Automatic state detection method and system applied to intelligent core barrel

Abstract

The invention discloses an automatic state detection method and system applied to an intelligent core barrel, and relates to the technical field of big data analys.The method comprises the steps that in-barrel pressure data and timestamps of historical transportation of a target intelligent core barrel are extracted to construct an analysis set, high-frequency pressure values are screened in combination with current transportation data, and a detection threshold value is obtained through weighted fusion; invalid pressure fluctuation caused by transportation dynamic interference is effectively filtered out, and an accurate data reference is provided for subsequent analysis; a neural network model is used for extracting time sequence features to generate a pressure change trend, batch analysis is carried out on the same-batch and same-attribute intelligent core barrels, accurate prediction and batch unified detection of the pressure trend are realized, and the detection efficiency and consistency are improved; and meanwhile, judgment and early warning are performed in combination with the historical longest transportation duration and the maximum cut-off pressure value predicted by the neural network model, so that the problems of disturbance of pressure data, inconsistent standards and lag in traditional detection are solved, and the real-time performance and reliability of intelligent core barrel state detection in the transportation process are ensured.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis technology, and specifically to an automated status detection method and system applied to a smart core bucket. Background Art

[0002] Thanks to the pressure sensing, vibration sensing, positioning and data recording functions of the built-in chip, the Smart Core Barrel can realize the visual display of the liquid remaining in the barrel and automatically warn the supplier when it is insufficient. It can accurately track the transportation trajectory and violent collision status and issue timely alarms. At the same time, it supports recycling and reuse and historical loading record tracing. Combined with ton barrels, it can realize simple packaging. It significantly improves the safety of liquid transportation, supply chain response efficiency and resource recycling rate, and solves the inefficiency and lag problems of traditional containers in status monitoring, information tracing, supply and demand coordination, etc., providing key support for the intelligent management of liquid materials. It has important practical value and industry innovation significance.

[0003] During the service life of the Smart Core Barrel, its transportation process often faces complex dynamic interference, such as road bumps, sudden stops and starts of transportation vehicles, centrifugal steering and other factors, which will cause the liquid loaded in the barrel to shake violently. During the shaking process of the liquid, its kinetic energy will be transferred to the space above the liquid surface, resulting in uneven and non-steady-state pressure fluctuations in this area. This fluctuation is not caused by the actual state change of the liquid in the barrel, but by the dynamic disturbance during transportation. Such non-steady-state pressure fluctuations will directly interfere with the pressure detection accuracy of the built-in chip of the Smart Core Barrel, causing the pressure data collected by the chip to contain a large amount of invalid interference information, which in turn affects the analysis and judgment of the actual state of the liquid in the barrel, and restricts the reliability of the core functions of the Smart Core Barrel such as pressure sensing and state monitoring in transportation scenarios. Therefore, there is an urgent need for an automated state detection method and system for the Smart Core Barrel. Summary of the Invention

[0004] The purpose of the present invention is to provide an automated status detection method and system for a smart core bucket to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an automated status detection method applied to a smart core bucket, the automated status detection method comprising the following steps: Step S1: Select any smart core bucket as the research object, denoted as the target smart core bucket, obtain the target smart core bucket's internal pressure data generated during multiple historical transportation processes and the corresponding timestamp data of the internal pressure data collection, and construct a transport pressure analysis set for the target smart core bucket in the transportation state; Step S1-1: Extract the historical transportation records of the target smart core bucket, determine the start and end timestamps of each historical transportation process, calculate the time interval between the start and end timestamps, and obtain the transportation duration of the target smart core bucket; Step S1-2: For each historical transport process, extract the internal pressure data and the corresponding timestamp data from the target smart core barrel's storage unit based on the start and end timestamps, archive and store them, and construct a transport barrel pressure analysis set. The internal pressure data represents the pressure value of the gas space above the liquid level in the smart core barrel. By selecting the target smart core bucket, extracting the start and end timestamps of its multiple historical transportation processes to determine the transportation duration, and extracting the corresponding pressure data in the bucket and the timestamp data from the storage unit based on the start and end timestamps to archive and construct a pressure analysis set in the transportation bucket, it can provide targeted and complete historical data support for subsequent pressure time series analysis, threshold calculation, etc., ensuring that the data accurately corresponds to the transportation process.

[0006] Step S2: Obtain in real time the internal pressure data of the target smart core barrel during the current transport process and the timestamp data corresponding to the internal pressure data acquisition, and construct a pressure time series curve graph of the target smart core barrel based on the timestamp data and the internal pressure data; and traverse and read the internal pressure data and timestamp data of the transport barrel internal pressure analysis set to construct a reference pressure time series curve graph; Step S2-1: During the current transportation process of the target smart core barrel, real-time pressure data of the barrel is collected via a built-in smart chip, wherein the smart chip has a built-in pressure sensor; the collection timestamp data corresponding to the pressure data of each barrel is synchronously recorded, the real-time collected barrel pressure data and the corresponding timestamp data are associated in the collection order, the timestamp data is selected as the horizontal axis to establish the x-axis, and the barrel pressure data is selected as the vertical axis to establish the y-axis, and a pressure time series curve graph of the target smart core barrel is constructed based on the x-axis and the y-axis; Step S2-2: Traverse the barrel pressure data of each historical transport process stored in the transport barrel pressure analysis set and the collection timestamp data corresponding to the barrel pressure data, continuously arrange the extracted barrel pressure data in chronological order according to the collection timestamp data, select the arranged transport timestamp data as the horizontal axis to establish the x-axis, select the arranged barrel pressure data as the vertical axis to establish the y-axis, and construct a reference pressure time series curve graph for the target smart core barrel based on the x-axis and y-axis; Through the intelligent chip, the pressure data and corresponding timestamp data of the target smart core barrel currently being transported are collected in real time and a pressure time series curve is constructed. At the same time, the historical data of the pressure analysis set in the transport barrel are traversed and arranged in time to construct a reference pressure time series curve. This can provide an intuitive chart of the change of barrel pressure over time during the current and historical transportation processes, laying a visual data foundation for the subsequent extraction of pressure detection thresholds.

