A method and system for real-time acquisition of intelligent cost accounting data for marine fisheries

Abstract

The present invention relates to the technical field of fishery cost management, specifically a real-time data collection method and system for intelligent cost accounting of marine fisheries, which obtains ship operation positioning data to calculate the speed change rate, screens stable periods to reconstruct operation sections, matches fuel records to analyze flow and level to calculate fuel consumption, combines wind speed, ocean current, and tide level to analyze disturbance direction to divide interference levels, and integrates time, location, behavior type, fuel consumption, and interference level to construct index splitting entries. The present invention identifies continuous operation intervals through speed change rate, combines fuel level and flow data to cross-validate and screen effective fuel consumption, and reduces statistical deviations. Through the dynamic mapping of equipment operating parameters and energy consumption output, a refined splitting of operation behavior and energy loss is achieved, and the ability to identify abnormal working conditions is improved. Environmental parameters such as ocean current and wind speed are introduced to calculate the combined force of disturbances, quantify the actual impact of natural conditions on energy consumption, and correct cost assessment errors.

Description

Technical Field

[0001] This invention relates to the field of fishery cost management technology, and in particular to a method and system for real-time acquisition of intelligent cost accounting data for marine fisheries. Background Technology

[0002] The field of fisheries cost management technology encompasses methods and systems for the quantitative accounting and analysis of various resource inputs and outputs in the fisheries production process. Its core content focuses on the collection, recording, classification, and accounting management of data related to labor costs, fuel consumption, equipment depreciation, and material procurement in the stages of fishing, aquaculture, transportation, and processing. Fisheries cost management is data-driven, using information technology to conduct full-process cost control and economic performance analysis of various fisheries operations, forming industry-specific data models and decision-making bases to achieve the scientific identification and dynamic control of cost components in fisheries economic activities.

[0003] The intelligent cost accounting data real-time acquisition method for marine fisheries refers to a method for extracting structured data from quantifiable items involved in marine fisheries operations, such as fuel consumption, man-hours, catch volume, and auxiliary material usage, through time-stamp-based synchronous acquisition rules and data interface protocols. This method encompasses operational behavior identification based on location tracking information, matching operational hours according to task scheduling logs, calculating catch volume through weighing records and fishing net counts, quantifying fuel usage based on real-time data from ship fuel sensors, and confirming material input based on order records. It employs a distributed database for data access and aggregation, and combines operational event identifiers to achieve unified collection and classification of various cost data.

[0004] Current technologies rely on independently collected fuel and labor time data, resulting in insufficient accuracy in matching operational phases with fuel consumption. This leads to statistical confusion in high-energy-consuming scenarios such as standby and fishing. Equipment energy consumption is allocated using a fixed ratio, ignoring the dynamic impact of operational fluctuations on costs and failing to identify inefficient behaviors such as idling or overloading. Environmental disturbances rely on manual experience coefficient corrections, lacking quantitative analysis of physical parameters, making it difficult to accurately assess the impact of drag, such as wind direction and angle, on fuel efficiency. Distributed databases aggregate data by category, but lack multi-dimensional indexes for spatiotemporal and behavioral data, hindering cross-origin tracing of fishing behavior, energy consumption fluctuations, and environmental disturbances, and limiting the real-time and targeted nature of cost optimization strategies. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method and system for real-time acquisition of intelligent cost accounting data for marine fisheries. The technical solution is as follows:

[0006] A method for real-time data collection for intelligent cost accounting in marine fisheries includes the following steps:

[0007] S1: Obtain the latitude and longitude sequence of the ship operation, calculate the time interval and speed change rate of adjacent sequence points, determine the operation continuity based on the change rate and reconstruct the continuous interval segment, and generate the operation segment identification result;

[0008] S2: Call the start and end timestamps in the work section identification results, match the flow rate and liquid level of the fuel record, compare the difference between the flow rate increment change and the liquid level trend, and calculate the fuel consumption to generate a dynamic sequence record of fuel consumption.

[0009] S3: Based on the fuel consumption dynamic sequence record results, the running time of the electric winch, the frequency of trawl net tension changes, and the net deployment record during the ship operation are mapped back to the original position. The energy consumption per unit of behavior is calculated according to the fuel consumption contribution ratio, and the energy consumption collection results of the operation behavior are generated.

[0010] S4: Extract the sea surface wind speed, ocean current direction difference and tide level change within the time period of the energy consumption collection results of the operation behavior, label the corresponding operation behavior data segment, and generate environmental interference labeling results;

[0011] S5: Based on the environmental interference labeling results, sort by time and integrate timestamps, geographical locations, behavior types, total fuel consumption, and interference levels to construct a behavior identifier index and split it into entries to obtain the cost accounting data stream collection results.

[0012] As a further aspect of the present invention, the operation section identification results include the start and end timestamps of the section, the geographical coordinate sequence of the operation area, and the continuous status judgment label; the fuel consumption dynamic sequence recording results include the instantaneous increase difference of flow rate, the liquid level trend deviation value, and the segmented cumulative fuel consumption; the operation behavior energy consumption collection results include the winch operation cycle duration distribution set, the trawl tension fluctuation frequency spectrum, the net action trigger count set, and the behavior type energy consumption weight coefficient; the environmental interference labeling results include the wind speed direction offset vector angle, the ocean current angle fluctuation range, the tide level disturbance range, and the hull disturbance level classification parameters; and the cost accounting data stream collection results include the behavior type coding index, the fuel consumption time series distribution table, and the environmental interference level mapping relationship.

[0013] As a further aspect of the present invention, the steps for obtaining the work section identification result are as follows:

[0014] S101: Obtain the continuous latitude and longitude positioning sequence during ship operations, calculate the time interval and speed change value between adjacent sequence points, call the ratio of speed change value to time interval to construct the speed change rate sequence, and obtain the low speed change time period sequence.

[0015] S102: Based on the low-speed time period sequence, determine whether the interval continuity of latitude and longitude sequence points and the number of sequence points in each time period exceed the set operation continuity standard, and obtain the operation duration interval sequence.

[0016] S103: Call the sequence of intervals for the duration of the operation, and based on the speed fluctuation level, average speed, duration, and number of sequence points within the same interval, use the following formula:

[0017]

[0018] The composite stability index value of the work area is obtained through calculation. Based on the index value, the time period and sequence number are extracted to obtain the work area identification result.

[0019] in, This represents the standard deviation of the velocity values ​​within the i-th segment. T represents the average velocity of the i-th segment. i Let n represent the duration of the i-th segment. i This represents the number of sequence points contained in the i-th segment. L represents the sum of the number of points in all m segments. i Let be the composite stability index value of the i-th working section.

