Chemical liquid management analysis method and device, storage medium and computer equipment

Abstract

According to the chemical liquid management analysis method and device, the storage medium and the computer equipment, when the analysis instruction is received, the target time period is determined through the received analysis instruction, the operation data of the liquid recovery system is obtained, the analysis range can be accurately positioned, and comprehensive data can be collected. And analyzing the long-term and short-term characteristics of the operation data in the target time period, and determining the use trend of the liquid, so that accurate mastering and prospective prediction of the use condition of the liquid can be realized. And then determining the total recovery amount and the recovery duration of the operation data in the target time period, calculating the recovery speed, and further refining the management indexes. Finally, a liquid management analysis report in the target time period is generated, the liquid management analysis report can effectively integrate and analyze data, the chemical liquid use and recovery process is optimized, and powerful support is provided for management decision making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and particularly relates to a chemical liquid management analysis method and device, a storage medium and a computer device. BACKGROUND

[0002] In an experimental environment and industrial production, insulating oil is an important insulating and cooling medium, and its use and recycling management is directly related to operation safety and resource utilization efficiency. At present, the management of insulating oil mainly relies on a traditional manual record management scheme, and the sampling amount, use amount and recycling amount of insulating oil are recorded manually for statistics and control. This scheme has many defects, such as easy recording errors, low efficiency and difficulty in achieving precise control.

[0003] At present, some simple liquid level monitoring schemes are also used in some scenes, such as a floating ball type liquid level sensor or a mechanical flow meter, but only basic liquid level alarm or rough flow statistics can be achieved, and an integrated management system is not formed. This scheme cannot meet the fine management needs in complex environments and cannot effectively integrate data for in-depth analysis and decision support. SUMMARY

[0004] The purpose of the present application is to at least solve one of the above technical defects, in particular, the technical defect that the prior art cannot meet the fine management needs in complex environments and cannot effectively integrate data for in-depth analysis and decision support.

[0005] In a first aspect, the present application provides a chemical liquid management analysis method applied to a control unit in a liquid recycling system, and the method comprises:

[0006] When an analysis instruction is received, a target time period is determined according to the analysis instruction, and running data of the liquid recycling system is acquired;

[0007] Long-term and short-term characteristics of the running data in the target time period are analyzed, and a liquid use trend is determined according to the long-term and short-term characteristics;

[0008] A total recycling amount and a recycling time length of the running data in the target time period are determined, so as to determine a recycling speed according to the total recycling amount and the recycling time length;

[0009] A liquid management analysis report in the target time period is generated according to the liquid use trend, the total recycling amount, the recycling time length and the recycling speed.

[0010] In one of the embodiments, the target time period is determined according to the analysis instruction, which comprises:

[0011] A time type in the analysis instruction is acquired;

[0012] When the time type is a time interval, a target time period carried in the analysis instruction is acquired;

[0013] When the time type is a time length, a time value carried in the analysis instruction is acquired, and a target time period is determined according to the time value and a current time as a right endpoint.

[0014] In one of the embodiments, the long-term and short-term features of the operation data in the target time period are analyzed, and a liquid use trend is determined according to the long-term and short-term features, including:

[0015] A time series data sequence composed of liquid consumption in the target time period is acquired from the operation data;

[0016] A seasonal trend decomposition algorithm is used to decompose the time series data sequence to obtain a seasonal component and a trend component;

[0017] The long-term and short-term features of the operation data in the target time period are determined according to the seasonal component and the trend component;

[0018] The long-term and short-term features are input into a preset trend analysis model to obtain a liquid use trend in the target time period.

[0019] In one of the embodiments, the long-term and short-term features of the operation data in the target time period are determined according to the seasonal component and the trend component, including:

[0020] Short-term fluctuation features are extracted from the seasonal component, including at least one of a daily peak time period, a daily average fluctuation amplitude, and a fluctuation regularity index;

[0021] Long-term change features are extracted from the trend component, including at least one of a quarterly growth rate, a trend direction, and a turning point occurrence time;

[0022] The long-term and short-term features are obtained by merging the short-term fluctuation features and the long-term change features.