[0007] Step S3: Analyze and filter the pressure timing curve of the smart core bucket to obtain a first pressure detection threshold; analyze and filter the reference pressure timing curve to obtain a second pressure detection threshold; and combine the first and second pressure detection thresholds using a weighted fusion calculation to obtain the pressure detection threshold of the target smart core bucket. Step S3-1: Calculate the frequency of occurrence of each pressure data value in the target smart core bucket according to the pressure data values ​​in the pressure time series curve of the target smart core bucket, and select the pressure data value with the highest frequency as the first pressure detection threshold of the target smart core bucket; Step S3-2: Calculate the frequency of occurrence of each pressure data value in the target smart core bucket based on the pressure data values ​​in the reference pressure time series curve of the target smart core bucket, and select the pressure data value with the highest frequency as the second pressure detection threshold of the target smart core bucket. Step S3-3: Set the weight coefficient of the first pressure detection threshold to α and the weight coefficient of the second pressure detection threshold to β, where the sum of α and β is 1; multiply the first pressure detection threshold by α to obtain a first weighted value; multiply the second pressure detection threshold by β to obtain a second weighted value; calculate the sum of the first weighted value and the second weighted value to obtain the pressure detection threshold of the target smart core bucket; The calculation formula for the pressure detection threshold of the target intelligent core bucket is as follows: ; Where, P max It represents the pressure detection threshold of the target smart core bucket; P1 represents the first pressure detection threshold; P2 represents the second pressure detection threshold; First, calculate the average of all the pressure data values ​​in the target smart core bucket pressure time series curve, record it as the current pressure average value, and calculate the absolute difference between the current pressure average value and the first pressure detection threshold, record it as the first difference value; At the same time, the average value of all the pressure data values ​​in the barrel in the reference pressure time series curve is calculated, recorded as the historical pressure average value, and the absolute difference between the historical pressure average value and the second pressure detection threshold is calculated, recorded as the second difference value; The first difference and the second difference are added to obtain the total difference. Finally, the first difference is divided by the total difference to obtain the weight coefficient α of the first pressure detection threshold. The second difference is divided by the total difference to obtain the weight coefficient β of the second pressure detection threshold, and α+β=1 is satisfied.

[0008] By calculating the frequency of occurrence of pressure data values ​​in each bucket from the pressure timing curve of the target smart core bucket and the reference pressure timing curve, and selecting the highest frequency value as the first pressure detection threshold and the second pressure detection threshold, and then obtaining the pressure detection threshold through weighted fusion, a clear judgment benchmark is provided for subsequent monitoring and screening of pressure data in the bucket.

[0009] Step S4: Monitor the pressure data in the target smart core bucket's pressure time series curve according to the pressure detection threshold, select the target smart core bucket's pressure data that meets the monitoring conditions, and the timestamp data corresponding to the pressure data, and construct a prediction analysis set of the target smart core bucket's pressure data, which is recorded as the barrel pressure prediction set. Step S4-1: Traverse all the internal pressure data and the timestamp data corresponding to the internal pressure data contained in the pressure time series curve of the target smart core bucket, determine the magnitude relationship between each internal pressure data value and the pressure detection threshold in turn, and select the internal pressure data of the bucket whose internal pressure value does not exceed the pressure detection threshold based on the magnitude relationship; Step S4-2: For the bucket pressure data whose value is greater than the pressure detection threshold, replace the bucket pressure data with the corresponding timestamp using the pressure detection threshold to obtain the target smart core bucket's replacement bucket pressure data; archive and store the selected bucket pressure data and the replacement bucket pressure data to construct a prediction analysis set of the target smart core bucket's bucket pressure data, which is recorded as the bucket pressure prediction set; By monitoring the pressure data in the target smart core bucket in the pressure time series curve diagram according to the pressure detection threshold, filtering the data that does not exceed the threshold, replacing the data that exceeds the threshold with the pressure detection threshold to obtain the replacement data, and then archiving and building the pressure prediction set in the bucket, the subsequent neural network model processing is provided with standardized pressure data and corresponding timestamp data, ensuring the rationality and availability of the prediction analysis data.