[0020] As a further aspect of the present invention, the steps for obtaining the fuel consumption dynamic sequence recording results are as follows:

[0021] S201: Based on the start and end timestamps of the time period corresponding to the work section identification result, match the timestamp of each record in the fuel record sequence, filter the data within the time period in which the liquid level change does not exceed the liquid level change threshold, and generate a liquid level stable record segment.

[0022] S202: Call the flow rate data and liquid level data from the liquid level stability recording segment, compare the flow rate change rate and liquid level change rate between adjacent records in the same recording sequence, calculate the degree of deviation of the difference, using the formula:

[0023]

[0024] The sequence offset fluctuation value is obtained through calculation, which measures the difference between changes in fuel flow rate and changes in liquid level trend, and generates offset detection results.

[0025] Among them, f r h represents the fuel flow rate value of the r-th record. r f represents the fuel level value of the r-th record. max h represents the maximum flow rate in the current segment. max R represents the maximum liquid level in the current segment. tΔQ represents the number of record pairs in the current fuel record sequence. s This represents the sequence offset fluctuation value;

[0026] S203: Call the record segments in the offset detection results whose offset values ​​are lower than the synchronization offset threshold, accumulate the fuel flow values ​​of all record points in the segment according to the time difference between records, establish the correspondence between the total fuel consumption per unit time of each segment and the timestamp, and obtain the fuel consumption dynamic sequence recording results.

[0027] As a further aspect of the present invention, the steps for obtaining the energy consumption data collection results of the work activity are as follows:

[0028] S301: Based on the fuel consumption dynamic sequence record results, extract the electric winch running duration, trawl net tension change frequency and net casting action record data within the corresponding time period, merge and rearrange each type of data according to the collection time order, establish the mapping relationship between each work equipment behavior and time node, and obtain the work equipment time sequence mapping result.

[0029] S302: Call the equipment operation data sequence corresponding to the behavior type in the time series mapping result of the work equipment, identify and judge the work behavior type under the same time period number, and map the behavior data in each time period to a fixed category code, using the formula:

[0030]

[0031] The relative power value of each unit behavior category is obtained through calculation, and it is associated with the corresponding behavior identifier to obtain the estimated energy consumption sequence of each unit behavior of the equipment.

[0032] Where, ε c U represents the estimated unit energy consumption for task category c. c F represents the number of time periods under behavior category c. c,u ΔD represents the equipment tension value corresponding to the time period numbered u. c,u ΔT represents the change in equipment displacement during this time period. c,u P represents the duration of that time period. c This indicates the rated power of the equipment corresponding to behavior category c;

[0033] S303: Based on the unit energy consumption value corresponding to each type of behavior in the equipment behavior unit energy consumption estimation sequence and the time length in the behavior time sequence, calculate the energy consumption contribution of each type of operation behavior, combine the calculation results into a structured sequence table, and generate the operation behavior energy consumption collection results.

[0034] As a further aspect of the present invention, the steps for obtaining the environmental interference labeling results are as follows:

[0035] S401: Obtain environmental factors within the time period of the energy consumption collection results of the operation behavior, collect sea surface wind speed direction data, ocean current direction data and tide level change height data of the corresponding sea area, calculate the angle difference between the ocean current direction and wind speed direction and the ship hull direction respectively, and obtain the disturbance angle difference sequence.

[0036] S402: Call the aforementioned disturbance angle difference sequence and the corresponding tide level change height sequence, and based on the coupling strength between the angle between the wind speed direction and the ocean current direction, and the angle between the tide level change height value and the ship's operating direction per unit time, use the following formula:

[0037]

[0038] The resultant force of the disturbance direction is calculated to obtain the influence value of the disturbance in the time period, the range of the disturbance influence amplitude is calculated, and the disturbance level is divided according to the disturbance reference level value to obtain the disturbance level identification result.

[0039] Among them, R d N represents the influence value of the resultant force in the direction of the disturbance. d θ represents the total number of time slices within that time period. w,j Let θ represent the wind direction angle in the j-th time slice. c,j θ represents the angle of the ocean current direction in the j-th time slice. s,j H represents the angle of the ship's working direction in the j-th time slice. j H represents the tidal change height at time segment j. max This indicates the maximum tidal level change height within the analyzed time period;

[0040] S403: Based on the interference level identification results, match and label the interference level corresponding to each time period with the corresponding time period in the energy consumption collection results of the operation behavior on the time axis, establish a multi-dimensional information combination field, and obtain the environmental interference labeling results.

[0041] As a further aspect of the present invention, the steps for obtaining the cost accounting data stream acquisition results are as follows:

[0042] S501: Based on the environmental interference labeling results, obtain the operation timestamp, operation geographical location, behavior type number, total fuel consumption, and interference level data within the corresponding time period, sort them by time, integrate the data item by item, and generate a behavior identifier index;

[0043] S502: Split the behavior identifier index to generate entries, obtain the cost accounting data stream for each job record, establish and save a data structure table based on the behavior identifier index and the cost accounting data stream, and obtain the cost accounting data stream acquisition results.

[0044] A real-time data acquisition system for intelligent cost accounting in marine fisheries, the system comprising:

[0045] The work section identification module obtains the ship's latitude and longitude positioning sequence, calculates the time interval between adjacent points and the rate of change of speed, filters time periods with a rate of change of speed lower than a set threshold, judges the compliance of the continuous threshold, reconstructs the start and end timestamps of the work section, and generates the work section identification result.

[0046] The fuel dynamic analysis module calls the start and end timestamps of the work section identification results, matches the fuel flow data and liquid level value, filters the liquid level change records that meet the standards, compares the difference between the flow increment and the liquid level trend, calculates the sum, and generates the fuel consumption dynamic sequence record results.

[0047] Based on the fuel consumption dynamic sequence record results, the behavior energy consumption collection module extracts the electric winch duration, trawl tension frequency, and net deployment records, maps the time axis and identifies the behavior type, calculates the ratio, and generates the operation behavior energy consumption collection results.

[0048] The environmental interference assessment module extracts the wind speed direction, ocean current difference, and tide height within the time period of the energy consumption data collection results of the operation behavior, calculates the angle between the parameters and the ship's direction, weights the resultant force value, matches the interference level, and generates environmental interference labeling results.

[0049] The cost data stream integration module calls the timestamp, location, behavior type, total fuel consumption, and level of the environmental interference labeling results, sorts and sets the field order, builds an index to generate entries, and generates cost accounting data stream collection results.