[0023] In one of the embodiments, the total recovery amount and the recovery time length of the operation data in the target time period are determined, and a recovery speed is determined according to the total recovery amount and the recovery time length, including:

[0024] Recovery data in the target time period is extracted from the operation data;

[0025] The total recovery amount and the recovery time length are determined according to the recovery data, and a quotient of the total recovery amount and the recovery time length is calculated to obtain a recovery speed.

[0026] In one embodiment, generating a liquid management analysis report for the target time period based on the liquid usage trend, the total recovery volume, the recovery duration, and the recovery speed includes:

[0027] Determine the report template based on the length of the target time period;

[0028] The liquid usage trend, total recovery volume, recovery duration, and recovery speed are filled into the report template to obtain a liquid management analysis report for the target time period.

[0029] In one embodiment, the method further includes:

[0030] The sampling amount at each moment within the target time period is determined based on the liquid usage trend. If there is a moment when the sampling amount exceeds the first preset threshold, an alarm is triggered.

[0031] In addition, the system monitors the pulse count change rate during the liquid recovery process and triggers an alarm when the pulse count change rate exceeds a second preset threshold.

[0032] Secondly, this application provides a chemical liquid management and analysis device, applied to a control unit in a liquid recovery system, the device comprising:

[0033] The instruction receiving module is used to determine the target time period according to the analysis instruction when it receives the analysis instruction, and to obtain the operating data of the liquid recovery system.

[0034] The feature analysis module is used to analyze the long-term and short-term characteristics of the operating data within the target time period, and to determine the liquid usage trend based on the long-term and short-term characteristics;

[0035] The data calculation module is used to determine the total amount of data recovered and the recovery duration of the operating data within the target time period, so as to determine the recovery speed based on the total amount of data recovered and the recovery duration.

[0036] The report generation module is used to generate a liquid management analysis report for the target time period based on the liquid usage trend, the total amount recovered, the recovery duration, and the recovery speed.

[0037] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the chemical liquid management and analysis method as described in any of the above embodiments.

[0038] Fourthly, this application provides a liquid recovery system, which includes a control unit, a control panel, a liquid level sensor, a weighing device, and a recovery tank;

[0039] The control panel is connected to the control unit and is used for visual input of various commands;

[0040] The liquid level sensor is installed on the top of the recovery tank to monitor the liquid level changes in the recovery tank in real time and upload the liquid level changes to the control unit.

[0041] The weighing device is used for quantity control during sampling;

[0042] The control unit is used to perform the steps of the chemical liquid management and analysis method as described in any of the above embodiments.

[0043] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0044] The chemical liquid management and analysis method, apparatus, storage medium, and computer equipment provided in this application, upon receiving an analysis command, determine the target time period and acquire operational data from the liquid recovery system, enabling precise positioning of the analysis scope and comprehensive data collection. Next, the long-term and short-term characteristics of the operational data within the target time period are analyzed to determine liquid usage trends, thus achieving accurate understanding and forward-looking prediction of liquid usage. Then, the total recovery volume and recovery duration within the target time period are determined, and the recovery rate is calculated, further refining management indicators. Finally, a liquid management analysis report for the target time period is generated. This report effectively integrates and analyzes data, optimizes the chemical liquid use and recovery process, and provides strong support for management decisions. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A schematic flowchart of a chemical liquid management and analysis method provided in this application embodiment;

[0047] Figure 2 A schematic diagram of a chemical liquid management and analysis device provided in this application embodiment;

[0048] Figure 3 This is a schematic diagram of a liquid recovery system provided in an embodiment of this application. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] In one embodiment, this application provides a chemical liquid management and analysis method. The following embodiments illustrate the application of this method to a control unit in a liquid recovery system, hereinafter referred to as the system. Figure 1 As shown, the method includes:

[0051] S101: When an analysis command is received, the target time period is determined according to the analysis command, and the operating data of the liquid recovery system is obtained.

