Hydraulic excavator action cycle statistical method, device, equipment, medium and system

Abstract

The invention discloses a hydraulic excavator action cycle statistical method, device, equipment, medium and system. The method comprises the steps of collecting main pump pressure real-time data of a target hydraulic excavator according to a preset sampling frequency, and generating main pump pressure time sequence data with a timestamp; performing down-sampling processing and binarization processing on the main pump pressure time sequence data to generate an original binarization state sequence; performing validity judgment on the original binarization sequence by using a preset validity judgment condition, and correcting the original binarization sequence based on a validity judgment result to obtain an updated binarization state sequence; and counting the number of action cycles based on the updated binarization state sequence, and generating an action cycle statistical result according to the extracted starting and ending timestamps of each action cycle. According to the embodiment of the invention, by carrying out downsampling, binarization correction and cyclic statistics on the main pump pressure data, the action cycle times and the starting and ending timestamps are automatically obtained, and accurate data support is provided for oil consumption tests.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering machinery, and in particular to a hydraulic excavator action cycle statistical method, device, equipment, medium and system. BACKGROUND

[0002] The work efficiency and energy consumption performance of a hydraulic excavator are core evaluation dimensions in the prototype test phase, and related evaluation needs to be completed through oil consumption target testing. In the excavating working condition, the action cycle times and corresponding oil consumption data in a specific time period need to be counted in order to accurately calculate the work efficiency and energy consumption level of the excavator. Therefore, the counting of the action cycle times of the hydraulic excavator is an indispensable key detection index in the oil consumption target testing, and the reliability of the counting result directly affects the judgment of the product performance and is an important basis for product optimization and performance verification.

[0003] At present, the counting scheme of the action cycle times of the hydraulic excavator still has significant deficiencies: (1) it relies on manual counting by test personnel, which not only needs to invest a high labor cost but also is prone to statistical errors due to external interference; (2) it uses the installation of a gyroscope to realize counting by detecting the horizontal direction angle cycle, but the gyroscope detection scheme is costly, the signal is unstable under complex working conditions, and the statistical precision is insufficient; (3) it counts based on the detection of the cycle state of the handle signal, but the handle signal detection is only suitable for full electric control models, the applicable range is narrow, and the compatibility is poor. Therefore, there is an urgent need for a counting scheme of the action cycle times of the hydraulic excavator that does not need additional sensors, has high statistical precision and is suitable for various types of hydraulic excavators. SUMMARY

[0004] Based on this, the present application provides a hydraulic excavator action cycle statistical method, device, equipment, medium and system to solve the problems of high labor cost, additional sensor dependence, poor adaptability or insufficient precision in the counting of the cycle times of the existing hydraulic excavator.

[0005] In a first aspect, an embodiment of the present application provides a hydraulic excavator action cycle statistical method, comprising:

[0006] acquiring, at a preset sampling frequency, real-time data of main pump pressure output by a built-in pressure sensor of a target hydraulic excavator and representing the running state of the target hydraulic excavator, to generate time-stamped main pump pressure time series data;

[0007] performing down-sampling processing on the main pump pressure time series data to obtain a down-sampling sequence, performing binaryzation processing on the down-sampling sequence by using a preset quantile value, and generating an original binaryzation state sequence;

[0008] performing validity determination on the original binaryzation sequence by using a preset validity determination condition, correcting the original binaryzation sequence based on the validity determination result, and obtaining an updated binaryzation state sequence;

[0009] count the number of action cycles based on the updated binary state sequence, and generate an action cycle statistical result according to the start and end time stamps of each action cycle extracted.

[0010] In a second aspect, the embodiment of the present application further provides a hydraulic excavator action cycle statistical device, comprising:

[0011] a pressure data sampling module configured to collect, at a preset sampling frequency, real-time data of main pump pressure output by a built-in pressure sensor of a target hydraulic excavator and representing a running state of the target hydraulic excavator, and generate time-stamped main pump pressure time series data;

[0012] a downsampling and binary processing module configured to perform downsampling processing on the main pump pressure time series data to obtain a downsampling sequence, and perform binary processing on the downsampling sequence by using a preset quantile value to generate an original binary state sequence;

[0013] a binary correction module configured to perform validity determination on the original binary sequence by using a preset validity determination condition, and correct the original binary sequence based on a validity determination result to obtain an updated binary state sequence;

[0014] an action cycle statistical result generation module configured to count the number of action cycles based on the updated binary state sequence, and generate an action cycle statistical result according to the start and end time stamps of each action cycle extracted.

[0015] In a third aspect, the embodiment of the present application further provides an electronic device, comprising at least one processor, and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the hydraulic excavator action cycle statistical method according to any one of the embodiments of the present application.

[0016] In a fourth aspect, the embodiment of the present application further provides a computer readable storage medium, which stores computer instructions for enabling a processor to implement the hydraulic excavator action cycle statistical method according to any one of the embodiments of the present application when executed.

[0017] In a fifth aspect, the embodiment of the present application further provides a hydraulic excavator action cycle statistical system, which comprises an edge computing terminal, a data storage server and a remote control terminal, wherein the edge computing terminal is configured to execute the hydraulic excavator action cycle statistical method according to any one of the embodiments of the present application.