[0010] Step S5: Process the target smart core barrel's internal pressure prediction set using a neural network model to obtain a pressure data change trend of the target smart core barrel during the current transportation process. The barrel pressure data and corresponding timestamp data in the target smart core barrel's barrel pressure prediction set, arranged in chronological order by timestamp data, are input into a neural network model; the neural network model comprises an input layer, a hidden layer, and an output layer, wherein the input layer is used to convert the format of the barrel pressure data and the corresponding timestamp data; the hidden layer is used to perform feature extraction and time series association operations on the converted data; the output layer is used to output the barrel pressure prediction values ​​corresponding to a preset number of subsequent time nodes during the current transportation process; based on the sequence of barrel pressure prediction values ​​at subsequent time nodes in chronological order, the barrel pressure data change trend of the target smart core barrel during the current transportation process is predicted; The neural network model used in step S5 is an LSTM, which can specifically process the set of barrel pressure prediction data arranged in chronological order by timestamp. It uses the time series correlation operation of the hidden layer to capture the long-term and short-term dependencies of the pressure data over time, accurately extract the time series features, and then output the barrel pressure prediction value at the subsequent preset time node; By inputting the barrel pressure data and corresponding timestamp data arranged in chronological order by timestamp in the barrel pressure prediction set into the neural network model, after input layer format conversion, hidden layer feature extraction and time series association operation, and output layer output of subsequent time node prediction values, the barrel pressure data change trend of the target smart core barrel currently being transported is obtained, providing a trend basis for pressure changes for subsequent automated status detection.

[0011] Step S6: Synchronously use steps S1 to S5 to process the smart core barrels with the same attributes in the current transportation process, obtain the pressure data change trend of each smart core barrel in the same batch transportation process, and perform automatic status detection on the smart core barrels with the same attributes.

[0012] Step S6-1: Select smart core barrels with the same attributes as the target smart core barrel during the current transportation process to form a set of smart core barrels from the same batch. Smart core barrels with the same attributes are those that carry the same liquid type and specifications and have the same barrel model. For each smart core barrel in the set of smart core barrels from the same batch, execute steps S1 to S5 to obtain a pressure change trend for each smart core barrel during the current transportation process. Step S6-2: Analyze and extract the historical longest transport time of the target smart core barrel obtained in step S1-1; analyze and calculate the transport time during the current transport process to obtain the maximum remaining transport time of the target smart core barrel; input the maximum remaining transport time into the neural network model of step S5 to obtain a predicted value of the barrel pressure at the end of the maximum remaining transport time, which is recorded as the maximum pressure value at the end; Step S6-3: Detect the pressure threshold preset in the smart core bucket according to the pressure threshold. If the maximum pressure value exceeds the pressure threshold, mark the smart core bucket as abnormal and issue a warning signal. If the maximum pressure value does not exceed the pressure threshold, mark the smart core bucket as normal, thus completing the automated status detection of the smart core bucket. By selecting smart core barrels with the same attributes to form a batch set, steps S1 to S5 are executed for each smart core barrel to obtain the pressure change trend. The maximum remaining transportation time, which is calculated by combining the longest historical transportation time with the current transportation time, is input into the neural network model to obtain the maximum cutoff pressure value. The mark status is then compared with the preset pressure threshold in the barrel, realizing batch automated status detection of smart core barrels in the same batch, ensuring the consistency and pertinence of status judgment, and being able to identify abnormal status in a timely manner and issue early warnings.

[0013] Furthermore, an automated status detection system applied to the smart core barrel includes a historical data acquisition module, a time series diagram construction module, a detection threshold calculation module, a prediction data screening module, a pressure trend prediction module, and a batch status detection module; The historical data acquisition module is used to obtain the pressure data and corresponding timestamp data of the target smart core barrel during historical transportation and to construct a pressure analysis set for the transported barrel; the time series graph construction module is used to construct a pressure time series curve graph of the current and historical transportation of the target smart core barrel; the detection threshold calculation module is used to calculate the pressure detection threshold of the target smart core barrel; the prediction data screening module is used to construct a prediction set of the pressure in the barrel of the target smart core barrel; the pressure trend prediction module is used to obtain the change trend of the pressure data in the barrel of the target smart core barrel during current transportation; the batch status detection module is used to perform automatic status detection on the smart core barrels of the same batch; The output end of the historical data acquisition module is electrically connected to the input end of the timing diagram construction module; the output end of the timing diagram construction module is electrically connected to the input end of the detection threshold calculation module; the output end of the detection threshold calculation module is electrically connected to the input end of the prediction data screening module; the output end of the prediction data screening module is electrically connected to the input end of the pressure trend prediction module; the output end of the pressure trend prediction module is electrically connected to the input end of the batch status detection module; The historical data acquisition module includes a historical transport record extraction unit and a historical pressure data archiving unit; the historical transport record extraction unit is used to extract the historical transport records of the target smart core bucket and determine the start and end timestamps and transport duration of each transport; the historical pressure data archiving unit is used to extract the pressure data in the bucket and the corresponding timestamp data based on the start and end timestamps of the historical transport and archive and store them; The timing diagram construction module includes a current pressure timing diagram construction unit and a reference pressure timing diagram construction unit; the current pressure timing diagram construction unit is used to construct a pressure timing curve diagram based on the pressure data in the barrel of the current transport and the corresponding timestamp data; the reference pressure timing diagram construction unit is used to construct a reference pressure timing curve diagram based on the pressure data in the barrel of the historical transport and the corresponding timestamp data; The detection threshold calculation module includes a basic threshold extraction unit and a threshold weighted fusion unit; the basic threshold extraction unit is used to extract the pressure value with the highest frequency from the current and reference pressure time series curves; the threshold weighted fusion unit is used to obtain the pressure detection threshold of the target smart core bucket through weighted fusion calculation; The prediction data screening module includes a compliance pressure screening unit and an over-threshold data processing unit; the compliance pressure screening unit is used to screen out the barrel pressure data and corresponding timestamp data that do not exceed the pressure detection threshold; the over-threshold data processing unit is used to replace the barrel pressure data that exceeds the threshold with the pressure detection threshold and archive it together with the compliance data; The pressure trend prediction module includes a neural network data processing unit and a pressure trend generation unit; the neural network data processing unit is used to input the data in the barrel pressure prediction set into the neural network model for format conversion, feature extraction and time series association operation; the pressure trend generation unit is used to generate the barrel pressure data change trend based on the subsequent time node pressure prediction value output by the neural network model; The batch status detection module includes a cut-off pressure prediction unit and a status threshold judgment unit; the cut-off pressure prediction unit is used to predict the pressure value in the barrel at the end of transportation based on the longest historical transportation time and the current transportation time; the status threshold judgment unit is used to compare the cut-off pressure prediction value with the preset barrel pressure threshold and mark the smart core barrel status.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention constructs an analysis set by extracting the pressure data and timestamps of the target smart core barrel during historical transportation, generates a pressure time series curve chart and a reference curve chart based on the current transportation data, filters high-frequency pressure values ​​and performs weighted fusion to obtain a detection threshold, effectively filtering out invalid pressure fluctuations caused by dynamic transportation interference, providing an accurate data benchmark for subsequent analysis, and solving the problem of pressure data being affected by disturbances in traditional detection.