[0052] A liquid recovery system refers to a collection of equipment and related control software used for collecting, storing, and processing liquids. The target time period refers to a specific time range specified in the analysis instructions, used to define the time boundary for data analysis. For example, it could be an hour, a day, or a week, depending on the analysis requirements. Operational data refers to various data generated by the liquid recovery system during operation, including liquid flow rate, liquid level, recovery time, and equipment status.

[0053] In this step, the user can trigger analysis commands through a visual interface. When the control unit in the system receives the analysis command, it first parses the command to determine the target time period. Specifically, this is done by identifying the time parameters in the analysis command and converting them into a time format that the system can process internally. Simultaneously, the system can retrieve operational data for the specified time period from the liquid recovery system's database or real-time data interface. For example, based on the target time period, data records that meet the time range are filtered from the stored operational data to obtain the operational data.

[0054] Specifically, a timestamp filtering mechanism can be used to quickly locate data within a target time period. For example, if the target time period is from 8:00 AM to 5:00 PM, all data records generated within this time period can be retrieved. These records may include flow rate data per minute, liquid level change data per hour, etc. Furthermore, to ensure data accuracy and integrity, the system can also perform data validation, checking for missing data or outliers, and performing necessary data cleaning and repair.

[0055] S102: Analyze the long-term and short-term characteristics of the operating data within the target time period, and determine the liquid usage trend based on the long-term and short-term characteristics.

[0056] Among them, short-term and long-term characteristics refer to the time-scale characteristics exhibited by operational data within the target time period, including short-term fluctuation characteristics and long-term change characteristics. Liquid usage trends refer to the overall usage and development direction of liquids within the target time period, including increases, decreases, or stabilization in usage.

[0057] In this step, when analyzing the operational data, the first step is to divide the data into time scales for long-term and short-term characteristic analysis, thereby determining the long-term and short-term characteristics. Specifically, time windowing techniques can be used to divide the data within the target time period into multiple time windows. For long-term characteristic analysis, larger time windows can be set, such as monthly or weekly. For short-term characteristic analysis, smaller time windows can be set, such as daily or hourly. In this way, statistical indicators such as mean, maximum, minimum, and standard deviation can be calculated for each time window. This ultimately yields the long-term and short-term characteristics.

[0058] S103: Determine the total amount of data recovered and the recovery duration within the target time period, so as to determine the recovery rate based on the total amount of data recovered and the recovery duration.

[0059] The recovery rate refers to the ratio of the total amount of liquid recovered by the liquid recovery system to the recovery time within a target time period. It is usually expressed as the amount of liquid recovered per unit time and reflects the operating efficiency of the liquid recovery system.

[0060] In this step, the system needs to extract all recovery records for the target time period from the operational data. These records include the start and end times of each recovery, as well as the amount of liquid recovered each time (determined by data from the liquid level sensor). Using this data, the total recovery volume and recovery duration can be calculated, and finally, the recovery rate can be calculated based on the total recovery volume and recovery duration.

[0061] S104: Generate a liquid management analysis report for the target time period based on liquid usage trends, total recovery volume, recovery duration, and recovery speed.

[0062] The liquid management analysis report is a comprehensive report generated within a target time period based on key indicators such as liquid usage trends, total recovery volume, recovery duration, and recovery speed. This report reflects the operational status of the liquid recovery system and provides detailed data analysis and management recommendations.

[0063] This step integrates and analyzes key indicators such as liquid usage trends, total recovery volume, recovery time, and recovery speed to generate a comprehensive report. Furthermore, this data can be analyzed in depth to create charts and text descriptions, which are then added to the report to obtain the final liquid management analysis report.