[0050] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0051] This invention identifies continuous operating intervals by the rate of change of speed and cross-validates effective fuel consumption using fuel level and flow data, reducing statistical bias. Through dynamic mapping of equipment operating parameters and energy consumption output, it achieves a refined breakdown of operational behavior and energy loss, improving the ability to identify abnormal operating conditions. Environmental parameters such as ocean currents and wind speed are introduced to calculate the combined force of disturbances, quantifying the actual impact of natural conditions on energy consumption and correcting cost assessment errors. By integrating spatiotemporal, behavioral, energy consumption, and interference tags to construct a multi-dimensional indexed data stream, it strengthens the correlation of multi-source information, supports real-time dynamic analysis and resource optimization decisions, and promotes cost accounting from experience-driven to data-intelligent driven. Attached Figure Description

[0052] Figure 1 This is a flowchart of the method of the present invention;

[0053] Figure 2 This is a flowchart illustrating the process of obtaining the work area identification results of this invention.

[0054] Figure 3This is a flowchart illustrating the process of obtaining the dynamic fuel consumption sequence recording results of this invention.

[0055] Figure 4 This is a flowchart illustrating the process of acquiring energy consumption data for operational activities according to the present invention.

[0056] Figure 5 This is a flowchart illustrating the process of obtaining environmental interference labeling results according to the present invention.

[0057] Figure 6 This is a flowchart illustrating the process of acquiring the cost accounting data stream collection results of this invention. Detailed Implementation

[0058] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0059] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0060] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0061] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0062] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0063] See also Figure 1 This invention provides a technical solution: a method for real-time acquisition of intelligent cost accounting data for marine fisheries, comprising the following steps:

[0064] S1: Obtain the continuous latitude and longitude positioning sequence during the ship operation, calculate the time interval and speed change rate of adjacent sequence points, filter the time period when the speed change rate is lower than the set change rate threshold, determine the operation continuity within the corresponding time period, reconstruct the operation continuity interval sequence based on the judgment result, and generate the operation segment identification result.

[0065] S2: Call the start and end timestamps of the corresponding time period in the work section identification results, match the flow data and fuel level in the fuel record sequence, filter the fuel record sequences whose level change does not exceed the set level change threshold, compare the difference between the flow increment change and the level trend in the same sequence, and calculate the fuel consumption of the same sequence to generate the fuel consumption dynamic sequence record results.

[0066] S3: Based on the fuel consumption dynamic sequence recording results, extract the data on the duration of electric winch operation, the frequency of tension change of trawl net device, and the net casting action record during the corresponding time period. Map the behavior parameters of each type of equipment in chronological order and label the behavior category. Calculate the energy consumption output per unit of behavior based on the fuel consumption contribution ratio corresponding to the behavior label and generate the energy consumption collection results of the operation behavior.

[0067] S4: Extract the sea surface wind speed direction value, ocean current direction difference and tide level change height within the time period of the energy consumption data collection results of the operation behavior, analyze the angle difference between the offset of each parameter and the hull operation direction, calculate the influence range of the resultant force of the disturbance direction and classify the interference level, label the interference level with the corresponding operation behavior data segment, and generate environmental interference labeling results.

[0068] S5: Based on the environmental interference labeling results, retrieve the operation timestamp, operation geographical location, behavior type number, total fuel consumption, and interference level within the corresponding time period, integrate them according to time, set the order of continuously stored fields, construct a behavior identifier index, split and generate entries based on behavior identifiers, and generate cost accounting data stream collection results.

[0069] The results of the operation section identification include the start and end timestamps of the section, the geographical coordinate sequence of the operation area, and the continuous status judgment label. The results of the fuel consumption dynamic sequence recording include the instantaneous increase difference of flow rate, the deviation value of liquid level trend, and the cumulative fuel consumption of each segment. The results of the energy consumption collection of operation behavior include the distribution set of winch operation cycle duration, the frequency spectrum of trawl tension fluctuation, the set of net action trigger times, and the energy consumption weight coefficient of behavior type. The results of the environmental interference labeling include the wind speed direction offset vector angle, the fluctuation range of ocean current angle, the tidal height disturbance range, and the classification parameters of the ship's disturbance level. The results of the cost accounting data stream collection include the behavior type coding index, the fuel consumption time series distribution table, and the environmental interference level mapping relationship.

[0070] See also Figure 2 The steps to obtain the work section identification results are as follows:

[0071] S101: Obtain the continuous latitude and longitude positioning sequence during ship operations, calculate the time interval and speed change value between adjacent sequence points, call the ratio of speed change value to time interval to construct the speed change rate sequence, and obtain the low speed change time period sequence.

[0072] For every two adjacent latitude and longitude points, calculate the time interval, which is the difference between the timestamp of the later point and the timestamp of the earlier point. Assuming the timestamp of the first point is 12:00:00 and the timestamp of the second point is 12:00:10, the time interval is 10 seconds. Next, calculate the distance between these two points using the latitude and longitude formula: d = R × arccos[sin(φ1) × sin(φ2) + cos(φ1) × cos(φ2) × cos(λ2 - λ1)], where R is the Earth's radius, approximately 6371 kilometers, and φ1, λ1 and φ2, λ2 are the latitude and longitude of the two points, respectively. Assuming the latitude and longitude of the two points are (30.0000°N, 120.0000°E) and (30.0005°N, 120.0005°E), substituting these values ​​into the formula, the distance is approximately 55.5 meters. Then, calculate the speed. The speed is equal to the distance divided by the time interval, i.e., 55.5 m / 10 s = 5.55 m / s. Next, the change in adjacent speeds is calculated, i.e., the next speed minus the previous speed. Assuming the previous speed is 5.00 m / s and the next speed is 5.55 m / s, the speed change is 0.55 m / s. Then, the rate of change of speed is calculated, which is equal to the speed change divided by the time interval, i.e., 0.55 m / s / 10 s = 0.055 m / s². The above steps are repeated to traverse the entire latitude and longitude sequence to construct a speed change rate sequence. Then, a speed change rate threshold is set, for example, 0.1 m / s², and time periods with a speed change rate lower than this threshold are selected. For example, if the speed change rate is lower than 0.1 m / s² in the time period from 12:00:00 to 12:05:00, then this time period is marked as a low-speed change time period, resulting in a low-speed change time period sequence.

[0073] S102: Based on the low-speed time period sequence, determine whether the interval continuity of latitude and longitude sequence points and the number of sequence points in each time period exceed the set operation continuity standard, and obtain the operation duration interval sequence.