[0018] The embodiment of the present application directly uses the main pump pressure data of the excavator itself without additional sensors, breaks through the dependence of the traditional scheme on additional hardware or specific models, and considers economy and universality; through progressive processing of "down-sampling-binaryzation-dynamic correction", the real working state is accurately captured by adaptive threshold filtering interference, and the precision limitation of fixed determination logic under complex working conditions is overcome; a closed loop from data acquisition to cycle statistics is formed, reliable results with timestamps are output, innovative technical support is provided for oil consumption test evaluation, and the core problems of large manual statistical error and poor stability of the traditional scheme are solved.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flow chart of a hydraulic excavator action cycle statistical method provided by the first embodiment of the present application;

[0022] Figure 2 is a structural schematic diagram of a hydraulic excavator action cycle statistical device provided by the second embodiment of the present application;

[0023] Figure 3 is a structural schematic diagram of an electronic device of a hydraulic excavator action cycle statistical method provided by the third embodiment of the present application;

[0024] Figure 4 is a connection relationship schematic diagram of an action cycle statistical system for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1 This is a flowchart of a hydraulic excavator motion cycle statistics method provided in Embodiment 1 of the present invention. This embodiment is applicable to the fuel consumption test scenario during the prototype testing phase of a hydraulic excavator, where the number of motion cycles is accurately counted under excavation conditions. This method can be executed by a hydraulic excavator motion cycle statistics device, which can be implemented in hardware and / or software and can be configured in the edge computing terminal of the motion cycle statistics system. Figure 1 As shown, the method includes:

[0029] S110. Collect real-time main pump pressure data representing the operating status of the target hydraulic excavator through its built-in pressure sensor according to the preset sampling frequency, and generate time-series data of main pump pressure with timestamp.

[0030] In this embodiment, the motion cycle statistics system refers to a system used to realize the motion cycle statistics of a hydraulic excavator, including at least an edge computing terminal, a data storage server, and a remote control terminal, with each terminal interacting through a communication network. The edge computing terminal is a computing device deployed locally on the hydraulic excavator, possessing data acquisition, processing, and preliminary analysis capabilities. It can directly communicate with the excavator's built-in sensors, reducing data transmission latency. The preset sampling frequency value is based on the pressure data acquisition interval pre-set according to the hydraulic excavator's operating scenario. The built-in pressure sensor is a sensor originally configured on the hydraulic excavator for monitoring the main pump pressure; its output signal directly reflects the main pump pressure changes during excavator operation. The real-time main pump pressure data is dynamic data characterizing the working pressure of the hydraulic excavator's main pump, collected in real-time by the pressure sensor; its numerical changes are directly related to the excavator's operating status. The timestamped main pump pressure time-series data refers to a set of main pump pressure data arranged in chronological order of acquisition time, with each pressure value corresponding to a unique timestamp, forming a sequence of "time + pressure value".

[0031] The edge computing terminal establishes communication with the built-in pressure sensor of the target hydraulic excavator according to a preset sampling frequency, and receives and records the main pump pressure data in real time. For each pressure value collected, the corresponding collection time is recorded synchronously, and finally a sequence of "timestamp + pressure value" arranged in chronological order is formed. For example, if the pressure is 28MPa collected at time t1 and 30MPa collected at time t2, the time series data is [(t1,28), (t2,30), ...].

[0032] S120. The main pump pressure time series data is downsampled to obtain a downsampled sequence. The downsampled sequence is binarized using a preset quantile value to generate an original binarized state sequence.

[0033] Downsampling processing refers to the edge computing terminal first downsampling the main pump pressure time-series data. If the original sampling frequency is higher than the preset target frequency, data is extracted at time intervals according to the target frequency to obtain the downsampling sequence; if the original frequency already meets the target, the time-series data is directly used as the downsampling sequence. Binarization processing refers to the edge computing terminal calling the preset quantile value obtained by statistically analyzing historical pressure data for the target excavator operation scenario stored in the data storage server, comparing each pressure value in the downsampling sequence with the quantile value. For example, when the pressure is ≥25MPa, it is marked as state value 0; when the pressure is <25MPa, it is marked as state value 1, finally generating the original binary state sequence [(t1,0), (t2,0), (t3,1), ...]), realizing a simplified representation of the pressure signal.

[0034] S130. Perform validity determination on the original binary sequence using preset validity determination conditions, and correct the original binary sequence based on the validity determination result to obtain an updated binary state sequence.

[0035] The preset validity criteria refer to the rules used to determine whether "continuous segments with 0 values" in the original binary state sequence represent actual work actions. The updated binary state sequence is obtained by correcting invalid 0-value segments in the original binary state sequence, eliminating short-term fluctuations that are mistakenly identified as "work," and more accurately reflecting the actual action status. The original binary state sequence may contain misjudgments due to brief pressure fluctuations (such as instantaneous high pressure during non-work periods being marked as 0), therefore, it needs to be corrected through validity criteria. That is, the edge computing terminal identifies all continuous segments with a state value of 0 in the sequence according to the preset validity criteria, and counts the cumulative duration of each segment; these durations are divided into two categories, effective work and invalid fluctuations, using a clustering algorithm, and a dynamic threshold is calculated. In a specific example, if the duration of a 0-value segment is ≥2 seconds, it is determined to be effective work, and the state value remains 0; if the duration of a 0-value segment is <2 seconds, it is determined to be invalid fluctuation, and the state value is corrected to 1. The updated binary state sequence obtained after correction more accurately reflects the actual work status.

[0036] S140. Count the number of action cycles based on the updated binarized state sequence, and generate action cycle statistics results based on the start and end timestamps of each extracted action cycle.

[0037] The number of operation cycles refers to the cumulative number of times a hydraulic excavator completes a single full operation, such as "digging-resetting". Start and end timestamps are the timestamps marking the beginning and end of each operation cycle, used to calculate the duration of a single cycle. Operation cycle statistics include comprehensive data on the total number of cycles, the start and end timestamps of each cycle, and its duration, used to evaluate the excavator's operational efficiency and energy consumption.