[0064] Specifically, during in-depth data analysis, data such as liquid usage trends, total recovery volume, recovery time, and recovery speed can be categorized and summarized. For example, liquid usage trends can be displayed using line charts, total recovery volume and recovery time using bar charts, and recovery speed using scatter plots. These charts can intuitively reflect the operating status and trends of the liquid recovery system. Simultaneously, textual descriptions can be generated to interpret and analyze the data in the charts, providing management suggestions and optimization measures.

[0065] In the above embodiments, upon receiving an analysis command, the system determines the target time period and acquires operational data from the liquid recovery system, enabling precise positioning of the analysis scope and comprehensive data collection. Next, the system analyzes the long- and short-term characteristics of the operational data within the target time period and determines liquid usage trends, thus achieving accurate understanding and forward-looking prediction of liquid usage. Then, the system determines the total recovery volume and duration within the target time period and calculates the recovery rate, further refining management indicators. Finally, a liquid management analysis report for the target time period is generated. This report effectively integrates and analyzes data, optimizes the chemical liquid usage and recovery process, and provides strong support for management decisions.

[0066] In one embodiment, determining the target time period according to the analysis instructions includes:

[0067] S1: Get the time type in the analysis command.

[0068] S2: When the time type is time interval, retrieve the target time period carried in the analysis command.

[0069] S3: When the time type is time length, obtain the time value carried in the analysis command, and determine the target time period based on the time value, with the current time as the right endpoint.

[0070] Among them, time type refers to the format of time information specified in the analysis instruction, which includes two types: time interval and time length.

[0071] In this embodiment, the method for determining the target time is determined by parsing the time type in the analysis command. When the time type is a time interval, the time period carried in the analysis command can be obtained and identified as the target time period. When the time type is a time length, the time value carried in the analysis command can be obtained, and then a time period is determined using this time value as the length and the current time as the right endpoint. This time period is the target time period. For example, if the time value is 30 days and the current time is February 1, 2023, then the target time period is from January 2, 2023 to February 1, 2023.

[0072] Understandably, by identifying the time type, user-specified time information can be accurately processed, whether it is a specific time interval or a relative time length, thereby flexibly adapting to different analysis needs and providing accurate data analysis services.

[0073] In one embodiment, the long-term and short-term characteristics of operational data within a target time period are analyzed, and liquid usage trends are determined based on these characteristics, including:

[0074] S1: Obtain a time-series data sequence consisting of liquid consumption within the target time period from the operational data.

[0075] S2: The seasonal trend decomposition algorithm is used to decompose the time series data sequence to obtain seasonal components and trend components.

[0076] S3: Determine the short-term and long-term characteristics of the operating data within the target time period based on the seasonal and trend components.

[0077] S4: Input the long-term and short-term characteristics into the preset trend analysis model to obtain the liquid usage trend within the target time period.

[0078] Seasonal trend decomposition algorithms are statistical methods used to analyze time-series data, decomposing it into seasonal, trend, and stochastic components. Commonly used algorithms include time series decomposition methods and methods based on Fourier transforms. The seasonal component refers to the periodically repeating part of a time-series data sequence. The trend component refers to the long-term trend of change in a time-series data sequence.

[0079] In this embodiment, liquid consumption data within the target time period is selected and organized into a time-series data sequence. Next, a seasonal trend decomposition algorithm is used to decompose the time-series data sequence into seasonal and trend components. Then, based on the seasonal and trend components, the short-term and long-term characteristics of the operational data within the target time period are determined. These short-term and long-term characteristics are then input into a pre-trained trend analysis model to obtain the liquid usage trend within the target time period.

[0080] Specifically, trend analysis models are used to predict and analyze trends in time-series data. It is understood that trend analysis models can employ mathematical models, such as linear regression models, or neural network models. When using a neural network model, short- and long-term features can be used as model inputs, liquid usage trends as model outputs, and a loss function can be constructed to train a pre-trained model. Once training is complete, the pre-trained model is determined as the trend analysis model. For specific details, please refer to the general training process of neural networks. This application does not impose specific limitations in this regard.

