condition data is completely collected and enters the processing path, the system records an entry time marker at the path's starting node. This marker is generated through the processing node's internal time recording mechanism and establishes a correspondence with the data set. Subsequently, the data set passes through subsequent processing nodes according to the predetermined path. When it completes the predetermined processing flow and generates data results usable for monitoring, the system records the corresponding completion time marker at the path's end. Throughout the entire process, the system always uses "a set of adsorption condition data" as the smallest processing unit, avoiding the mixing of data from different collection periods, thus ensuring consistency in temporal semantics.

[0021] Based on the aforementioned start and finish time markers, a time series quantity reflecting the overall processing characteristics of each set of adsorption condition data in the data processing system can be constructed. This time series quantity is given by the following formula:

[0022] in, This indicates the time marker corresponding to when the set of adsorption condition data enters the starting node of the data processing path. This marker is generated by the acquisition module or edge processing node after confirming a complete set of VOCs concentration, temperature and differential pressure signals. This indicates the completion time marker when the same set of adsorption condition data completes the predetermined processing flow and forms monitoring data results. This marker is generated by the processing node at the end of the path when the processing flow ends. This is used to characterize the overall processing features of the adsorption condition data in the current data processing path, reflecting the processing process that adsorption condition information undergoes from generation to the formation of usable monitoring results. Through this method, each set of VOCs concentration, adsorption bed temperature, and adsorption bed pressure difference signals obtained within the same acquisition cycle can be associated with a clearly corresponding processing time series characterization. They are related. The system combines the two to form a monitoring data packet with a fixed structure and clear semantics.

[0023] S2. Based on the continuously collected monitoring data packets, calculate the change range of adsorption conditions within adjacent periods, and combine it with the processing timing characterization in the current monitoring data packets to generate the demand intensity of data processing timeliness requirements under the current adsorption state; the demand intensity is used to comprehensively represent the relationship between the rate of change of the current adsorption state and the rhythm of the data processing process.

[0024] Specifically, this step uses the monitoring data packet generated in Step 1 as the sole processing object. Under the continuously operating VOCs activated carbon adsorption scenario, the obtained adsorption condition information and data processing characteristics are further transformed into a data processing requirement expression that can directly constrain subsequent efficiency monitoring behavior. This requirement expression describes the timeliness level that the data should possess to form usable information under the current adsorption state evolution conditions. Its core value lies in incorporating "adsorption process changes" and "data processing rhythm" into the same computational semantics, providing a realistic and executable basis for subsequent efficiency monitoring.

[0025] In the engineering implementation, the system continuously receives the monitoring data packet sequence output from step one. Each monitoring data packet corresponds to a complete acquisition cycle, containing the VOCs concentration, adsorption bed temperature, adsorption bed differential pressure signals within that cycle, and the corresponding data processing timing parameters. In the edge processing module, these monitoring data packets are written to a short-term buffer in the order of arrival. When a new monitoring data packet arrives, the system automatically retrieves the previous monitoring data packet that is adjacent in time as the comparison object, thereby forming the working condition change relationship between adjacent collection cycles without introducing additional time windows or complex historical modeling.

[0026] Furthermore, in the specific processing, the system performs time-series alignment of the operating condition data in two adjacent monitoring data packets and directly calculates the magnitude of changes in operating conditions. For VOCs concentration, the system uses the difference in VOCs concentration between the inlet and outlet as a characterizing quantity and calculates the magnitude of change of this concentration difference within adjacent acquisition periods to obtain... For the adsorption bed temperature, the system calculates the temperature change amplitude between adjacent cycles in the same way, obtaining... For the pressure difference in the adsorption bed, the system calculates the pressure difference variation amplitude between adjacent periods to obtain... The aforementioned changes are all derived from the original values ​​already present in the monitoring data packet, without introducing additional smoothing, filtering, or prediction operations, thus maintaining consistency with the data structure of step one. In actual operation, this processing method can stably reflect typical physical characteristics such as "sudden concentration changes," "temperature fluctuations," and "bed resistance changes" during the adsorption process.