[0038] This invention eliminates the need for additional sensors, directly utilizing the excavator's own main pump pressure data. This breaks through the dependence of traditional solutions on additional hardware or specific machine models, balancing economy and universality. It employs a progressive processing method of "downsampling-binarization-dynamic correction," using adaptive threshold filtering to accurately capture the actual operating state and overcome the accuracy limitations of fixed judgment logic under complex working conditions. It forms a closed loop from data acquisition to cyclical statistics, outputting reliable results with timestamps. This provides innovative technical support for fuel consumption test evaluation, solving the core problems of large errors in manual statistics and poor stability in traditional solutions.

[0039] Optionally, the main pump pressure time-series data is downsampled to obtain a downsampled sequence. The downsampled sequence is then binarized using a preset quantile value to generate an original binarized state sequence. This process may include:

[0040] Read the pre-written standard frequency, compare the standard frequency with the preset sampling frequency, and determine whether the preset sampling frequency of the main pump pressure timing data is greater than the standard frequency: wherein, the preset sampling frequency is set in advance according to the target hydraulic excavator operation scenario;

[0041] If not, the main pump pressure time series data is directly used as a downsampling sequence, which is a sequence consisting of multiple segments containing timestamps and pressure values ​​arranged in chronological order.

[0042] If so, the main pump pressure time series data is extracted at equal intervals according to the standard frequency time interval to obtain a downsampling sequence with a fixed time interval.

[0043] Traverse each segment in the downsampling sequence according to the time order, and compare the pressure value in each segment with the preset quantile value one by one:

[0044] The preset quantile value is obtained by calling the historical main pump pressure time series data of the target hydraulic excavator operation scenario in the data storage server.

[0045] Replace pressure values ​​less than the preset quantile with state value 1, and replace pressure values ​​greater than or equal to the preset quantile with state value 0 to obtain the original binary state sequence corresponding to the downsampling sequence.

[0046] The standard frequency is a pre-set target frequency used for downsampling processing. Its value is determined based on the hydraulic excavator's motion characteristics to ensure that the downsampled data retains key pressure change information. The edge computing terminal reads the pre-written standard frequency and compares it with the preset sampling frequency of the main pump pressure time series data to determine whether the preset sampling frequency is greater than the standard frequency. In a specific example, if the preset sampling frequency is less than or equal to the standard frequency, it means that the original data density is lower than or equal to the target density, and no additional processing is required; the main pump pressure time series data is directly used as the downsampling sequence. If the preset sampling frequency is greater than the standard frequency, it means that the original data density is too high, and downsampling is required. In this case, the main pump pressure time series data is extracted at equal intervals according to the standard frequency time interval. For example, from 10 data points collected per second, the 10th data point is retained, resulting in a downsampling sequence with a fixed time interval of 1 second.

[0047] The edge computing terminal sends a request to the data storage server, retrieving preset quantile values ​​obtained from the statistical analysis of historical main pump pressure time-series data for the target hydraulic excavator's operating scenario. The edge computing terminal iterates through each segment of the downsampled sequence in chronological order, comparing the pressure value of each segment with the preset quantile value. If the pressure value is <25MPa, it is determined to be a low-pressure state (not in operation), and the pressure value of that segment is replaced with a state value of 1. If the pressure value is ≥25MPa, it is determined to be a high-pressure state (effective operation), and the pressure value of that segment is replaced with a state value of 0. The resulting original binary state sequence is formatted as multiple segments of "timestamp + state value" arranged in chronological order, realizing the conversion from a continuous pressure signal to a discrete state signal.

[0048] In this embodiment, downsampling and binarization processing are based on the output data of the hydraulic excavator's own main pump pressure sensor. The state is distinguished by preset quantile values, which reduces data redundancy, improves processing efficiency, avoids additional hardware costs, and balances economy and basic recognition accuracy.

[0049] Furthermore, the binarized sequence is validated using preset validity criteria, and the original binarized sequence is corrected based on the validity criteria to obtain an updated binarized state sequence, which may include:

[0050] In the original binary state sequence, all target segments with state values ​​of 0 are identified, and multiple consecutive segments with 0 values ​​are obtained by combining them according to timestamps; wherein each group of consecutive segments with 0 values ​​includes at least one target segment;

[0051] By calculating the cumulative duration of each continuous segment with a zero value, and taking each continuous segment with a zero value and its corresponding cumulative duration as a duration data point, a duration dataset is obtained.

[0052] Cluster analysis is performed on each duration data in the duration dataset. The duration dataset is divided into a first-class duration subset and a second-class duration subset using a clustering algorithm. The cluster center points of the first-class duration subset and the second-class duration subset are calculated respectively.

[0053] Calculate the arithmetic mean of two cluster centers and use it as a dynamic threshold. Then, compare the dynamic threshold with the cumulative duration of each consecutive segment with a value of 0 in the duration dataset.

[0054] If it is determined that the cumulative duration of the current 0-value continuous segment group is greater than or equal to the dynamic threshold, then the current 0-value continuous segment is determined as a valid work segment, and the state value in the current 0-value continuous segment group remains unchanged.

[0055] If the cumulative duration of the current 0-value continuous segment group is less than the dynamic threshold, then the current 0-value continuous segment is determined to be an invalid work segment, and the status value in the current 0-value continuous segment group is corrected from 0 to 1;

[0056] By summarizing the new continuous segments with 0 values ​​after validity determination, an updated binary state sequence is obtained; wherein the updated binary state sequence consists of multiple segments including timestamps and state values.