[0081] Understandably, employing a seasonal trend decomposition algorithm can separate seasonal variations and long-term trends in time-series data, allowing for the analysis of their respective impacts on liquid consumption. This clearly identifies the periodicity and long-term patterns of liquid consumption, optimizes chemical liquid usage and recycling processes, and provides strong support for management decisions.

[0082] In one embodiment, determining the short- and long-term characteristics of the operational data over a target time period based on seasonal and trend components includes:

[0083] S1: Extract short-term fluctuation characteristics from seasonal components.

[0084] S2: Extract long-term change characteristics from trend components.

[0085] S3: Combine short-term fluctuation characteristics and long-term change characteristics to obtain the short-term and long-term characteristics of the operating data in the target time period.

[0086] Short-term fluctuation characteristics refer to the rapid changes and fluctuations in liquid consumption over a relatively short period. Long-term fluctuation characteristics refer to the overall trend of liquid consumption over a longer period. Short-term fluctuation characteristics include at least one of the following: daily peak hours, daily average fluctuation amplitude, and fluctuation regularity indicators. Long-term fluctuation characteristics include at least one of the following: quarterly growth rate, trend direction, and the timing of inflection points.

[0087] In this embodiment, short-term fluctuation features are extracted from the seasonal component, while long-term change features are extracted from the trend component. The short-term and long-term fluctuation features are then combined to obtain the long-short-term features. Specifically, when extracting short-term fluctuation features, the high-frequency fluctuation components in the seasonal component can be analyzed. For example, mathematical tools such as Fourier transform or wavelet transform can be used to identify the high-frequency components in the seasonal component, which typically correspond to short-term fluctuation features. For the extraction of long-term change features, the trend component can be smoothed. For example, methods such as moving average or exponential smoothing can be used to remove short-term fluctuations in the trend component, thereby extracting the long-term trend.

[0088] In one embodiment, determining the total amount of data recovered and the recovery duration within a target time period, and determining the recovery rate based on the total amount of data recovered and the recovery duration, includes:

[0089] S1: Extract the recycled data within the target time period from the running data.

[0090] S2: Determine the total amount and duration of recycling based on the recycling data, and calculate the quotient of the total amount and duration of recycling to obtain the recycling speed.

[0091] The recovery data includes the recovery amount and duration for each recovery attempt within the target time period.

[0092] In this embodiment, the recycling data within the target time period is extracted from the running data. Then, by statistically analyzing the recycling amount and duration of each recycling start in the recycling data, the total recycling amount and duration within the target time period are determined. Finally, the quotient of the total recycling amount and duration within the target time period is calculated to obtain the recycling speed.

[0093] In one embodiment, a liquid management analysis report for a target time period is generated based on liquid usage trends, total recovery volume, recovery duration, and recovery speed, including:

[0094] S1: Determine the report template based on the length of the target time period.

[0095] S2: Fill the report template with liquid usage trends, total recovery volume, recovery duration, and recovery speed to obtain a liquid management analysis report for the target time period.

[0096] In this embodiment, report templates corresponding to target time periods of different lengths can be pre-set. Then, the corresponding report template is determined according to the length of the target time period, and the liquid usage trend, total recovery volume, recovery time, and recovery speed are filled into the report template to obtain a liquid management analysis report for the target time period. In addition, the liquid usage trend, total recovery volume, recovery time, and recovery speed can be visualized, and the visualization results can be added to the liquid management analysis report.

[0097] In one embodiment, the chemical liquid management and analysis method further includes:

[0098] The sampling amount is determined at each moment within the target time period based on the liquid usage trend. If there is a moment when the sampling amount exceeds the first preset threshold, an alarm is triggered. In addition, the pulse count change rate of the liquid recovery system during the liquid recovery process is monitored, and an alarm is triggered when the pulse count change rate exceeds the second preset threshold.

[0099] The first preset threshold and the second preset threshold can be set based on experience. In some embodiments, the second preset threshold can be set to 3%.