[0027] While obtaining the magnitude of changes in operating conditions, the system directly reads the data processing timing parameters already contained in the current monitoring data packet. This is used to reflect the overall processing characteristics of the adsorption condition information from entering the data processing chain to forming usable results. Based on the above information, the system constructs a data processing demand intensity quantity. This is used to comprehensively characterize the relationship between the rate of change of the current adsorption state and the rhythm of the data processing. To enable the combination of signals from different operating conditions within the same evaluation framework, the system further converts the changes in inlet and outlet VOCs concentration difference, adsorption bed temperature, and adsorption bed pressure difference within adjacent acquisition cycles into corresponding change characterization quantities based on a preset reference range. , and ,in Depend on The ratio to the concentration reference range is obtained. Depend on The ratio to the temperature reference range is obtained. Depend on The ratio to the differential pressure reference range is obtained; the reference range is pre-written into a configuration file based on historical operating data or design conditions during the unit commissioning phase, and is used to characterize the typical variation range of each operating condition under normal operating conditions. This demand intensity is calculated as follows:

[0028] in, This represents the concentration characterization quantity corresponding to the change in the concentration difference between inlet and outlet VOCs within adjacent collection periods, derived from... Calculated by converting the concentration to a preset reference range; The temperature characterization quantity represents the temperature change of the adsorption bed within adjacent collection cycles, derived from... Calculated by converting to a preset temperature reference range; The pressure differential characterization quantity represents the pressure differential change corresponding to the pressure differential change in the adsorption bed within adjacent collection cycles, derived from... Calculated by converting with the preset differential pressure reference range; This represents the processing timing characteristic of the current monitoring data packet, which is... Calculated relative to the preset processing timing reference range; This expression represents the intensity of data processing demand under the current adsorption state, describing the realistic requirements for data processing timeliness under these conditions. By unifying the magnitude of changes in operating conditions and the parameters characterizing the processing timeline into representative quantities before combining them, a stable correspondence is established between the calculation of the demand intensity and the changes in the current adsorption state. The formula introduces... The term is used to increase the weight of the impact of pressure difference changes on demand intensity, so that data processing needs in scenarios such as gradual bed saturation, channel blockage, or sudden increase in resistance can be amplified in a timely manner; the denominator contains The term then makes the demand intensity a characteristic of the treatment time series when the pressure difference changes significantly. The sensitivity is adjusted synchronously, thereby obtaining It better meets the actual monitoring needs during the operation of activated carbon adsorption.

[0029] At the engineering implementation level, the system will calculate the... Pressure difference characterization and the corresponding range of operating conditions The required fields to write into the current monitoring data packet, where As with The directly related monitoring computation is stored along with the data packet. The recorded differential pressure changes are used for subsequent monitoring result interpretation and event backtracking, thus forming a monitoring data package that expresses data processing requirements. In subsequent steps, this data package is directly used to select efficiency monitoring constraints and execute data processing efficiency monitoring, so that the basic data object formed in step one logically evolves into an input carrier that can drive monitoring decisions.

[0030] S3. Based on the required intensity, dynamically select the corresponding monitoring strategy, combine the change amplitude of the adsorption bed pressure difference signal to determine the efficiency of the actual data processing process, generate monitoring judgment quantity, and trigger the corresponding monitoring action.

[0031] Specifically, this step follows the chain of input objects formed in step two and executes accordingly: carrying the data processing requirements. After the monitoring data packet enters the monitoring execution module, the system directly reads the contents of the data packet. The data processing timing profile formed in step one And the differential pressure characterization quantity written in step two Furthermore, under the same data semantics, this step monitors the efficiency of the actual data processing process and triggers actions. Its role in the overall solution is to translate the requirement of "how fast data processing is needed under the current adsorption state" into an executable decision, enabling monitoring actions to change with the actual evolution of the adsorption process. This, in the context of VOCs activated carbon adsorption, characterized by strong phases and short risk windows, creates stable and usable operational management signals.

[0032] In engineering implementation, the monitoring execution module processes monitoring data packets serially, with each data packet being the smallest processing unit. Each monitoring data packet has already been paired with the previous data packet and its value calculated in the edge processing module. Therefore, the monitoring execution module does not need to trace back to a longer history; it only needs to read the fields of the current data packet immediately upon receiving it to complete the monitoring. The monitoring execution module internally maintains a preset monitoring policy table, which uses... Using the current interval as an index, return two parameters required for this monitoring: one describing the amount of time series data processed under the current demand intensity. The target level; another item describes the allowable fluctuation range. This strategy table can be written during the device commissioning phase based on field operation experience, and stored in a fixed configuration in the configuration file of the edge processing module or the upper-level monitoring system; the indexing method of the strategy table adopts interval mapping, which can be implemented in engineering with several segmented thresholds, for example, ... The parameters are divided into several levels according to their size, and each level corresponds to a set of parameters, thereby avoiding the uncontrollability caused by continuous function fitting.