[0057] In the original binary state sequence, brief segments with zero values ​​may appear due to factors such as hydraulic system pressure fluctuations and external interference. Therefore, the edge computing terminal traverses the original binary state sequence, filters out all target segments with a state value of 0, and combines consecutive target segments into multiple consecutive segments with zero values ​​in chronological order. For example, if the original sequence is [(t1,1), (t2,0), (t3,0), (t4,1), (t5,0), (t6,1)], then two consecutive segments with zero values ​​are identified: [(t2,0), (t3,0)] and [(t5,0)]. Note that each segment contains at least one target segment. For each consecutive segment with zero values, its cumulative duration is calculated, which is the difference between the timestamps of the first and last segments of the segment. Each consecutive segment with a value of 0 is associated with its cumulative duration, and the summaries are used to obtain a duration dataset, such as: {([(t2,0),(t3,0)],5 seconds), ([(t5,0)],0.5 seconds)}.

[0058] The edge computing terminal uses a clustering algorithm to classify the cumulative duration in the time-based dataset, automatically dividing it into two subsets. For example, the first subset contains short-duration data with a cluster center of 0.8 seconds; the second subset contains long-duration data with a cluster center of 4.2 seconds. The core of clustering analysis is to automatically distinguish the duration difference between actual operations and pressure fluctuations using data distribution characteristics, without requiring a manually preset fixed threshold, adapting to duration changes in different operation scenarios. The arithmetic mean of the two cluster centers is used to obtain the dynamic threshold. Continuing with the above example, the dynamic threshold = (0.8 + 4.2) / 2 = 2.5 seconds. This dynamic threshold is a dynamically adaptive value that changes with the actual duration data distribution, offering greater versatility and accuracy compared to a fixed threshold. Valid operation segments correspond to the excavator's actual operating actions; invalid operation segments correspond to brief pressure fluctuations during non-operational states. The edge computing terminal compares the cumulative duration of each consecutive segment with a value of 0 with a dynamic threshold one by one, and executes different correction logic: if the cumulative duration is greater than or equal to the dynamic threshold, it is determined to be a valid job segment, and the state value of all segments within the segment remains 0 (no correction is needed); if the cumulative duration is less than the dynamic threshold, it is determined to be an invalid job segment, and the state value of all segments within the segment is corrected from 0 to 1. All corrected segments are then summarized: valid job segments retain 0 values, invalid job segments are corrected to 1 values, and non-job segments with 1 values ​​remain unchanged, ultimately forming an updated binary state sequence. For example, the corrected sequence in the above example is [(t1,1), (t2,0), (t3,0), (t4,1), (t5,1), (t6,1)], eliminating the invalid 0-value segment [(t5,0)], which more accurately reflects the actual job status.

[0059] In this embodiment, the validity determination divides the duration of continuous segments with zero values ​​into two categories through cluster analysis. Instead of a fixed threshold, a dynamic threshold is used to replace invalid fluctuations and valid operations. This can adapt to the pressure characteristics under different working conditions, accurately correct misjudged segments, and solve the problem of insufficient accuracy caused by interference in existing solutions.

[0060] Optionally, the number of action loops is counted based on the updated binarized state sequence, and action loop statistics are generated based on the extracted start and end timestamps of each action loop, including:

[0061] Traverse the state values ​​of each segment in the updated binary state sequence, and use the preset complete action loop judgment condition to filter out the candidate segments located between two different state values ​​as switching nodes.

[0062] Traverse each switching node sequentially according to time. If the state value of the segment preceding the current switching node is 1 and the state value of the segment following the current switching node is 0, then record the timestamp corresponding to the current switching node as the start timestamp of the current action loop group.

[0063] Continue traversing the next switching node from the next segment. When the state value of the previous segment of the next switching node is identified as 0 and the state value of the next segment of the next switching node is 1, the timestamp corresponding to the next switching stage is recorded as the end timestamp of the current action loop group, and the action loop count is incremented by 1.

[0064] The total number of all action loop groups is counted as the action loop count, and the start and end timestamps of each loop group are compiled to form an action loop statistics result that includes the action loop count and the duration of each loop.

[0065] Wherein, if the updated binary state sequence starts with state value 0, then the first node in the sequence whose state value changes from 0 to 1 is taken as the end timestamp of the first action loop group, and the start timestamp of the sequence is taken as the start timestamp of the first action loop group; if the sequence ends with state value 0, then it is considered that the current action loop group has not ended, and it is not included in the current loop count.

[0066] A segment refers to a basic unit arranged chronologically in the updated binary state sequence. Each segment contains a timestamp and a state value, and can be understood as a single element in the sequence. For example, in [(t1,0), (t2,0), (t3,1), ...], (t1,0) is a segment. The edge computing terminal traverses all segments in the updated binary state sequence chronologically, comparing the state values ​​of adjacent segments one by one. For example, in the sequence [(t1,1), (t2,1), (t3,0), (t4,0), (t5,1)], (t3,0) is the "1→0" switching node, and (t5,1) is the "0→1" switching node. The role of the switching node is to locate the time point of state change, providing a marker for the start and end determination of subsequent loops. When traversing to a certain switching node, if the state value of its previous segment is 1 and its own state value is 0 (i.e., "1→0" switching), then the timestamp of that node is recorded as the start timestamp of the current action loop group (marking the start of the job). The preset complete action loop determination condition refers to the rule used to define a complete action loop. Continuing to traverse from this node, when the next switching node is encountered, and its previous segment's state value is 0 and its own state value is 1 (i.e., a "0→1" switch), the timestamp of this node is recorded as the end timestamp of the current action loop group (marking the end of the task), and the action loop count is incremented by 1. For example, if switching node t3 (1→0) is the start timestamp and subsequent switching node t5 (0→1) is the end timestamp, a complete loop is formed, and the count is recorded as 1. After traversing all switching nodes, the total number of complete action loop groups is accumulated, which is the total number of action loops. Simultaneously, for each loop group, the end timestamp is subtracted from the start timestamp to calculate the loop duration. The final action loop statistics include: the total number of loops, the start timestamp, end timestamp, and corresponding duration for each loop group.