[0074] The process involves determining whether the time intervals between latitude and longitude sequence points are continuous within each time period, i.e., whether the time intervals between adjacent points are within a set range. For example, the standard for time interval continuity is set to no more than 15 seconds. If the time intervals between adjacent points are all 10 seconds within a certain time period, then that time period is considered continuous. Next, the number of sequence points within that time period is counted. For example, if a certain time period contains 30 points, then the process continues by setting a standard for operation continuity. For example, if the number of sequence points within a time period is no less than 20 and the time intervals are continuous, then the time periods that meet this standard are selected. For example, if a certain time period contains 30 points and the time intervals are continuous, then that time period is considered to meet the standard for operation continuity. Finally, the sequence of operation duration intervals is obtained.

[0075] S103: Call the interval sequence of the operation duration, based on the speed fluctuation level, average speed, duration, and number of sequence points within the same interval, using the following formula:

[0076]

[0077] The composite stability index value of the work area is obtained through calculation. Based on the index value, the time period and sequence number are extracted to obtain the work area identification result.

[0078] in, This represents the standard deviation of the velocity values ​​within the i-th segment. T represents the average velocity of the i-th segment. i Let n represent the duration of the i-th segment. i This represents the number of sequence points contained in the i-th segment. L represents the sum of the number of points in all m segments. i Let be the composite stability index value of the i-th working section;

[0079] Calculate the standard deviation of the velocity within this interval. The formula for standard deviation is: Where, n i v is the number of points within the interval. ij Let the velocity be at point j. To calculate the average velocity within a given interval, assuming there are five velocity values ​​within the interval: 5.0, 5.2, 5.1, 5.3, and 5.2 m / s, calculate the average velocity: Calculate the standard deviation:

[0080]

[0081] Calculate the duration of the interval. Assuming the interval starts at 12:00:00 and ends at 12:05:00, the duration is 5 minutes, or 300 seconds. Assuming the obtained... T i = 300 seconds, n i =5, Calculate the composite stability index value and substitute it into the calculation:

[0082]

[0083] Set a stability benchmark value, such as 1.0, and determine whether the composite stability index value is greater than the benchmark value. If it is greater, extract the time period and sequence number of the interval, and finally obtain the work section identification result.

[0084] The results show that the speed fluctuation of the current work section is small, the duration is long, and the point density is moderate. The composite stability index exceeds the benchmark value, indicating that the section exhibits continuous and stable operation characteristics and can be identified as a work activity section.

[0085] See also Figure 3 The steps for obtaining the dynamic fuel consumption sequence record results are as follows:

[0086] S201: Based on the start and end timestamps of the time period corresponding to the work section identification results, match the timestamp of each record in the fuel record sequence, filter the data within the time period where the liquid level change does not exceed the liquid level change threshold, and generate a liquid level stable record segment.

[0087] Assuming a start time of 08:00:00 on April 1, 2025, and an end time of 10:00:00 on April 1, 2025, extract all records within this time range from the fuel record sequence. Assuming data is recorded once per minute during this period, a total of 120 records will be extracted. Each record contains a timestamp, flow rate data, and fuel level data. Set a fuel level change threshold, for example, ±0.5%, meaning that the fuel level change cannot exceed 0.5% within this time period. Calculate the fuel level change for each record relative to the previous record. Assuming the fuel level of the r-th record is h... r The fuel level in record r-1 is h. r-1 Then the change Δh r =h r -h r-1 If Δh r The absolute value does not exceed the set threshold, i.e., |Δh r If the value is ≤0.5%, the record is retained; otherwise, it is discarded. Through the above screening, a record segment with stable liquid level changes is obtained. Assuming that 80 records are finally selected, a record segment with stable liquid level is generated.

[0088] S202: Retrieve flow rate and level data from the stable liquid level recording segment, compare the rate of change of flow rate and the rate of change of liquid level between adjacent records in the same recording sequence, calculate the degree of deviation of the difference, using the formula:

[0089]

[0090] The sequence offset fluctuation value is obtained through calculation, which measures the difference between changes in fuel flow rate and changes in liquid level trend, and generates offset detection results.

[0091] Among them, f r h represents the fuel flow rate value of the r-th record. r f represents the fuel level value of the r-th record. maxh represents the maximum flow rate in the current segment. max R represents the maximum liquid level in the current segment. t ΔQ represents the number of record pairs in the current fuel record sequence. s This represents the sequence offset fluctuation value;

[0092] For example, assuming the recording time in the segment is once per minute, and there are 5 records extracted, each record in chronological order is as follows: Record 1: Flow rate f1 = 420 L / h, liquid level h1 = 940 mm; Record 2: Flow rate f2 = 430 L / h, liquid level h2 = 930 mm; Record 3: Flow rate f3 = 440 L / h, liquid level h3 = 920 mm; Record 4: Flow rate f4 = 455 L / h, liquid level h4 = 910 mm; Record 5: Flow rate f5 = 470 L / h, liquid level h5 = 900 mm; Extract the maximum flow rate f. max With maximum liquid level h max , where f max =470L / h, h max = 940mm, number of recording intervals R t =4, calculate the offset value for each pair of records in sequence:

[0093] For r=1:

[0094] Denominator normalization factor:

[0095] The offset of this pair of records is: 0.0107 / 1.0106≈0.0106;

[0096] For r=2:

[0097] The denominator is also 1.0106, and the offset is 0.0106;

[0098] For r=3:

[0099] The denominator is also 1.0106, and the offset is 0.0211;

[0100] For r=4:

[0101] The denominator is also 1.0106, and the offset is 0.0211;

[0102] The average of the offsets of the four pairs of records yields the sequence offset fluctuation value:

[0103]

[0104] The results show that in the current stable liquid level recording segment, the average deviation between the fuel flow rate change and the fuel level trend change between adjacent time points is 0.01585. If the subsequent synchronization offset threshold is set to 0.02, the data segment meets the synchronization requirements and can be used for subsequent fuel consumption analysis.

[0105] S203: Call the record segment whose offset value is lower than the synchronization offset threshold in the offset detection result, accumulate the fuel flow value of all record points in the segment according to the time difference between records, establish the correspondence between the total fuel consumption per unit time of each segment and the timestamp, and obtain the fuel consumption dynamic sequence recording result.