[0015] 2. The present invention constructs a prediction set by screening compliant pressure data based on the detection threshold and replacing the over-threshold data, and uses a neural network model to extract time series features to generate pressure change trends. The above process is executed in batches for the same batch of smart core buckets with the same attributes, thereby achieving accurate prediction of pressure trends and unified batch detection, improving detection efficiency and consistency, and overcoming the defects of inconsistent traditional batch detection standards.

[0016] 3. The present invention combines the longest historical transportation time with the current transportation time to obtain the maximum remaining transportation time, uses a neural network model to predict the maximum pressure value, compares it with the preset threshold to complete status judgment and issue an early warning, forming a complete process from data collection and processing to status early warning, ensuring the real-time and reliability of the smart core barrel status detection during transportation, and solving the problem of traditional detection lag. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of an automated status detection method for a smart core bucket according to the present invention; Figure 2 This is a structural diagram of an automatic status detection system applied to a smart core bucket according to the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 As shown, the present invention provides a technical solution, an automatic status detection method applied to a smart core bucket, and the automatic status detection method includes the following steps: Step S1: Select any smart core bucket as the research object, denoted as the target smart core bucket, obtain the target smart core bucket's internal pressure data generated during multiple historical transportation processes and the corresponding timestamp data of the internal pressure data collection, and construct a transport pressure analysis set for the target smart core bucket in the transportation state; Step S1-1: Extract the historical transportation records of the target smart core bucket, determine the start and end timestamps of each historical transportation process, calculate the time interval between the start and end timestamps, and obtain the transportation duration of the target smart core bucket; Step S1-2: For each historical transport process, extract the internal pressure data and the corresponding timestamp data from the target smart core barrel's storage unit based on the start and end timestamps, archive and store them, and construct a transport barrel pressure analysis set. The internal pressure data represents the pressure value of the gas space above the liquid level in the smart core barrel. During specific implementation, after selecting the target smart core bucket, it is necessary to completely extract historical transportation records from its storage unit, accurately define each transportation cycle through the start and end timestamps, and ensure that the extracted pressure data in the bucket and the corresponding timestamps strictly fall within each transportation cycle. This ensures that the pressure analysis set in the transportation bucket can truly reflect the pressure characteristics of different transportation processes, providing a reliable historical benchmark for subsequent analysis.

[0020] Step S2: Obtain in real time the internal pressure data of the target smart core barrel during the current transport process and the timestamp data corresponding to the internal pressure data acquisition, and construct a pressure time series curve graph of the target smart core barrel based on the timestamp data and the internal pressure data; and traverse and read the internal pressure data and timestamp data of the transport barrel internal pressure analysis set to construct a reference pressure time series curve graph; Step S2-1: During the current transportation process of the target smart core barrel, real-time pressure data of the barrel is collected via a built-in smart chip, wherein the smart chip has a built-in pressure sensor; the collection timestamp data corresponding to the pressure data of each barrel is synchronously recorded, the real-time collected barrel pressure data and the corresponding timestamp data are associated in the collection order, the timestamp data is selected as the horizontal axis to establish the x-axis, and the barrel pressure data is selected as the vertical axis to establish the y-axis, and a pressure time series curve graph of the target smart core barrel is constructed based on the x-axis and the y-axis; Step S2-2: Traverse the barrel pressure data of each historical transport process stored in the transport barrel pressure analysis set and the collection timestamp data corresponding to the barrel pressure data, continuously arrange the extracted barrel pressure data in chronological order according to the collection timestamp data, select the arranged transport timestamp data as the horizontal axis to establish the x-axis, select the arranged barrel pressure data as the vertical axis to establish the y-axis, and construct a reference pressure time series curve graph for the target smart core barrel based on the x-axis and y-axis; In specific implementation, the construction of the current pressure time series curve chart needs to rely on the real-time acquisition function of the smart chip to ensure the synchronization of pressure data and timestamps, and the acquisition frequency should be adapted to the dynamic changes of the transportation scenario; the reference pressure time series curve chart needs to arrange the historical transportation data in chronological order, eliminate the interference of time intervals of different transportation cycles, and keep the time axis scales of the two curves consistent, so as to facilitate intuitive comparison of pressure change patterns.