[0067] If the updated binary state sequence starts with state value 0 (i.e., the first segment of the sequence is 0, indicating that the excavator was already in operation when monitoring began), then the timestamp of the first "0→1" switching node in the sequence is used as the end timestamp of the first loop, and the timestamp of the first segment of the sequence is used as the start timestamp of the loop (completing the start marker of the loop). If the sequence ends with state value 0 (i.e., the last segment is 0, indicating that the excavator was still operating when monitoring ended), then the incomplete operation segment is not included in the loop count (only the complete "1→0→1" loop is counted).

[0068] In this embodiment, the action cycle statistics accurately extract the start and end timestamps and count the number of times by identifying state switching nodes and complete cycle rules. The output results can directly support the efficiency and energy consumption assessment in fuel consumption tests. Moreover, because it is based on the main pump pressure signal, it is compatible with various hydraulic excavators, breaking the limitation of the solution that only relies on the handle signal for fully electric control models.

[0069] Example 2

[0070] Figure 2 This is a schematic diagram of a flood evolution simulation device under a levee breach scenario provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes:

[0071] The pressure data sampling module 210 is used to collect real-time main pump pressure data representing the operating status of the target hydraulic excavator through the built-in pressure sensor according to a preset sampling frequency, and generate time-series main pump pressure data with timestamps.

[0072] The downsampling and binarization processing module 220 is used to downsample the main pump pressure time series data to obtain a downsampled sequence, and to binarize the downsampled sequence using a preset quantile value to generate an original binarized state sequence.

[0073] Binarization correction module 230 is used to perform validity determination on the original binarized sequence using preset validity determination conditions, and correct the original binarized sequence based on the validity determination result to obtain an updated binarized state sequence.

[0074] The action loop statistics generation module 240 is used to count the number of action loops based on the updated binary state sequence, and generate action loop statistics results based on the extracted start and end timestamps of each action loop.

[0075] This invention eliminates the need for additional sensors, directly utilizing the excavator's own main pump pressure data. This breaks through the dependence of traditional solutions on additional hardware or specific machine models, balancing economy and universality. It employs a progressive processing method of "downsampling-binarization-dynamic correction," using adaptive threshold filtering to accurately capture the actual operating state and overcome the accuracy limitations of fixed judgment logic under complex working conditions. It forms a closed loop from data acquisition to cyclical statistics, outputting reliable results with timestamps. This provides innovative technical support for fuel consumption test evaluation, solving the core problems of large errors in manual statistics and poor stability in traditional solutions.

[0076] Optionally, based on the above embodiments, the downsampling and binarization processing module 220 may include:

[0077] A frequency comparison unit is used to read a pre-written standard frequency, compare the standard frequency with the preset sampling frequency, and determine whether the preset sampling frequency of the main pump pressure timing data is greater than the standard frequency: wherein the preset sampling frequency is set in advance according to the target hydraulic excavator operation scenario;

[0078] The first type of downsampling processing unit is used to, if not, directly use the main pump pressure time series data as a downsampling sequence, wherein the downsampling sequence is a sequence consisting of multiple segments containing timestamps and pressure values ​​arranged in chronological order.

[0079] The second type of downsampling processing unit is used to, if so, extract the main pump pressure time series data at equal intervals according to the standard frequency time interval to obtain a downsampling sequence with a fixed time interval.

[0080] The pressure value comparison unit is used to traverse each segment in the downsampling sequence according to the time order, and compare the pressure value in each segment with the preset quantile value one by one:

[0081] The preset quantile value is obtained by calling the historical main pump pressure time series data of the target hydraulic excavator operation scenario in the data storage server.

[0082] The state value replacement unit is used to replace pressure values ​​less than the preset quantile value with state value 1, and pressure values ​​greater than or equal to the preset quantile value with state value 0, so as to obtain the original binary state sequence corresponding to the downsampling sequence.

[0083] Optionally, based on the above embodiments, the binarization correction module 230 may include:

[0084] A state value recognition unit is used to identify all target segments with state values ​​of 0 in the original binary state sequence, and to combine them according to timestamps to obtain multiple continuous segments with 0 values; wherein each group of continuous segments with 0 values ​​includes at least one target segment;

[0085] The cumulative duration statistics unit is used to calculate the cumulative duration of each continuous segment with a zero value, and to collect each continuous segment with a zero value and its corresponding cumulative duration as a duration data point, thereby obtaining a duration dataset.

[0086] The duration clustering unit is used to perform cluster analysis on each duration data in the duration dataset, and to divide the duration dataset into a first-class duration subset and a second-class duration subset using a clustering algorithm, and to calculate the cluster center points of the first-class duration subset and the second-class duration subset respectively.