[0033] Furthermore, to ensure the determination of typical anomalies in the adsorption scenario is targeted, this step introduces a penalty term related to changes in adsorption bed resistance into the monitoring quantity construction. This allows for the timely amplification of sudden pressure increases caused by gradual bed blockage or near-saturation. In practice, the monitoring execution module obtains the pressure difference variation amplitude between adjacent periods from the current monitoring data packet. (This amplitude is calculated in step two) The result has already been obtained from adjacent data packets, and in the engineering implementation, it is stored in the derived field of the current data packet as an intermediate result of the requirement calculation, and then compared with... , Together they are used to form the monitoring decision quantity. Monitoring decision quantity Calculated using the following formula:

[0034] in, This indicates the intensity of data processing requirements obtained in step two, derived from the requirement field of the current monitoring data packet; This represents the processing timing characteristic of the current monitoring data packet, derived from the data processing timing characterization formed in step one. It is calculated relative to the preset processing timing reference range and stored as a derived field of the monitoring data packet; This represents the differential pressure characteristic corresponding to the current monitoring data packet, determined in step two based on the differential pressure change within adjacent acquisition periods. And the preset differential pressure reference range is converted; The penalty coefficient is derived from the monitoring policy table and the current... Configuration values ​​corresponding to the range; This represents a comprehensive monitoring and judgment quantity, used to uniformly measure the time-sensitive requirements under adsorption conditions and the performance of the treatment process. The logarithmic term in this formula characterizes the pressure difference. Nonlinear amplification is applied to maintain a gradual effect when the pressure difference changes are small, and to rapidly increase the penalty intensity when the pressure difference changes significantly, thereby improving the ability to identify anomalies in the near-saturation stage and the stage of sudden increase in resistance.

[0035] In obtaining Subsequently, the monitoring execution module constructs a decision boundary based on the allowed range returned by the policy table and generates a monitoring status. This decision boundary adopts a "demand intensity-driven dynamic threshold," and the threshold value is related to... Interval binding changes with the strategy table configuration. In the engineering implementation, the decision boundary is represented by a scalar threshold, which is stored in segments in the configuration file; at runtime, it is determined by... After obtaining the threshold from the interval index, the comparison is completed. The comparison result is used to generate a monitoring status identifier and form a set of monitoring actions corresponding to that status. The action set can be implemented using a rule table: each monitoring status corresponds to a set of action codes, which drive the monitoring system to execute specific actions, such as refreshing the status on the monitoring interface, generating an alarm message and pushing it to the operation and maintenance terminal, or writing abnormal events into the operation log. The execution of actions is completed in the edge processing module or monitoring platform in an asynchronous queue manner. Each task in the queue contains a monitoring data packet identifier, a monitoring status identifier, a trigger time marker, and a list of action codes, thereby ensuring that action execution is decoupled from the main data processing link and avoiding interference from the action execution itself. The generation process.

[0036] A real-world operational example illustrates the logical loop of this step: When the pressure difference in the adsorption bed gradually increases and then suddenly surges over a certain period, while the VOCs concentration difference begins to fluctuate, the calculations in step two... The value will be in the higher range; accordingly, the policy table will select a more stringent monitoring configuration and assign a higher penalty coefficient. This step involves calculation. A significant increase in the time-logarithmic penalty term causes the monitoring status to quickly enter an abnormal range, triggering an alarm on the operations and maintenance side and recording the corresponding monitoring event. Conversely, during periods of stable operating conditions and slow pressure differential changes, The range is relatively low, so a moderate configuration is selected for the strategy table. Mainly composed of The system determines whether the monitoring status is stable and output as normal or attenuated, making the monitoring system's actions more aligned with the actual needs and rhythm of adsorption operation.

[0037] This invention also provides a VOCs activated carbon adsorption data processing efficiency monitoring system, the system comprising: The adsorption condition acquisition unit is used to acquire adsorption condition data of the VOCs activated carbon adsorption system and monitor its flow process in the data processing link to generate monitoring data packets; wherein, each set of monitoring data packets includes the VOCs concentration, adsorption bed temperature, adsorption bed pressure difference signal and corresponding processing time sequence characterization obtained in the same acquisition cycle. The data analysis unit is used to calculate the change range of adsorption conditions in adjacent periods based on the continuously collected monitoring data packets, and generate a demand intensity quantity for data processing timeliness requirements under the current adsorption state by combining the processing time sequence characterization quantity in the current monitoring data packets; the demand intensity quantity is used to comprehensively represent the relationship between the rate of change of the current adsorption state and the rhythm of the data processing process. The action generation unit is used to dynamically select the corresponding monitoring strategy based on the required intensity, combine the change amplitude of the adsorption bed pressure difference signal to determine the efficiency of the actual data processing process, generate a monitoring judgment quantity, and trigger the corresponding monitoring action.