[0106] Assuming the synchronization offset threshold is set to 0.02, that is, when the sequence offset fluctuation value ΔQ... s When the value is less than 0.02, the synchronicity between the flow rate change and the liquid level change is considered to meet the requirements. This assumes that the ΔQ obtained in the aforementioned calculation... s The value is 0.015, which is less than the set threshold. Therefore, the record segment is considered valid. For all record points within that segment, fuel consumption is calculated based on the time difference between records. Assuming the record interval is 1 minute (i.e., the time difference is 60 seconds), the fuel flow rate for the r-th record is f. r If the unit is L / h, then the fuel consumption during this time interval is (f r / 3600)·60=f r / 60L, assuming f r If the fuel consumption is 450 L / h, then the fuel consumption within this time interval is 450 / 60 = 7.5 L. This is calculated for all records within the segment, obtaining the fuel consumption for each time interval. These fuel consumptions are then summed to obtain the total fuel consumption for the segment. Assuming there are 80 records in the segment, the total fuel consumption is... Record the start timestamp of each time interval to establish a dynamic sequence record of fuel consumption.

[0107] Please see Figure 4 The steps for obtaining the energy consumption data collection results of the work activity are as follows:

[0108] S301: Based on the fuel consumption dynamic sequence record results, extract the electric winch operation duration, trawl tension change frequency and net casting action record data within the corresponding time period. Merge and rearrange each type of data according to the collection time order, establish the mapping relationship between each operation equipment behavior and time node, and obtain the operation equipment time sequence mapping result.

[0109] For example, during the period from 08:00 to 10:00 on April 1, 2025, the vessel operation involved three types of equipment: electric winches, trawl nets, and net casting equipment. The operating status data of the electric winches was collected in real time by power meters and start / stop switch status sensors, sampled once per second, forming a binary start / stop sequence and a power sequence with corresponding timestamps. The tension change frequency data of the trawl nets was collected by tension sensors, and the number of tension peak occurrences was counted every 30 seconds using a sliding window method to form a tension change frequency sequence. The net casting action record data was recorded by the action status sensors of the casting machinery, recording the net casting and retrieval actions in the form of timestamps. Each record included the action type and the time of occurrence. These three types of data... According to the order of their respective timestamps, a unified time axis is constructed, and a mapping matrix is ​​built with a resolution of 1 minute. The presence, magnitude and trend of each device behavior status within each minute are corresponding to the presence, magnitude and trend of each behavior status within each minute. A behavior identification coding system is set up. For example, the operation of the electric winch is identified as 1, the tension fluctuation of the trawl net is identified as 2, and the net deployment is identified as 3. The behavior status of each minute is mapped to a coding sequence. If the electric winch is running and the tension fluctuation frequency is higher than 2 times / minute within the minute of 08:03, it is mapped as [1,2]. If only the net deployment action occurs, it is mapped as [3]. After the unified mapping is completed, the system establishes a behavior sequence matrix according to the time sequence to form a complete device behavior time sequence mapping result.

[0110] S302: Retrieve the equipment operation data sequence corresponding to the behavior type in the time series mapping result of the work equipment, identify and judge the work behavior type under the same time period number, and map the behavior data within each time period to a fixed category code, using the formula:

[0111]

[0112] The relative power value of each unit behavior category is obtained through calculation, and it is associated with the corresponding behavior identifier to obtain the estimated energy consumption sequence of each unit behavior of the equipment.

[0113] Where, ε c U represents the estimated unit energy consumption for task category c. c F represents the number of time periods under behavior category c. c,u ΔD represents the equipment tension value corresponding to the time period numbered u. c,u ΔT represents the change in equipment displacement during this time period. c,u P represents the duration of that time period. c This indicates the rated power of the equipment corresponding to behavior category c;

[0114] For example, if the tension data recorded within a certain minute is [510, 495, 480, 499] N, its arithmetic mean F is 496 N. The displacement change value is recorded by the displacement sensor installed on the ship, which records the actual traction distance of the equipment. If the winch tractions 3.5 meters within that minute, then ΔD = 3.5 m. The recording time period is 60 seconds. The rated power P of the equipment is provided by the factory calibration, such as 1800W for an electric winch and 150W for a trawl net device. 0W, assuming the behavior category contains 4 time periods: Segment 1: F = 496N, ΔD = 3.5m, ΔT = 60s, P = 1800W; Segment 2: F = 515N, ΔD = 3.0m, ΔT = 60s, P = 1800W; Segment 3: F = 500N, ΔD = 4.2m, ΔT = 60s, P = 1800W; Segment 4: F = 490N, ΔD = 3.8m, ΔT = 60s, P = 1800W;

[0115] Substituting into the formula, multi-level calculations are performed as follows:

[0116] Section 1:

[0117] Section 2:

[0118] Section 3:

[0119] Section 4:

[0120]

[0121] The results show that the energy consumption output of this behavior category under each unit of rated power is a dimensionless relative value of 0.01677, that is, the equipment completes the unit behavior operation with an average of 1.68% of the rated power. Finally, the estimated energy consumption sequence of equipment behavior is obtained.

[0122] S303: Based on the unit energy consumption value corresponding to each type of behavior in the equipment behavior unit energy consumption estimation sequence and the time length in the behavior time sequence, calculate the energy consumption contribution of each type of operation behavior, combine the calculation results into a structured sequence table, and generate the operation behavior energy consumption collection results.

[0123] For example, if an electric winch's operation lasts for 10 minutes, trawl tension fluctuations last for 15 minutes, and net deployment lasts for 8 minutes, the corresponding unit energy consumption values ​​are 0.01677, 0.01245, and 0.00780, respectively, and the rated power of the equipment is 1800W, 1500W, and 800W, respectively, then the total energy consumption for each operation can be calculated using the formula: E = ε. c ×P c ×T cThe energy consumption of the electric winch is calculated as follows: E = 0.01677 × 1800 × 600 = 18082.8 J, where 600 is the number of seconds corresponding to 10 minutes. The energy consumption of the trawl device is 0.01245 × 1500 × 900 = 16706.25 J, and the energy consumption of the net casting is 0.00780 × 800 × 480 = 2995.2 J. The total energy consumption of each type of behavior is organized into a structured table, which includes five items: behavior category, unit energy consumption value, duration, rated power, and total energy consumption. The final energy consumption data collection results for the operation behavior are established.

[0124] See also Figure 5 The steps for obtaining the environmental interference labeling results are as follows:

[0125] S401: Obtain environmental factors within the time period of the energy consumption data collection results of the operation, collect sea surface wind speed direction data, ocean current direction data and tide level change height data of the corresponding sea area, calculate the angle difference between the ocean current direction and wind speed direction and the ship hull direction respectively, and obtain the disturbance angle difference sequence.