[0100] In this embodiment, during liquid usage, if the sample volume at a certain moment exceeds a preset first threshold, it indicates that the liquid usage is abnormally high, and the system will automatically trigger an alarm to allow operators to promptly check for leaks, misuse, or other problems in the equipment or process. During liquid recovery, the system monitors the pulse signals generated during the recovery process. By calculating the rate of change of the number of pulses per unit time (the pulse count change rate), the system can determine whether the recovery process is stable. If this rate of change exceeds a second preset threshold, it indicates abnormal fluctuations in the recovery flow rate, which may indicate blockage, pump failure, or sensor malfunction. In this case, an alarm will also be triggered to alert personnel for intervention.

[0101] In one embodiment, this application also provides a user sampling and recycling process, as detailed below (PLC refers to a programmable logic controller, i.e., the control unit in this application):

[0102] Sampling process: The user sets the sampling volume through the intelligent industrial control screen, the PLC starts the oil pump, the sampling ultrasonic level sensor monitors the oil volume in real time, and when the set value is reached, the PLC controls the oil pump to stop, and at the same time records the sampling data and uploads it to the intelligent management system.

[0103] Recycling process: After the experiment, the insulating oil is poured into the recycling container. The accumulated ultrasonic liquid level sensor monitors the liquid level change. The PLC starts the oil pump according to preset conditions (such as the recycling amount reaching the threshold) to pump the oil to the recycling tank. The total recycling amount is counted and updated to the system at the same time.

[0104] Abnormal Handling: If the sensor detects an abnormal oil level (such as excessive sampling or leakage during recovery), the PLC will immediately trigger an alarm on the intelligent industrial control screen and stop the oil pump operation, awaiting manual handling.

[0105] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0106] The chemical liquid management and analysis apparatus provided in the embodiments of this application is described below. The chemical liquid management and analysis apparatus described below can be referred to in correspondence with the chemical liquid management and analysis method described above.

[0107] like Figure 2 As shown, this application provides a chemical liquid management and analysis device 200, a control unit applied in a liquid recovery system, the device comprising:

[0108] The instruction receiving module 201 is used to determine the target time period according to the analysis instruction when it receives the analysis instruction, and to obtain the operating data of the liquid recovery system.

[0109] The feature analysis module 202 is used to analyze the long-term and short-term characteristics of the operating data within the target time period, and to determine the liquid usage trend based on the long-term and short-term characteristics;

[0110] The data calculation module 203 is used to determine the total amount of data recovered and the recovery duration within the target time period, so as to determine the recovery speed based on the total amount of data recovered and the recovery duration.

[0111] The report generation module 204 is used to generate a liquid management analysis report for a target time period based on liquid usage trends, total recovery volume, recovery duration, and recovery speed.

[0112] In the above embodiments, upon receiving an analysis command, the system determines the target time period and acquires operational data from the liquid recovery system, enabling precise positioning of the analysis scope and comprehensive data collection. Next, the system analyzes the long- and short-term characteristics of the operational data within the target time period and determines liquid usage trends, thus achieving accurate understanding and forward-looking prediction of liquid usage. Then, the system determines the total recovery volume and duration within the target time period and calculates the recovery rate, further refining management indicators. Finally, a liquid management analysis report for the target time period is generated. This report effectively integrates and analyzes data, optimizes the chemical liquid usage and recovery process, and provides strong support for management decisions.

[0113] In one embodiment, the instruction receiving module includes:

[0114] The type acquisition submodule is used to retrieve the time type in the analysis command;

[0115] The first acquisition submodule is used to acquire the target time period carried in the analysis instruction when the time type is time interval;

[0116] The second acquisition submodule is used to acquire the time value carried in the analysis command when the time type is time length, and determine the target time period based on the time value with the current time as the right endpoint.

[0117] In one embodiment, the feature analysis module includes:

[0118] The sequence acquisition submodule is used to obtain time-series data sequences consisting of liquid consumption within a target time period from the operational data;

[0119] The data decomposition submodule is used to decompose time-series data sequences using a seasonal trend decomposition algorithm to obtain seasonal components and trend components.

