Marine fishery intelligent cost accounting data real-time acquisition method and system

Abstract

The invention relates to the technical field of fishery cost management, in particular to a marine fishery intelligent cost accounting data real-time acquisition method and system, and the method comprises the steps: obtaining ship operation positioning data, calculating a speed change rate, screening a stable time period, reconstructing an operation section, and matching a fuel record to analyze flow and liquid level statistics oil consumption. The disturbance direction is analyzed by combining wind speed, ocean current and tide level to divide interference grades, and index split entries are constructed by integrating time, position, behavior type, oil consumption and interference grades. According to the method, the continuous operation interval is recognized through the speed change rate, effective fuel consumption is screened by combining fuel liquid level and flow data cross validation, and the statistical deviation is reduced. Through dynamic mapping of equipment operation parameters and energy consumption output, fine splitting of operation behaviors and energy loss is realized, and the abnormal working condition identification capability is improved. Environmental parameters such as ocean current and wind speed are introduced to calculate disturbance resultant force, actual influences of natural conditions on energy consumption are quantified, and cost evaluation errors are corrected.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery cost management, and in particular to a method and system for real-time collection of intelligent cost accounting data for marine fisheries. Background Art

[0002] The field of fishery cost management technology includes relevant methods and systems for quantitative accounting and analysis of various resource inputs and outputs in the fishery production process. The core content mainly focuses on the collection, recording, classification and accounting management of data such as labor costs, fuel consumption, equipment depreciation, and material procurement in the fishing, breeding, transportation, and processing links. Fishery cost management is based on data-driven, and uses information technology to conduct full-process cost control and economic performance analysis of various fishery business activities, forming industry-specific data models and decision-making bases, and realizing scientific identification and dynamic control of cost structures in fishery economic activities.

[0003] Among them, the real-time data collection method for intelligent cost accounting of marine fisheries refers to the method of extracting structured data through synchronous collection rules based on timestamps and data interface protocols for quantifiable items such as fuel consumption, man-hours, catch, and auxiliary material use involved in the process of marine fishery operations. It covers the identification of operation behaviors based on location tracking information, matching operation hours based on task scheduling logs, counting the number of catches through weighing records combined with fishing nets, quantifying fuel use based on real-time feedback from ship fuel sensors, and confirming material input based on order records. It uses a distributed database for data access and aggregation, and combines operation event identification codes to achieve unified collection and classification of various cost data.

[0004] Existing technologies rely on the independent collection of fuel and working hours data, and the matching accuracy of the operation stage and fuel consumption is insufficient, resulting in statistical confusion of high-energy consumption scenarios such as standby and fishing. Equipment energy consumption is allocated at a fixed ratio, ignoring the dynamic impact of operating status fluctuations on costs, and unable to identify inefficient behaviors such as idling or overload. Environmental interference relies on manual experience coefficient correction, lacks quantitative analysis of physical parameters, and is difficult to accurately assess the loss of fuel efficiency caused by resistance such as wind direction angle. Distributed databases collect data by category, and do not establish a multi-dimensional index of time, space and behavior, which restricts the cross-tracing of fishing behavior, energy consumption fluctuations and environmental interference, and limits the real-time and targeted nature of cost optimization strategies. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a method and system for real-time collection of intelligent cost accounting data for marine fisheries. The technical solution is as follows:

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

[0007] S1: Obtain the longitude and latitude sequence of the ship operation, calculate the time interval and speed change rate between adjacent sequence points, judge the operation persistence according to the change rate and reconstruct the continuous interval segment, and generate the operation section identification result;

[0008] S2: Call the start and end timestamps in the operation section identification result, match the flow rate and liquid level of the fuel record, compare the flow rate increment change and the liquid level trend difference, and count the fuel consumption to generate the fuel consumption dynamic sequence record result;