[0021] Step S3: Analyze and filter the pressure timing curve of the smart core bucket to obtain a first pressure detection threshold; analyze and filter the reference pressure timing curve to obtain a second pressure detection threshold; and combine the first and second pressure detection thresholds using a weighted fusion calculation to obtain the pressure detection threshold of the target smart core bucket. Step S3-1: Calculate the frequency of occurrence of each pressure data value in the target smart core bucket according to the pressure data values ​​in the pressure time series curve of the target smart core bucket, and select the pressure data value with the highest frequency as the first pressure detection threshold of the target smart core bucket; Step S3-2: Calculate the frequency of occurrence of each pressure data value in the target smart core bucket based on the pressure data values ​​in the reference pressure time series curve of the target smart core bucket, and select the pressure data value with the highest frequency as the second pressure detection threshold of the target smart core bucket. Step S3-3: Set the weight coefficient of the first pressure detection threshold to α and the weight coefficient of the second pressure detection threshold to β, where the sum of α and β is 1; multiply the first pressure detection threshold by α to obtain a first weighted value; multiply the second pressure detection threshold by β to obtain a second weighted value; calculate the sum of the first weighted value and the second weighted value to obtain the pressure detection threshold of the target smart core bucket; In specific implementation, the extraction of the first and second pressure detection thresholds must be based on a sufficient amount of pressure data. Frequency statistics should cover the entire cycle of the pressure time series curve to avoid high-frequency values ​​deviating from the actual stable pressure due to insufficient data. When weighted fusion is used, α can focus on the real-time characteristics of the current transportation, while β can focus on the universality of historical data. Through a reasonable ratio of the two, the final pressure detection threshold can reflect both the current status and historical experience. Step S4: Monitor the pressure data in the target smart core bucket's pressure time series curve according to the pressure detection threshold, select the target smart core bucket's pressure data that meets the monitoring conditions, and the timestamp data corresponding to the pressure data, and construct a prediction analysis set of the target smart core bucket's pressure data, which is recorded as the barrel pressure prediction set. Step S4-1: Traverse all the internal pressure data and the timestamp data corresponding to the internal pressure data contained in the pressure time series curve of the target smart core bucket, determine the magnitude relationship between each internal pressure data value and the pressure detection threshold in turn, and select the internal pressure data of the bucket whose internal pressure value does not exceed the pressure detection threshold based on the magnitude relationship; Step S4-2: For the bucket pressure data whose value is greater than the pressure detection threshold, replace the bucket pressure data with the corresponding timestamp using the pressure detection threshold to obtain the target smart core bucket's replacement bucket pressure data; archive and store the selected bucket pressure data and the replacement bucket pressure data to construct a prediction analysis set of the target smart core bucket's bucket pressure data, which is recorded as the bucket pressure prediction set; In practice, screening for compliant pressure data requires evaluating each data point in the pressure time series curve one by one to ensure that no pressure value at any time point is missed. When processing over-threshold data, the original data at the corresponding timestamp must be strictly replaced with the pressure detection threshold, without changing the continuity of the time series. This ensures that the constructed in-bucket pressure forecast set can filter out abnormal interference while retaining true time series correlations, providing high-quality data for subsequent forecasts. Step S5: Process the target smart core barrel's internal pressure prediction set using a neural network model to obtain a pressure data change trend of the target smart core barrel during the current transportation process. The barrel pressure data and corresponding timestamp data in the target smart core barrel's barrel pressure prediction set, arranged in chronological order by timestamp data, are input into a neural network model; the neural network model comprises an input layer, a hidden layer, and an output layer, wherein the input layer is used to convert the format of the barrel pressure data and the corresponding timestamp data; the hidden layer is used to perform feature extraction and time series association operations on the converted data; the output layer is used to output the barrel pressure prediction values ​​corresponding to a preset number of subsequent time nodes during the current transportation process; based on the sequence of barrel pressure prediction values ​​at subsequent time nodes in chronological order, the barrel pressure data change trend of the target smart core barrel during the current transportation process is predicted; During implementation, the inputs of the neural network model must be strictly arranged in timestamp order, and the format conversion of the input layer must adapt to the model's requirements for data dimensions. Feature extraction in the hidden layer must focus on capturing the trend characteristics and temporal correlation patterns of pressure changes over time. The number of preset time nodes in the output layer should be set based on the accuracy requirements of transportation monitoring, so that the generated pressure change trend can accurately reflect the subsequent pressure development situation. Step S6: Synchronously use steps S1 to S5 to process the smart core barrels with the same attributes in the current transportation process, obtain the pressure data change trend of each smart core barrel in the same batch transportation process, and perform automatic status detection on the smart core barrels with the same attributes.