[0087] A dynamic threshold generation unit is used to calculate the arithmetic mean of two cluster centers and use it as a dynamic threshold. The dynamic threshold is then compared one by one with the cumulative duration of each continuous segment with a value of 0 in the duration dataset.

[0088] The first validity determination unit is used to determine the current 0-value continuous segment as a valid work segment if the cumulative duration of the current 0-value continuous segment group is greater than or equal to the dynamic threshold, and to keep the state value in the current 0-value continuous segment group unchanged.

[0089] The second validity determination unit is used to determine the current 0-value continuous segment as an invalid operation segment if the cumulative duration of the current 0-value continuous segment group is less than the dynamic threshold, and to correct the status value in the current 0-value continuous segment group from 0 to 1.

[0090] The binarized state sequence update unit is used to summarize each group of new continuous segments with 0 values ​​after validity determination to obtain an updated binarized state sequence; wherein, the updated binarized state sequence is composed of multiple segments including timestamps and state values.

[0091] Optionally, based on the above embodiments, the action loop statistics result generation module 240 may include:

[0092] The switching node filtering unit is used to traverse the state values ​​of each segment in the updated binary state sequence and use the preset complete action loop judgment condition to filter out the candidate segments located between two different state values ​​as switching nodes.

[0093] The loop start time determination unit is used to traverse each switching node in chronological order. If the state value of the segment before the current switching node is 1 and the state value of the segment after the current switching node is 0, the timestamp corresponding to the current switching node is recorded as the start timestamp of the current action loop group.

[0094] The loop end time determination unit is used to continue traversing the next switching node from the next segment. When it is found that the state value of the previous segment of the next switching node is 0 and the state value of the next segment of the next switching node is 1, the timestamp corresponding to the next switching stage is recorded as the end timestamp of the current action loop group, and the action loop count is incremented by 1.

[0095] The loop count unit is used to count the total number of all action loop groups as the action loop count, and to organize the start and end timestamps of each loop group to form an action loop statistics result that includes the action loop count and the duration of each loop.

[0096] Wherein, if the updated binary state sequence starts with state value 0, then the first node in the sequence whose state value changes from 0 to 1 is taken as the end timestamp of the first action loop group, and the start timestamp of the sequence is taken as the start timestamp of the first action loop group; if the sequence ends with state value 0, then it is considered that the current action loop group has not ended, and it is not included in the current loop count.

[0097] The hydraulic excavator motion cycle statistics device provided in this embodiment of the invention can execute the hydraulic excavator motion cycle statistics method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0098] Example 3

[0099] Figure 3 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0100] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0101] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0102] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a statistical method for the motion cycle of a hydraulic excavator.

[0103] That is: collect real-time main pump pressure data, which represents the operating status of the target hydraulic excavator, through the built-in pressure sensor, according to the preset sampling frequency, and generate time-series data of main pump pressure with timestamps;

[0104] The main pump pressure time series data is downsampled to obtain a downsampled sequence. The downsampled sequence is then binarized using a preset quantile value to generate an original binarized state sequence.

[0105] The original binary sequence is validated using preset validation criteria, and the original binary sequence is corrected based on the validation result to obtain an updated binary state sequence.

[0106] The number of action loops is counted based on the updated binary state sequence, and the action loop statistics are generated based on the start and end timestamps of each extracted action loop.

[0107] In some embodiments, a hydraulic excavator motion cycle statistics method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the hydraulic excavator motion cycle statistics method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a hydraulic excavator motion cycle statistics method by any other suitable means (e.g., by means of firmware).

[0108] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0110] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0111] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0112] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0113] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0114] Example 4

[0115] Figure 4 This is a schematic diagram of the connection relationship of a hydraulic excavator motion cycle statistics system provided in Embodiment 4 of the present invention, as shown below. Figure 4 As shown, the system includes: an edge computing terminal, a data storage server, and a remote control terminal, wherein the edge computing terminal is used to execute a hydraulic excavator motion cycle statistics method according to any embodiment of the present invention, including:

[0116] The edge computing terminal is used to collect real-time main pump pressure data representing the operating status of the target hydraulic excavator through its built-in pressure sensor at a preset sampling frequency, and generate time-series main pump pressure data with timestamps.

[0117] The edge computing terminal is used to downsample the main pump pressure time series data to obtain a downsampled sequence, and then use a preset quantile value to binarize the downsampled sequence to generate an original binarized state sequence.

[0118] The edge computing terminal is used to perform validity determination on the original binary sequence using preset validity determination conditions, and to correct the original binary sequence based on the validity determination result to obtain an updated binary state sequence.

[0119] The edge computing terminal is used to count the number of action cycles based on the updated binary state sequence, and generate action cycle statistics results based on the extracted start and end timestamps of each action cycle.

[0120] In this embodiment, the hydraulic excavator serves as the data source, outputting real-time main pump pressure data through its built-in pressure sensor to provide raw information to the system. The edge computing terminal is the data processing core of the hydraulic excavator's motion cycle statistics system. The edge computing terminal establishes stable communication with the excavator's built-in pressure sensor via a hardware interface, collecting main pump pressure data in real-time at a preset sampling frequency and synchronously generating timestamped main pump pressure time-series data. The edge computing terminal locally executes downsampling, binarization, validity correction, and cyclic statistical algorithms, sequentially generating a downsampling sequence, an original binarized state sequence, and an updated binarized state sequence, ultimately outputting the motion cycle statistics results. This achieves localized real-time processing, avoiding remote transmission delays, and provides a complete data foundation for subsequent storage and control.