[0126] Assuming the recorded wind direction is 45° east of north, then assign a value of 45°. The ocean current direction is recorded by a Doppler current meter installed on the bottom of the ship, and the direction vector is updated every minute, also in degrees. If the current current direction points to 120° in the current sampling period, then set φ. vc =120°, the tidal level change height is obtained by sampling the water level every minute using a tide gauge, and the difference between the water level in each minute and the previous minute is taken as the tidal level change, in meters. For example, if the tide level in the previous minute was 2.3m and it is 2.5m in the current minute, then Δh = 0.2m. Simultaneously, the ship's operating direction data is acquired. This data is provided by the heading angle recorded by the ship's inertial navigation system or GPS heading instrument, with an accuracy of 0.1° and a sampling period of 1 second. The average value over a 60-second time period is taken as the ship's operating direction value for that minute. For example, if the average heading angle over this time period is 105°, then let φ vs =105°, synchronize the time of data within each minute, construct sampling intervals in minutes, and each interval contains four sets of parameters: wind direction angle φ vw φ of ocean current direction vc , hull heading angle φ vs , Tide level change height h td The data are arranged and recorded sequentially according to time to construct a data sequence for disturbance impact analysis, and finally the disturbance angle difference sequence is obtained.

[0127] S402: Call the disturbance angle difference sequence and the corresponding tide level change height sequence, and use the following formula based on the coupling strength between the angle between the wind speed direction and the ocean current direction, and the angle between the tide level change height value and the ship's operating direction per unit time:

[0128]

[0129] The resultant force of the disturbance direction is calculated to obtain the influence value of the disturbance in the time period, the range of the disturbance influence amplitude is calculated, and the disturbance level is divided according to the disturbance reference level value to obtain the disturbance level identification result.

[0130] Among them, R d N represents the influence value of the resultant force in the direction of the disturbance. d θ represents the total number of time slices within that time period. w,j Let θ represent the wind direction angle in the j-th time slice. c,j θ represents the angle of the ocean current direction in the j-th time slice. s,j H represents the angle of the ship's working direction in the j-th time slice. j H represents the tidal change height at time segment j. max This indicates the maximum tidal level change height within the analyzed time period;

[0131] Numerical calculations were performed on the angles between the wind direction, current direction, and ship orientation. The wind direction angle was calculated as follows: α w =|φ vw -φ vs |, the angle between the flow directions is: α c =|φ vc -φ vs Then, substitute both into the expression for the angle influence factor and calculate |cos(α) respectively. w | and |cos(α) c The value of α is [0,1]. When the included angle is 0°, it indicates a completely aligned direction, and the cosine value is 1. When the included angle is 90°, the cosine value is 0, indicating a vertical influence. In a practical example, if the wind direction is 75° and the ship's heading is 105° within a certain minute, then α... w =30°, corresponding to a cosine value of approximately 0.866, with the ocean current direction at 140°, α c =35°, cosine value is approximately 0.819; tidal level change value is taken from the collected value h during this time period. td,e And compare it with the maximum tidal level variation h within the analysis period. td,max Normalization is performed; for example, if the maximum tidal level change is 0.9m and the current minute change is 0.2m, then the normalized value is 0.222. Additionally, the angle difference α between the wind and the current is calculated. wf =|φ vw -φ vc The normalization process is performed, and the result is divided by 180 to obtain the directional dispersion term, for example, |75-140| / 180=0.361;

[0132] Taking 5 minutes of data as an example, the changes in wind direction, current direction, ship direction, and tide level for each minute are set as follows: Minute 1: 75°, 140°, 105°, 0.2m; Minute 2: 80°, 130°, 100°, 0.4m; Minute 3: 95°, 110°, 105°, 0.3m; Minute 4: 60°, 150°, 100°, 0.5m; Minute 5: 70°, 120°, 90°, 0.25m.

[0133] The maximum tidal level variation was set to 0.9m. After substituting the values ​​into the formula and calculating each item, the average value was summed to obtain the resultant force influence value R in the direction of the disturbance. e =1.504. Compare this value with the interference level benchmark value. Let the benchmark interval be divided as follows: 0-0.8 is Level I interference, 0.8-1.2 is Level II interference, 1.2-1.6 is Level III interference, and above 1.6 is Level IV interference. Then the current interval is 1.504, which is classified as Level III. The interference level identification result is finally obtained.

[0134] S403: Based on the interference level identification results, match and label the interference level corresponding to each time period with the corresponding time period in the energy consumption collection results of the operation behavior on the time axis, establish a multi-dimensional information combination field, and obtain the environmental interference labeling results;

[0135] If a behavior segment spans 3 minutes, the interference level within each of those 3 minutes is obtained, and the highest interference level is taken as the corresponding level for that behavior segment. For example, if a behavior segment is 08:01-08:03, and the corresponding interference levels are II, II, and III, then the value is assigned to III. This interference level is added as a field to the behavior record, and the behavior identifier, energy consumption value, and interference level are generated simultaneously as three parallel records. Finally, they are integrated into an energy consumption data structure table with an interference level field to establish the environmental interference labeling results.

[0136] See also Figure 6 The steps for obtaining the cost accounting data stream collection results are as follows:

[0137] S501: Based on the environmental interference labeling results, obtain the operation timestamp, operation geographical location, behavior type number, total fuel consumption, and interference level data within the corresponding time period, sort them by time, integrate the data item by item, and generate a behavior identifier index;

[0138] Assuming the extraction time range is from 00:00 on October 1, 2024 to 23:59 on October 7, 2024, all job records are extracted. Timestamps are in the format of "year-month-day hour:minute:second" for exact matching. The corresponding records need to have the following fields read: "Job Start Time," "Job End Time," "Job Latitude and Longitude Coordinates," "Behavior Type Number," "Fuel Consumption (Unit: L)," and "Interference Level (Level 1-5)." The data fields are imported into the data cleaning module, and field integrity checks are performed. Missing values ​​are handled using nearest-neighbor interpolation. All unit values ​​are uniformly converted to the International System of Units (SI), such as converting latitude and longitude to decimals and retaining two decimal places for fuel consumption. All job records are sorted by timestamp field, arranged from earliest to latest according to "Job Start Time." The system then checks for duplicate coordinates, duplicate behavior types, or abnormal fuel fluctuations between adjacent records (if the difference in fuel consumption between two adjacent records exceeds a certain threshold...). Records exceeding 50L are marked as abnormal for screening and filtering. The cleaned data is then used to generate a unique job behavior identifier, named using the format "behavior number + timestamp + coordinate ID" to create a unique index ID. For example, "A03_202410021215_115", where "A03" is the behavior number, "202410021215" is the timestamp format (October 2, 2024, 12:15), and "115" is the coordinate hash. The identifier is then used as the primary key and written into the behavior identifier index table for subsequent field matching operations. For example, if the behavior type number of the record at 12:15 on October 2, 2024 is A03, the corresponding geographical coordinates are 121.48° East longitude and 31.22° North latitude, the fuel consumption is 38.5L, and the interference level is 3, then the generated identifier is "A03_202410021215_115", which is marked as a valid behavior entry.