[0022] Step S6-1: Select smart core barrels with the same attributes as the target smart core barrel during the current transportation process to form a set of smart core barrels from the same batch. Smart core barrels with the same attributes are those that carry the same liquid type and specifications and have the same barrel model. For each smart core barrel in the set of smart core barrels from the same batch, execute steps S1 to S5 to obtain a pressure change trend for each smart core barrel during the current transportation process. Step S6-2: Analyze and extract the historical longest transport time of the target smart core barrel obtained in step S1-1; analyze and calculate the transport time during the current transport process to obtain the maximum remaining transport time of the target smart core barrel; input the maximum remaining transport time into the neural network model of step S5 to obtain a predicted value of the barrel pressure at the end of the maximum remaining transport time, which is recorded as the maximum pressure value at the end; Step S6-3: Detect the pressure threshold preset in the smart core bucket according to the pressure threshold. If the maximum pressure value exceeds the pressure threshold, mark the smart core bucket as abnormal and issue a warning signal. If the maximum pressure value does not exceed the pressure threshold, mark the smart core bucket as normal, thus completing the automated status detection of the smart core bucket. During specific implementation, the selection of smart core barrels from the same batch must strictly adhere to the consistency standards for the type of loaded liquid, specifications, and barrel model to ensure the comparability of individuals within the batch. When calculating the maximum remaining transportation time, the time units of the longest historical transportation time and the current transportation time must be accurately matched. The neural network model's prediction of the maximum cutoff pressure value must be based on the pressure change trend of each smart core barrel itself. A unified preset barrel pressure standard must be used for threshold comparison to ensure the fairness and accuracy of status detection.

[0023] Example 2, as Figure 2 As shown, the present invention provides an automated status detection system for smart core barrels, which includes a historical data acquisition module, a time sequence diagram construction module, a detection threshold calculation module, a prediction data screening module, a pressure trend prediction module and a batch status detection module; The historical data acquisition module is used to obtain the pressure data and corresponding timestamp data of the target smart core barrel during historical transportation and to construct a pressure analysis set for the transported barrel; the time series graph construction module is used to construct a pressure time series curve graph of the current and historical transportation of the target smart core barrel; the detection threshold calculation module is used to calculate the pressure detection threshold of the target smart core barrel; the prediction data screening module is used to construct a prediction set of the pressure in the barrel of the target smart core barrel; the pressure trend prediction module is used to obtain the change trend of the pressure data in the barrel of the target smart core barrel during current transportation; the batch status detection module is used to perform automatic status detection on the smart core barrels of the same batch; The output end of the historical data acquisition module is electrically connected to the input end of the timing diagram construction module; the output end of the timing diagram construction module is electrically connected to the input end of the detection threshold calculation module; the output end of the detection threshold calculation module is electrically connected to the input end of the prediction data screening module; the output end of the prediction data screening module is electrically connected to the input end of the pressure trend prediction module; the output end of the pressure trend prediction module is electrically connected to the input end of the batch status detection module; The historical data acquisition module includes a historical transport record extraction unit and a historical pressure data archiving unit; the historical transport record extraction unit is used to extract the historical transport records of the target smart core bucket and determine the start and end timestamps and transport duration of each transport; the historical pressure data archiving unit is used to extract the pressure data in the bucket and the corresponding timestamp data based on the start and end timestamps of the historical transport and archive and store them; The timing diagram construction module includes a current pressure timing diagram construction unit and a reference pressure timing diagram construction unit; the current pressure timing diagram construction unit is used to construct a pressure timing curve diagram based on the pressure data in the barrel of the current transport and the corresponding timestamp data; the reference pressure timing diagram construction unit is used to construct a reference pressure timing curve diagram based on the pressure data in the barrel of the historical transport and the corresponding timestamp data; The detection threshold calculation module includes a basic threshold extraction unit and a threshold weighted fusion unit; the basic threshold extraction unit is used to extract the pressure value with the highest frequency from the current and reference pressure time series curves; the threshold weighted fusion unit is used to obtain the pressure detection threshold of the target smart core bucket through weighted fusion calculation; The prediction data screening module includes a compliance pressure screening unit and an over-threshold data processing unit; the compliance pressure screening unit is used to screen out the barrel pressure data and corresponding timestamp data that do not exceed the pressure detection threshold; the over-threshold data processing unit is used to replace the barrel pressure data that exceeds the threshold with the pressure detection threshold and archive it together with the compliance data; The pressure trend prediction module includes a neural network data processing unit and a pressure trend generation unit; the neural network data processing unit is used to input the data in the barrel pressure prediction set into the neural network model for format conversion, feature extraction and time series association operation; the pressure trend generation unit is used to generate the barrel pressure data change trend based on the subsequent time node pressure prediction value output by the neural network model; The batch status detection module includes a cut-off pressure prediction unit and a status threshold judgment unit; the cut-off pressure prediction unit is used to predict the pressure value in the barrel at the end of transportation based on the longest historical transportation time and the current transportation time; the status threshold judgment unit is used to compare the cut-off pressure prediction value with the preset barrel pressure threshold and mark the smart core barrel status.