[0120] The feature determination submodule is used to determine the short- and long-term features of the running data in the target time period based on the seasonal and trend components.

[0121] The feature analysis submodule is used to input long-term and short-term features into a preset trend analysis model to obtain the liquid usage trend within the target time period.

[0122] In one embodiment, the feature determination submodule includes:

[0123] The first extraction unit is used to extract short-term fluctuation characteristics from the seasonal components. The short-term fluctuation characteristics include at least one of the following: daily peak period, daily average fluctuation amplitude, and fluctuation regularity indicators.

[0124] The second extraction unit is used to extract long-term change features from the trend components. The long-term change features include at least one of the following: quarterly growth rate, trend direction, and inflection point occurrence time.

[0125] The feature merging unit is used to merge short-term fluctuation features and long-term change features to obtain the short-term and long-term features of the running data within the target time period.

[0126] In one embodiment, the data computing module includes:

[0127] The data extraction submodule is used to extract the recycled data within the target time period from the running data;

[0128] The data calculation submodule is used to determine the total amount of recycling and the recycling time based on the recycling data, and to calculate the quotient of the total amount of recycling and the recycling time to obtain the recycling speed.

[0129] In one embodiment, the report generation module includes:

[0130] The template determination submodule is used to determine the report template based on the length of the target time period.

[0131] The data filling submodule is used to fill the report template with liquid usage trends, total recovery volume, recovery duration, and recovery speed to obtain a liquid management analysis report for the target time period.

[0132] In one embodiment, the chemical liquid management and analysis device further includes:

[0133] The alarm module is used to determine the sampling amount at each moment within the target time period based on the liquid usage trend. If there is a moment when the sampling amount exceeds the first preset threshold, an alarm is triggered. It also monitors the pulse count change rate of the liquid recovery system during the liquid recovery process and triggers an alarm when the pulse count change rate exceeds the second preset threshold.

[0134] The division of modules in the above-described chemical liquid management and analysis device is merely illustrative. In other embodiments, the chemical liquid management and analysis device can be divided into different modules as needed to complete all or part of its functions. Each module in the above-described chemical liquid management and analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0135] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the chemical liquid management and analysis method as described in any of the above embodiments.

[0136] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. Furthermore, in the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if there is transmission of electrical signals or data between the connected objects.

[0137] like Figure 3 As shown, in one embodiment, this application also provides a liquid recovery system, the system including a control unit, a control panel, a liquid level sensor, a weighing device, and a recovery tank;

[0138] The control panel is connected to the control unit and is used for visual input of various commands;

[0139] The liquid level sensor is installed on the top of the recovery tank to monitor the liquid level changes in the recovery tank in real time and upload the liquid level changes to the control unit.

[0140] The weighing device is used for quantity control during sampling;

[0141] The control unit is used to perform the steps of the chemical liquid management and analysis method as described in any of the above embodiments.

[0142] exist Figure 3In this context, PLC stands for Programmable Logic Controller, which is a control unit. The hazardous materials recycling tank is the recycling tank mentioned above.

[0143] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the liquid recovery system to which the present application is applied. A specific liquid recovery system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0144] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0145] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0146] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing and analyzing chemical liquids, characterized in that, A control unit applied in a liquid recovery system, the method comprising: When an analysis command is received, the target time period is determined according to the analysis command, and the operating data of the liquid recovery system is obtained; Analyze the long-term and short-term characteristics of the operational data within the target time period, and determine the liquid usage trend based on the long-term and short-term characteristics; The total amount and duration of the recovery data within the target time period are determined, and the recovery rate is determined based on the total amount and duration of the recovery. A liquid management analysis report for the target time period is generated based on the liquid usage trend, the total amount recovered, the recovery time, and the recovery speed.