[0139] S502: Split the behavior identifier index to generate entries, obtain the cost accounting data stream for each work record, establish and save the data structure table based on the behavior identifier index and the cost accounting data stream, and obtain the cost accounting data stream acquisition results;

[0140] The original work record file is retrieved based on the three fields "behavior ID + timestamp + coordinate ID". The original data values ​​for the corresponding record are extracted. These fields include the work time period (e.g., 12:15–12:35), coordinates (e.g., 121.48°E, 31.22°N), behavior type ID (e.g., A03), fuel consumption (e.g., 38.5L), and interference level (e.g., 3). This record is then input into the cost data processing module. The module calls the work duration field (work end time minus work start time, e.g., 20 minutes), fuel consumption field (e.g., 38.5L), and interference level (level 3, quantified as a coefficient of 1.3 according to rules) to read and combine each item, generating the cost accounting data stream for this entry. The data stream structure is organized in JSON format, such as: {"behavior ID": "A03_20241002 The data is structured as follows: `1215_115”, "Time Period": 20, "Fuel Consumption": 38.5, "Interference Coefficient": 1.3`. Then, the same operation is performed on all behavior items to generate multiple cost accounting data streams in batches. These data streams are written into independent tables, and a field mapping structure is established by using the behavior ID as the primary key and associating it with the aforementioned behavior identifier index table. The fields "Behavior ID", "Operation Time Period", "Coordinate Number", "Fuel Consumption", and "Interference Level Quantification Value" are listed as structured fields and organized into a data structure table. For example: Behavior ID = A03_202410021215_115, Time Period = 20 minutes, Coordinate Number = 115, Fuel Consumption = 38.5L, Interference Level Coefficient = 1.3. After the structure table is completed, it is saved as a standard CSV file and uploaded to the database to obtain the cost accounting data stream collection results.

[0141] A real-time data acquisition system for intelligent cost accounting in marine fisheries, comprising:

[0142] The work section identification module obtains the ship's latitude and longitude positioning sequence, calculates the time interval between adjacent points and the rate of change of speed, filters time periods with a rate of change of speed lower than a set threshold, judges the compliance of the continuous threshold, reconstructs the start and end timestamps of the work section, and generates the work section identification result.

[0143] The fuel dynamic analysis module calls the start and end timestamps of the work section identification results, matches the fuel flow data and liquid level value, filters the liquid level change records that meet the standards, compares the difference between the flow increment and the liquid level trend, calculates the sum, and generates the fuel consumption dynamic sequence record results.

[0144] The behavior energy consumption collection module extracts electric winch duration, trawl tension frequency, and net deployment records based on the dynamic sequence record of fuel consumption. It maps the time axis and identifies the behavior type, calculates the ratio, and generates the operation behavior energy consumption collection results.

[0145] The environmental interference assessment module extracts wind speed direction, ocean current difference, and tide height within the time period of the energy consumption data collection results of the operation behavior, calculates the angle between the parameters and the ship's direction, calculates the resultant force value by weighting, matches the interference level, and generates environmental interference labeling results.

[0146] The cost data stream integration module calls the timestamp, location, behavior type, total fuel consumption, and level of the environmental interference labeling results, sorts and sets the field order, builds an index to generate entries, and generates the cost accounting data stream collection results.

[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for real-time data acquisition for intelligent cost accounting in marine fisheries, characterized in that, Includes the following steps: S1: Obtain the latitude and longitude sequence of the ship operation, calculate the time interval and speed change rate of adjacent sequence points, determine the operation continuity based on the change rate and reconstruct the continuous interval segment, and generate the operation segment identification result; S2: Call the start and end timestamps in the work section identification results, match the flow rate and liquid level of the fuel record, compare the difference between the flow rate increment change and the liquid level trend, and calculate the fuel consumption to generate a dynamic sequence record of fuel consumption. S3: Based on the fuel consumption dynamic sequence record results, the running time of the electric winch, the frequency of trawl net tension changes, and the net deployment record during the ship operation are mapped back to the original position. The energy consumption per unit of behavior is calculated according to the fuel consumption contribution ratio, and the energy consumption collection results of the operation behavior are generated. S4: Extract the sea surface wind speed, ocean current direction difference and tide level change within the time period of the energy consumption collection results of the operation behavior, label the corresponding operation behavior data segment, and generate environmental interference labeling results; The steps for obtaining environmental interference labeling results are as follows: S401: Obtain environmental factors within the time period of the energy consumption collection results of the operation behavior, collect sea surface wind speed direction data, ocean current direction data and tide level change height data of the corresponding sea area, calculate the angle difference between the ocean current direction and wind speed direction and the ship hull direction respectively, and obtain the disturbance angle difference sequence. S402: Call the aforementioned disturbance angle difference sequence and the corresponding tide level change height sequence, and based on the coupling strength between the angle between the wind speed direction and the ocean current direction, and the angle between the tide level change height value and the ship's operating direction per unit time, use the following formula: ； The resultant force of the disturbance direction is calculated to obtain the influence value of the disturbance in the time period, the range of the disturbance influence amplitude is calculated, and the disturbance level is divided according to the disturbance reference level value to obtain the disturbance level identification result. in, This indicates the influence value of the resultant force in the direction of the disturbance. This indicates the total number of time slices within that time period. Indicates the first Wind direction and angle for each time frame Indicates the first The direction and angle of the ocean current in each time frame. Indicates the first The angle of the ship's working direction in each time frame. Indicates the first The height of the tidal range at different times. This indicates the maximum tidal level change height within the analyzed time period; S403: Based on the interference level identification results, match and label the interference level corresponding to each time period with the corresponding time period in the energy consumption collection results of the operation behavior on the time axis, establish a multi-dimensional information combination field, and obtain the environmental interference labeling results; S5: Based on the environmental interference labeling results, sort by time and integrate timestamps, geographical locations, behavior types, total fuel consumption, and interference levels to construct a behavior identifier index and split it into entries to obtain the cost accounting data stream collection results.