[0121] Optionally, the data storage server is communicatively connected to the edge computing terminal and is used to receive action cycle statistical data packets packaged and transmitted by the edge computing terminal at a preset period after generating action cycle statistical results based on the start and end timestamps of each extracted action cycle. The action cycle statistical data packets include: main pump pressure time series data, downsampling sequence, updated binarized state sequence, and action cycle statistical results.

[0122] The data storage server is used to perform integrity verification on the received data packets, and store them in chronological order after the integrity verification is passed, forming a historical data set.

[0123] The data storage server is used to retrieve data packets for the corresponding time period from the historical data set according to the time range parameter in the request when it receives a historical data query request initiated by the remote control terminal, and feed them back to the remote control terminal for review analysis or report generation.

[0124] In this embodiment, the edge computing terminal packages the main pump pressure time-series data, downsampling sequence, updated binarized state sequence, and action cycle statistical results into an action cycle statistical data package according to a preset cycle, and pushes it to the data storage server via a wireless network. After receiving the data package, the data storage server performs integrity verification by checking field integrity and data format standardization. After the verification is successful, it archives the data according to the index rule of "device number-timestamp" to form a structured historical data set. Test personnel can initiate a query request by inputting query conditions through a remote control terminal. The data storage server matches the index according to the request parameters, retrieves the complete data package for the corresponding time period, and transmits it back to the remote control terminal through encrypted transmission, supporting test review or report generation.

[0125] Furthermore, the remote control terminal is communicatively connected to the edge computing terminal, and is used to receive and display the action cycle statistics pushed in real time by the edge computing terminal after generating the action cycle statistics by extracting the start and end timestamps of each action cycle;

[0126] The remote control terminal is used to send configuration instructions to the edge computing terminal based on the pushed action cycle statistics results. The configuration instructions are used to guide the edge computing terminal to adjust at least one of the preset sampling frequency, preset quantile value, and validity judgment conditions.

[0127] The remote control terminal receives real-time action cycle statistics pushed by the edge computing terminal, displaying key indicators such as the number of cycles and the duration of each cycle in a visual interface. If the test personnel find insufficient statistical accuracy or poor adaptability, they can input new parameters through the parameter configuration module of the remote control terminal to generate configuration instructions. The configuration instructions are encrypted and sent to the edge computing terminal. Upon receiving the instructions, the terminal automatically overwrites the original operating parameters and executes the statistical process according to the new parameters from the next data acquisition, achieving dynamic optimization. Through the instruction flow between the remote control terminal and the edge computing terminal, dynamic parameter adjustment is achieved without disassembling or operating the equipment on-site. This adapts to the pressure characteristics of different excavator models and different working scenarios, improving the system's versatility.

[0128] This embodiment achieves full automation of the "automatic data acquisition - automatic processing - automatic storage - remote monitoring - remote control" process through three-terminal collaboration, eliminating the need for on-site personnel for testing. It is particularly suitable for long-term continuous fuel consumption test scenarios, reducing labor costs and improving test efficiency.

[0129] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0130] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for statistical analysis of the motion cycles of a hydraulic excavator, characterized in that, Executed by the edge computing terminal in the action loop statistics system, including: Collect real-time main pump pressure data, which represents the operating status of the target hydraulic excavator, through the built-in pressure sensor, according to the preset sampling frequency, and generate time-series main pump pressure data with timestamps. The main pump pressure time series data is downsampled to obtain a downsampled sequence. The downsampled sequence is then binarized using a preset quantile value to generate an original binarized state sequence. The original binary sequence is validated using preset validation criteria, and the original binary sequence is corrected based on the validation result to obtain an updated binary state sequence. The number of action loops is counted based on the updated binary state sequence, and the action loop statistics are generated based on the start and end timestamps of each extracted action loop.

2. The method according to claim 1, characterized in that, The main pump pressure time series data is downsampled to obtain a downsampled sequence. The downsampled sequence is then binarized using a preset quantile value to generate an original binarized state sequence, including: Read the pre-written standard frequency, compare the standard frequency with the preset sampling frequency, and determine whether the preset sampling frequency of the main pump pressure timing data is greater than the standard frequency: wherein, the preset sampling frequency is set in advance according to the target hydraulic excavator operation scenario; If not, the main pump pressure time series data is directly used as a downsampling sequence, which is a sequence consisting of multiple segments containing timestamps and pressure values ​​arranged in chronological order. If so, the main pump pressure time series data is extracted at equal intervals according to the standard frequency time interval to obtain a downsampling sequence with a fixed time interval. Traverse each segment in the downsampling sequence according to the time order, and compare the pressure value in each segment with the preset quantile value one by one: The preset quantile value is obtained by calling the historical main pump pressure time series data of the target hydraulic excavator operation scenario in the data storage server. Replace pressure values ​​less than the preset quantile with state value 1, and replace pressure values ​​greater than or equal to the preset quantile with state value 0 to obtain the original binary state sequence corresponding to the downsampling sequence.