[0024] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

Claims

1. An automated status detection method for a smart core bucket, characterized by: The automated state detection method comprises the following steps: Step S1: Select any smart core bucket as the research object, denoted as the target smart core bucket, obtain the target smart core bucket's internal pressure data generated during multiple historical transportation processes and the corresponding timestamp data of the internal pressure data collection, and construct a transport pressure analysis set for the target smart core bucket in the transportation state; Step S2: Obtain in real time the internal pressure data of the target smart core barrel during the current transport process and the timestamp data corresponding to the internal pressure data acquisition, and construct a pressure time series curve graph of the target smart core barrel based on the timestamp data and the internal pressure data; and traverse and read the internal pressure data and timestamp data of the transport barrel internal pressure analysis set to construct a reference pressure time series curve graph; Step S3: Analyze the pressure data values ​​in the target smart core bucket in the pressure time series curve and the reference pressure time series curve, calculate the frequency of occurrence of the pressure data values ​​in each bucket, select the pressure data value in the bucket with the highest frequency in the pressure time series curve as the first pressure detection threshold of the target smart core bucket, select the pressure data value in the bucket with the highest frequency in the reference pressure time series curve as the second pressure detection threshold of the target smart core bucket, and use weighted fusion calculation to combine the first and second pressure detection thresholds to obtain the pressure detection threshold of the target smart core bucket. Step S4: Monitor the pressure data in the target smart core bucket's pressure time series curve according to the pressure detection threshold, select the target smart core bucket's pressure data that meets the monitoring conditions, and the timestamp data corresponding to the pressure data, and construct a prediction analysis set of the target smart core bucket's pressure data, which is recorded as the barrel pressure prediction set. Step S5: Process the target smart core barrel's internal pressure prediction set using a neural network model to obtain a pressure data change trend of the target smart core barrel during the current transportation process. Step S6: Synchronously use steps S1 to S5 to process the smart core barrels with the same attributes in the current transportation process, obtain the pressure data change trend of each smart core barrel in the same batch transportation process, and perform automatic status detection on the smart core barrels with the same attributes.

2. The automated status detection method for a smart core bucket according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1: Extract the historical transportation records of the target smart core bucket, determine the start and end timestamps of each historical transportation process, calculate the time interval between the start and end timestamps, and obtain the transportation duration of the target smart core bucket; Step S1-2: For each historical transportation process, extract the barrel pressure data and the timestamp data corresponding to the barrel pressure data collection from the storage unit of the target smart core barrel based on the start timestamp and the end timestamp, archive and store them to build a transport barrel pressure analysis set, where the barrel pressure data is represented as the pressure value of the gas space above the liquid surface of the liquid loaded inside the smart core barrel.

3. The method for automatic status detection applied to a smart core bucket according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1: During the current transportation process of the target smart core barrel, real-time pressure data of the barrel is collected via a built-in smart chip, wherein the smart chip has a built-in pressure sensor; the collection timestamp data corresponding to the pressure data of each barrel is synchronously recorded, the real-time collected barrel pressure data and the corresponding timestamp data are associated in the collection order, the timestamp data is selected as the horizontal axis to establish the x-axis, and the barrel pressure data is selected as the vertical axis to establish the y-axis, and a pressure time series curve graph of the target smart core barrel is constructed based on the x-axis and the y-axis; Step S2-2, traverse the barrel pressure data of each historical transportation process stored in the transport barrel pressure analysis set and the collection timestamp data corresponding to the barrel pressure data, arrange the extracted barrel pressure data continuously in order according to the collection timestamp data, select the arranged transport timestamp data as the horizontal axis to establish the x-axis, select the arranged barrel pressure data as the vertical axis to establish the y-axis, and construct a reference pressure time series curve graph of the target smart core barrel based on the x-axis and y-axis.

4. The automated status detection method for a smart core bucket according to claim 3, characterized in that: In step S3, the weight coefficient of the first pressure detection threshold is set to α, and the weight coefficient of the second pressure detection threshold is set to β, where the sum of α and β is 1; the first pressure detection threshold is multiplied by α to obtain a first weighted value; the second pressure detection threshold is multiplied by β to obtain a second weighted value; the sum of the first weighted value and the second weighted value is calculated to obtain the pressure detection threshold of the target smart core bucket.

5. The method for automatic status detection applied to a smart core bucket according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1: Traverse all the internal pressure data and the timestamp data corresponding to the internal pressure data contained in the pressure time series curve of the target smart core bucket, determine the magnitude relationship between each internal pressure data value and the pressure detection threshold in turn, and select the internal pressure data of the bucket whose internal pressure value does not exceed the pressure detection threshold based on the magnitude relationship; Step S4-2: For the barrel pressure data whose value is greater than the pressure detection threshold, the barrel pressure data of the corresponding timestamp is replaced by the pressure detection threshold to obtain the substitute barrel pressure data of the target smart core barrel; the selected barrel pressure data and the substitute barrel pressure data are archived and stored to construct a barrel pressure data prediction and analysis set for the target smart core barrel, which is recorded as the barrel pressure prediction set.