2. The real-time data acquisition method for intelligent cost accounting of marine fisheries according to claim 1, characterized in that: The operation section identification results include the start and end timestamps of the section, the geographical coordinate sequence of the operation area, and the continuous status judgment label. The fuel consumption dynamic sequence recording results include the instantaneous increase difference of flow rate, the liquid level trend deviation value, and the segmented cumulative fuel consumption. The operation behavior energy consumption collection results include the winch operation cycle duration distribution set, the trawl tension fluctuation frequency spectrum, the net action trigger number set, and the behavior type energy consumption weight coefficient. The environmental interference labeling results include the wind speed direction offset vector angle, the ocean current angle fluctuation range, the tide level disturbance range, and the hull disturbance level classification parameters. The cost accounting data stream collection results include the behavior type coding index, the fuel consumption time series distribution table, and the environmental interference level mapping relationship.

3. The real-time data acquisition method for intelligent cost accounting of marine fisheries according to claim 1, characterized in that: The steps to obtain the work section identification results are as follows: S101: Obtain the continuous latitude and longitude positioning sequence during ship operations, calculate the time interval and speed change value between adjacent sequence points, call the ratio of speed change value to time interval to construct the speed change rate sequence, and obtain the low speed change time period sequence. S102: Based on the low-speed time period sequence, determine whether the interval continuity of latitude and longitude sequence points and the number of sequence points in each time period exceed the set operation continuity standard, and obtain the operation duration interval sequence. S103: Call the sequence of intervals for the duration of the operation, and based on the speed fluctuation level, average speed, duration, and number of sequence points within the same interval, use the following formula: ； The composite stability index value of the work area is obtained through calculation. Based on the index value, the time period and sequence number are extracted to obtain the work area identification result. in, Indicates the first The standard deviation of the velocity values ​​within each segment Indicates the first The average speed of each segment Indicates the first The duration of each segment, Indicates the first The number of sequence points contained in each segment. Indicates all The sum of the points in each segment For the first The composite stability index value of each working section.

4. The real-time data acquisition method for intelligent cost accounting of marine fisheries according to claim 1, characterized in that: The steps for obtaining the dynamic fuel consumption sequence recording results are as follows: S201: Based on the start and end timestamps of the time period corresponding to the work section identification result, match the timestamp of each record in the fuel record sequence, filter the data within the time period in which the liquid level change does not exceed the liquid level change threshold, and generate a liquid level stable record segment. S202: Call the flow rate data and liquid level data from the liquid level stability recording segment, compare the flow rate change rate and liquid level change rate between adjacent records in the same recording sequence, calculate the degree of deviation of the difference, using the formula: ； The sequence offset fluctuation value is obtained through calculation, which measures the difference between changes in fuel flow rate and changes in liquid level trend, and generates offset detection results. in, Indicates the first The fuel flow rate value of each record. Indicates the first The fuel level value recorded in the record. This indicates the maximum flow rate in the current segment. This indicates the maximum liquid level in the current segment. This indicates the number of record pairs in the current fuel record sequence. This represents the sequence offset fluctuation value; S203: Call the record segments in the offset detection results whose offset values ​​are lower than the synchronization offset threshold, accumulate the fuel flow values ​​of all record points in the segment according to the time difference between records, establish the correspondence between the total fuel consumption per unit time of each segment and the timestamp, and obtain the fuel consumption dynamic sequence recording results.

5. The method for real-time acquisition of intelligent cost accounting data for marine fisheries according to claim 1, characterized in that: The steps for obtaining energy consumption data collection results for work activities are as follows: S301: Based on the fuel consumption dynamic sequence record results, extract the electric winch running duration, trawl net tension change frequency and net casting action record data within the corresponding time period, merge and rearrange each type of data according to the collection time order, establish the mapping relationship between each work equipment behavior and time node, and obtain the work equipment time sequence mapping result. S302: Call the equipment operation data sequence corresponding to the behavior type in the time series mapping result of the work equipment, identify and judge the work behavior type under the same time period number, and map the behavior data in each time period to a fixed category code, using the formula: ； The relative power value of each unit behavior category is obtained through calculation, and it is associated with the corresponding behavior identifier to obtain the estimated energy consumption sequence of each unit behavior of the equipment. in, Indicates the category of work behavior Estimated unit energy consumption For behavior categories The number of time periods below Indicates the number The corresponding equipment tension value during the time period. This indicates the amount of equipment displacement change during that time period. Indicates the duration of that time period. Indicates behavior category The rated power of the corresponding equipment; S303: Based on the unit energy consumption value corresponding to each type of behavior in the equipment behavior unit energy consumption estimation sequence and the time length in the behavior time sequence, calculate the energy consumption contribution of each type of operation behavior, combine the calculation results into a structured sequence table, and generate the operation behavior energy consumption collection results.

6. The method for real-time acquisition of intelligent cost accounting data for marine fisheries according to claim 1, characterized in that: The steps for obtaining the cost accounting data stream collection results are as follows: S501: Based on the environmental interference labeling results, obtain the operation timestamp, operation geographical location, behavior type number, total fuel consumption, and interference level data within the corresponding time period, sort them by time, integrate the data item by item, and generate a behavior identifier index; S502: Split the behavior identifier index to generate entries, obtain the cost accounting data stream for each job record, establish and save a data structure table based on the behavior identifier index and the cost accounting data stream, and obtain the cost accounting data stream acquisition results.

7. A real-time data acquisition system for intelligent cost accounting in marine fisheries, characterized in that, The system comprises: Real-time data acquisition method for intelligent cost accounting of marine fisheries according to any one of claims 1-6; The work section identification module obtains the ship's latitude and longitude positioning sequence, calculates the time interval between adjacent points and the rate of change of speed, filters time periods with a rate of change of speed lower than a set threshold, judges the compliance of the continuous threshold, reconstructs the start and end timestamps of the work section, and generates the work section identification result. The fuel dynamic analysis module calls the start and end timestamps of the work section identification results, matches the fuel flow data and liquid level value, filters the liquid level change records that meet the standards, compares the difference between the flow increment and the liquid level trend, calculates the sum, and generates the fuel consumption dynamic sequence record results. Based on the fuel consumption dynamic sequence record results, the behavior energy consumption collection module extracts the electric winch duration, trawl tension frequency, and net deployment records, maps the time axis and identifies the behavior type, calculates the ratio, and generates the operation behavior energy consumption collection results. The environmental interference assessment module extracts the wind speed direction, ocean current difference, and tide height within the time period of the energy consumption data collection results of the operation behavior, calculates the angle between the parameters and the ship's direction, weights the resultant force value, matches the interference level, and generates environmental interference labeling results. The cost data stream integration module calls the timestamp, location, behavior type, total fuel consumption, and level of the environmental interference labeling results, sorts and sets the field order, builds an index to generate entries, and generates cost accounting data stream collection results.

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