3. The method according to claim 2, characterized in that, The binarized sequence is validated using preset validation criteria. Based on the validation result, the original binarized sequence is corrected to obtain an updated binarized state sequence, including: In the original binary state sequence, all target segments with state values ​​of 0 are identified, and multiple consecutive segments with 0 values ​​are obtained by combining them according to timestamps; wherein each group of consecutive segments with 0 values ​​includes at least one target segment; By calculating the cumulative duration of each continuous segment with a zero value, and taking each continuous segment with a zero value and its corresponding cumulative duration as a duration data point, a duration dataset is obtained. Cluster analysis is performed on each duration data in the duration dataset. The duration dataset is divided into a first-class duration subset and a second-class duration subset using a clustering algorithm. The cluster center points of the first-class duration subset and the second-class duration subset are calculated respectively. Calculate the arithmetic mean of two cluster centers and use it as a dynamic threshold. Then, compare the dynamic threshold with the cumulative duration of each consecutive segment with a value of 0 in the duration dataset. If it is determined that the cumulative duration of the current 0-value continuous segment group is greater than or equal to the dynamic threshold, then the current 0-value continuous segment is determined as a valid work segment, and the state value in the current 0-value continuous segment group remains unchanged. If the cumulative duration of the current 0-value continuous segment group is less than the dynamic threshold, then the current 0-value continuous segment is determined to be an invalid work segment, and the status value in the current 0-value continuous segment group is corrected from 0 to 1; By summarizing the new continuous segments with 0 values ​​after validity determination, an updated binary state sequence is obtained; wherein the updated binary state sequence consists of multiple segments including timestamps and state values.

4. The method according to claim 2, characterized in that, The number of action loops is counted based on the updated binarized state sequence, and action loop statistics are generated based on the extracted start and end timestamps of each action loop, including: Traverse the state values ​​of each segment in the updated binary state sequence, and use the preset complete action loop judgment condition to filter out the candidate segments located between two different state values ​​as switching nodes. Traverse each switching node sequentially according to time. If the state value of the segment preceding the current switching node is 1 and the state value of the segment following the current switching node is 0, then record the timestamp corresponding to the current switching node as the start timestamp of the current action loop group. Continue traversing the next switching node from the next segment. When the state value of the previous segment of the next switching node is identified as 0 and the state value of the next segment of the next switching node is 1, the timestamp corresponding to the next switching stage is recorded as the end timestamp of the current action loop group, and the action loop count is incremented by 1. The total number of all action loop groups is counted as the action loop count, and the start and end timestamps of each loop group are compiled to form an action loop statistics result that includes the action loop count and the duration of each loop. Wherein, if the updated binary state sequence starts with state value 0, then the first node in the sequence whose state value changes from 0 to 1 is taken as the end timestamp of the first action loop group, and the start timestamp of the sequence is taken as the start timestamp of the first action loop group; if the sequence ends with state value 0, then it is considered that the current action loop group has not ended, and it is not included in the current loop count.

5. A hydraulic excavator motion cycle statistics device, characterized in that, The edge computing terminal configured in the action loop statistics system includes: The pressure data sampling module is used to collect real-time main pump pressure data, which represents the operating status of the target hydraulic excavator, through the built-in pressure sensor, according to a preset sampling frequency, and generate time-series main pump pressure data with timestamps. The downsampling and binarization processing module is used to downsample the main pump pressure time series data to obtain a downsampled sequence, and then use a preset quantile value to binarize the downsampled sequence to generate an original binarized state sequence. The binarization correction module is used to perform validity determination on the original binarized sequence using preset validity determination conditions, and correct the original binarized sequence based on the validity determination result to obtain an updated binarized state sequence. The action loop statistics generation module is used to count the number of action loops based on the updated binary state sequence, and generate action loop statistics results based on the extracted start and end timestamps of each action loop.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a hydraulic excavator motion cycle statistical method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a method for statistically analyzing the motion cycles of a hydraulic excavator according to any one of claims 1-4.

8. A hydraulic excavator motion cycle statistics system, characterized in that, The system includes: an edge computing terminal, a data storage server, and a remote control terminal, wherein the edge computing terminal is used to execute the hydraulic excavator motion cycle statistics method according to any one of claims 1-5, including: The edge computing terminal is used to collect real-time main pump pressure data representing the operating status of the target hydraulic excavator through its built-in pressure sensor at a preset sampling frequency, and generate time-series main pump pressure data with timestamps. The edge computing terminal is used to downsample the main pump pressure time series data to obtain a downsampled sequence, and then use a preset quantile value to binarize the downsampled sequence to generate an original binarized state sequence. The edge computing terminal is used to perform validity determination on the original binary sequence using preset validity determination conditions, and to correct the original binary sequence based on the validity determination result to obtain an updated binary state sequence. The edge computing terminal is used to count the number of action cycles based on the updated binary state sequence, and generate action cycle statistics results based on the extracted start and end timestamps of each action cycle.

9. The system according to claim 8, characterized in that, The data storage server is communicatively connected to the edge computing terminal and is used to receive action cycle statistical data packets packaged and transmitted by the edge computing terminal at a preset period after generating action cycle statistical results based on the start and end timestamps of each extracted action cycle. The action cycle statistical data packets include: main pump pressure time series data, downsampling sequence, updated binarized state sequence, and action cycle statistical results. The data storage server is used to perform integrity verification on the received data packets, and store them in chronological order after the integrity verification is passed, forming a historical data set. The data storage server is used to retrieve the historical action cycle statistics package for the corresponding time period from the historical data set according to the time range parameter in the request when it receives a historical data query request initiated by the remote control terminal, and feed it back to the remote control terminal for review analysis or report generation.

10. The system according to claim 8, characterized in that, The remote control terminal is communicatively connected to the edge computing terminal and is used to receive and display the action cycle statistics pushed in real time by the edge computing terminal after generating action cycle statistics by extracting the start and end timestamps of each action cycle. The remote control terminal is used to send configuration instructions to the edge computing terminal based on the pushed action cycle statistics results. The configuration instructions are used to guide the edge computing terminal to adjust at least one of the preset sampling frequency, preset quantile value, and validity judgment conditions.

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