[0038] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0039] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or units may be electrical, mechanical, or other forms.

[0040] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0041] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for monitoring the efficiency of VOCs activated carbon adsorption data processing, characterized in that, The method includes: The adsorption operating data of the VOCs activated carbon adsorption system is collected and its flow process in the data processing link is monitored to generate monitoring data packets; wherein, each monitoring data packet includes the VOCs concentration, adsorption bed temperature, adsorption bed pressure difference signal and corresponding processing time sequence characterization obtained in the same acquisition cycle. Based on the continuously collected monitoring data packets, the change range of adsorption conditions within adjacent periods is calculated. Combined with the processing timing characteristics in the current monitoring data packets, a demand intensity quantity for data processing timeliness under the current adsorption state is generated. The demand intensity quantity is used to comprehensively represent the relationship between the rate of change of the current adsorption state and the rhythm of the data processing process. Based on the required intensity, the corresponding monitoring strategy is dynamically selected, and the efficiency of the actual data processing process is determined by combining the change amplitude of the adsorption bed pressure difference signal, generating a monitoring judgment quantity to trigger the corresponding monitoring action.

2. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 1, characterized in that, The adsorption operating data includes at least the VOCs concentration at the inlet and outlet, the adsorption bed temperature, and the adsorption bed pressure difference.

3. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 2, characterized in that, The process of collecting adsorption condition data from the VOCs activated carbon adsorption system and monitoring its flow in the data processing chain to generate monitoring data packets specifically involves: The starting time of each set of adsorption condition data entering the data processing link and the ending time of its completion of processing to form usable results are obtained. The difference between the end time and the start time is calculated to obtain the processing timing characterization. The set of adsorption operating conditions data and its corresponding processing time sequence parameters are combined to form a monitoring data package.

4. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 1, characterized in that, The demand intensity is calculated based on the changes in VOCs concentration, adsorption bed temperature, and adsorption bed differential pressure signal within adjacent periods, as well as the processing time sequence characterization.

5. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 4, characterized in that, The amplitude of the pressure difference signal in the adsorption bed is used to adjust the sensitivity of the demand intensity to the processing time sequence when the pressure difference changes significantly.

6. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 1, characterized in that, The dynamic selection of the corresponding monitoring strategy is specifically as follows: A monitoring strategy table indexed by the demand intensity range is pre-defined. Based on the range where the currently calculated demand intensity is located, the corresponding target processing level parameters and allowable fluctuation range parameters are retrieved.

7. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 6, characterized in that, The efficiency of the actual data processing process is determined by combining the change amplitude of the adsorption bed pressure difference signal, and a monitoring judgment quantity is generated, specifically as follows: The monitoring and judgment quantity is obtained by multiplying the demand intensity quantity by the current processing time sequence characterization quantity and adding a penalty term related to the change amplitude of the adsorption bed pressure difference; The monitoring judgment quantity is compared with the judgment boundary determined according to the selected monitoring strategy to obtain the monitoring status.

8. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 7, characterized in that, The penalty term is proportional to the logarithm of the change in the differential pressure signal of the adsorption bed, which causes the penalty intensity to increase rapidly during the stage of sudden increase in differential pressure.

9. The method for monitoring the efficiency of VOCs activated carbon adsorption data processing according to claim 7, characterized in that, The step of triggering the monitoring action shall, based on the monitoring status, perform at least one of the following actions: refreshing the interface status, pushing alarm messages, or recording abnormal events.

10. A VOCs activated carbon adsorption data processing efficiency monitoring system, characterized in that, The system includes: The adsorption condition acquisition unit is used to acquire adsorption condition data of the VOCs activated carbon adsorption system and monitor its flow process in the data processing link to generate monitoring data packets; wherein, each set of monitoring data packets includes the VOCs concentration, adsorption bed temperature, adsorption bed pressure difference signal and corresponding processing time sequence characterization obtained in the same acquisition cycle. The data analysis unit is used to calculate the change range of adsorption conditions in adjacent periods based on the continuously collected monitoring data packets, and generate a demand intensity quantity for data processing timeliness requirements under the current adsorption state by combining the processing time sequence characterization quantity in the current monitoring data packets; the demand intensity quantity is used to comprehensively represent the relationship between the rate of change of the current adsorption state and the rhythm of the data processing process. The action generation unit is used to dynamically select the corresponding monitoring strategy based on the required intensity, combine the change amplitude of the adsorption bed pressure difference signal to determine the efficiency of the actual data processing process, generate a monitoring judgment quantity, and trigger the corresponding monitoring action.

Images