2. The chemical liquid management and analysis method according to claim 1, characterized in that, Determining the target time period according to the analysis instructions includes: Obtain the time type from the analysis command; When the time type is a time interval, the target time period carried in the analysis instruction is obtained; When the time type is time length, the time value carried in the analysis instruction is obtained, and the target time period is determined based on the time value, with the current time as the right endpoint.

3. The chemical liquid management and analysis method according to claim 1, characterized in that, The analysis of the operational data's long-term and short-term characteristics within the target time period, and the determination of liquid usage trends based on these characteristics, includes: Obtain a time-series data sequence consisting of liquid consumption within the target time period from the operational data; The time series data sequence is decomposed using a seasonal trend decomposition algorithm to obtain seasonal components and trend components; The long-term and short-term characteristics of the operational data in the target time period are determined based on the seasonal component and the trend component. The long-term and short-term characteristics are input into a preset trend analysis model to obtain the liquid usage trend within the target time period.

4. The chemical liquid management and analysis method according to claim 3, characterized in that, Determining the short- and long-term characteristics of the operational data within the target time period based on the seasonal component and the trend component includes: Short-term fluctuation characteristics are extracted from the seasonal components, and the short-term fluctuation characteristics include at least one of the following: daily peak period, daily average fluctuation amplitude, and fluctuation regularity index; Long-term change features are extracted from the trend components, and the long-term change features include at least one of the following: quarterly growth rate, trend direction, and inflection point occurrence time; By combining the short-term fluctuation characteristics and the long-term change characteristics, the short-term and long-term characteristics of the operating data in the target time period are obtained.

5. The chemical liquid management and analysis method according to claim 1, characterized in that, Determining the total amount and duration of the operational data recovered within the target time period, and determining the recovery rate based on the total amount and duration of the data recovered, includes: Extract the recycling data within the target time period from the operational data; The total amount of data recovered and the recovery time are determined based on the recovery data, and the quotient of the total amount of data recovered and the recovery time is calculated to obtain the recovery speed.

6. The chemical liquid management and analysis method according to claim 1, characterized in that, The process of generating a liquid management analysis report for the target time period based on the liquid usage trend, the total recovery volume, the recovery duration, and the recovery speed includes: Determine the report template based on the length of the target time period; The liquid usage trend, total recovery volume, recovery duration, and recovery speed are filled into the report template to obtain a liquid management analysis report for the target time period.

7. The chemical liquid management and analysis method according to any one of claims 1 to 6, characterized in that, The method further includes: The sampling amount at each moment within the target time period is determined based on the liquid usage trend. If there is a moment when the sampling amount exceeds the first preset threshold, an alarm is triggered. In addition, the system monitors the pulse count change rate during the liquid recovery process and triggers an alarm when the pulse count change rate exceeds a second preset threshold.

8. A chemical liquid management and analysis device, characterized in that, A control unit applied in a liquid recovery system, the device comprising: The instruction receiving module is used to determine the target time period according to the analysis instruction when it receives the analysis instruction, and to obtain the operating data of the liquid recovery system. The feature analysis module is used to analyze the long-term and short-term characteristics of the operating data within the target time period, and to determine the liquid usage trend based on the long-term and short-term characteristics; The data calculation module is used to determine the total amount of data recovered and the recovery duration of the operating data within the target time period, so as to determine the recovery speed based on the total amount of data recovered and the recovery duration. The report generation module is used to generate a liquid management analysis report for the target time period based on the liquid usage trend, the total amount recovered, the recovery duration, and the recovery speed.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the chemical liquid management and analysis method as described in any one of claims 1 to 7.

10. A liquid recovery system, characterized in that, The system includes a control unit, a control panel, a liquid level sensor, a weighing device, and a recovery tank; The control panel is connected to the control unit and is used for visual input of various commands; The liquid level sensor is installed on the top of the recovery tank to monitor the liquid level changes in the recovery tank in real time and upload the liquid level changes to the control unit. The weighing device is used for quantity control during sampling; The control unit is used to perform the steps of the chemical liquid management and analysis method as described in any one of claims 1 to 7.

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