6. The method for automatic status detection applied to a smart core bucket according to claim 5, characterized in that: In step S5, the bucket pressure data and the corresponding timestamp data in the target smart core bucket pressure prediction set, which are arranged in order by timestamp data, are input into a neural network model; the neural network model comprises an input layer, a hidden layer, and an output layer, and the input layer is used to convert the format of the bucket pressure data and the corresponding timestamp data; The hidden layer is used to perform feature extraction and time series association operations on the converted data; the output layer is used to output the predicted value of the barrel pressure corresponding to the subsequent preset number of time nodes in the current transportation process; based on the sequence of the barrel pressure prediction values ​​​​at the subsequent time nodes in chronological order, the barrel pressure data change trend of the target smart core barrel during the current transportation process is predicted.

7. The method for automatic status detection applied to a smart core bucket according to claim 6, characterized in that: The specific steps of step S6 are as follows: Step S6-1: Select smart core barrels with the same attributes as the target smart core barrel during the current transportation process to form a set of smart core barrels from the same batch. Smart core barrels with the same attributes are those that carry the same liquid type and specifications and have the same barrel model. For each smart core barrel in the set of smart core barrels from the same batch, execute steps S1 to S5 to obtain a pressure change trend for each smart core barrel during the current transportation process. Step S6-2: Analyze and extract the longest historical transportation time of the target smart core bucket in step S1-1; Analyze and calculate the transport time in the current transport process to obtain the maximum remaining transport time of the target smart core barrel; input the maximum remaining transport time into the neural network model of step S5 to obtain the predicted value of the barrel pressure at the end of the maximum remaining transport time, which is recorded as the maximum pressure value at the end; Step S6-3: Detect the pressure threshold preset in the smart core bucket, mark the smart core bucket whose maximum pressure value exceeds the pressure threshold as abnormal, and issue a warning signal; The smart core bucket whose maximum pressure value does not exceed the pressure threshold in the bucket is marked as normal, completing the automatic status detection of the smart core bucket.

8. An automated status detection system for a smart core bucket, which is applied to the automated status detection method for a smart core bucket according to any one of claims 1 to 7, characterized in that: The automated state detection system includes a historical data acquisition module, a time sequence diagram construction module, a detection threshold calculation module, a prediction data screening module, a pressure trend prediction module and a batch state detection module; The historical data acquisition module is used to obtain the pressure data and corresponding timestamp data of the target smart core barrel in historical transportation and construct a pressure analysis set for the transport barrel; the timing diagram construction module is used to construct a pressure timing curve diagram of the current transportation and historical transportation of the target smart core barrel; the detection threshold calculation module is used to calculate the pressure detection threshold of the target smart core barrel; the prediction data screening module is used to construct a prediction set of the pressure in the barrel of the target smart core barrel; the pressure trend prediction module is used to obtain the change trend of the pressure data in the barrel of the target smart core barrel in current transportation; the batch status detection module is used to perform automatic status detection on the smart core barrels of the same batch.

9. The automated status detection system for a smart core bucket according to claim 8, characterized in that: The historical data acquisition module includes a historical transport record extraction unit and a historical pressure data archiving unit; the historical transport record extraction unit is used to extract the historical transport records of the target smart core bucket and determine the start and end timestamps and transport duration of each transport; the historical pressure data archiving unit is used to extract the pressure data in the bucket and the corresponding timestamp data based on the start and end timestamps of the historical transport and archive and store them; The timing diagram construction module includes a current pressure timing diagram construction unit and a reference pressure timing diagram construction unit; the current pressure timing diagram construction unit is used to construct a pressure timing curve diagram based on the pressure data in the barrel of the current transport and the corresponding timestamp data; the reference pressure timing diagram construction unit is used to construct a reference pressure timing curve diagram based on the pressure data in the barrel of the historical transport and the corresponding timestamp data; The detection threshold calculation module includes a basic threshold extraction unit and a threshold weighted fusion unit; the basic threshold extraction unit is used to extract the pressure value with the highest frequency of occurrence from the current and reference pressure time series curve diagrams; the threshold weighted fusion unit is used to obtain the pressure detection threshold of the target smart core bucket through weighted fusion calculation.

10. The automated status detection system for a smart core bucket according to claim 8, characterized in that: The prediction data screening module includes a compliance pressure screening unit and an over-threshold data processing unit; the compliance pressure screening unit is used to screen out the barrel pressure data and corresponding timestamp data that do not exceed the pressure detection threshold; the over-threshold data processing unit is used to replace the barrel pressure data that exceeds the threshold with the pressure detection threshold and archive it together with the compliance data; The pressure trend prediction module includes a neural network data processing unit and a pressure trend generation unit; the neural network data processing unit is used to input the data in the barrel pressure prediction set into the neural network model for format conversion, feature extraction and time series association operation; the pressure trend generation unit is used to generate the barrel pressure data change trend based on the subsequent time node pressure prediction value output by the neural network model; The batch status detection module includes a cut-off pressure prediction unit and a status threshold judgment unit; the cut-off pressure prediction unit is used to predict the pressure value in the barrel at the end of transportation based on the longest historical transportation time and the current transportation time; the status threshold judgment unit is used to compare the cut-off pressure prediction value with the preset barrel pressure threshold and mark the smart core barrel status.

Images