[0009] S3: Based on the fuel consumption dynamic sequence record result, map the running duration of the electric winch, the change frequency of the trawl tension, and the net deployment record during the ship operation, calculate the unit energy consumption of the behavior according to the fuel consumption contribution ratio, and generate the operation behavior energy consumption acquisition result;

[0010] S4: Extract the sea surface wind speed, sea current direction difference and tide level change during the time period of the operation behavior energy consumption acquisition result, label the corresponding operation behavior data segment, and generate the environmental interference label annotation result;

[0011] S5: According to the environmental interference label annotation result, sort by time and integrate the timestamp, geographical location, behavior type, total fuel consumption, and interference level, construct a behavior identification index and split it into entries to obtain the cost accounting data stream acquisition result.

[0012] As a further solution of the present invention, the operation section identification result includes the interval start and end timestamps, the geographical coordinate sequence of the operation area, and the persistence status determination label. The fuel consumption dynamic sequence record result includes the instantaneous flow rate increment difference, the liquid level trend deviation value, and the segmented cumulative fuel consumption. The operation behavior energy consumption acquisition result includes the winch operation cycle duration distribution set, the trawl tension fluctuation frequency spectrum, the net action trigger times set, and the behavior type energy consumption weight coefficient. The environmental interference label annotation result includes the wind speed direction offset vector angle, the sea current included angle fluctuation range, the tide level height disturbance interval, and the ship body disturbance level classification parameter. The cost accounting data stream acquisition result includes the behavior type coding index, the fuel consumption time series distribution table, and the environmental interference level mapping relationship.

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

[0014] S101: Obtain the continuous longitude and latitude positioning sequence during the ship operation, calculate the time interval and speed change value between adjacent sequence points, call the ratio of the speed change value to the time interval to construct a 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 time continuity of the longitude and latitude sequence points and the number of sequence points in each time period exceed the set operation persistence standard, and obtain the operation continuous time period interval sequence;

[0016] S103: Call the operation continuous time period interval sequence, and according to the speed fluctuation level, average speed, duration and number of sequence points within the same interval, use the formula:

[0017]

[0018] Calculate to obtain the composite stability index value of the operation interval, extract the time period and sequence number according to the index value, and obtain the operation section identification result;

[0019] Among them, represents the standard deviation of the speed values in the i-th section, represents the average speed of the i-th section, T i represents the duration of the i-th section, n i represents the number of sequence points included in the i-th section, represents the total number of points under all m sections, L i is the composite stability index value of the i-th operation section.

[0020] As a further solution of the present invention, the steps for obtaining the fuel consumption dynamic sequence record result are:

[0021] S201: Based on the start and end time stamps of the time periods corresponding to the operation section identification result, match the time stamps of each record in the fuel record sequence, filter the data within the time period where the liquid level change amount does not exceed the liquid level change amount threshold, and generate a liquid level stable record segment;

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

[0023]

[0024] Calculate to obtain the sequence offset fluctuation value, measure the difference intensity between the fuel flow rate change and the liquid level trend change, and generate an offset detection result;

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

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

[0027] As a further solution of the present invention, the steps for obtaining the acquisition result of the operation behavior energy consumption are as follows:

[0028] S301: Based on the fuel consumption dynamic sequence record result, extract the running duration of the electric winch, the tension change frequency of the trawl device, and the net deployment action record data within the corresponding time period, respectively rearrange and reorganize each type of data in the order of collection time, and establish the mapping relationship between each operation equipment behavior and the time node to obtain the operation equipment time series mapping result;

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

[0030]

[0031] Calculate the relative power value of the unit behavior category through operation, establish an association with the corresponding behavior identifier, and obtain the equipment behavior unit energy consumption estimation sequence;

[0032] Among them, ε c represents the unit energy consumption estimation of the operation behavior category c, U c is the number of time periods under the behavior category c, F c,u represents the corresponding equipment pulling force value in the time period numbered u, ΔD c,u represents the equipment displacement change amount within this time period, ΔT c,u represents the duration of this time period, P c represents the rated power of the equipment corresponding to the behavior category c;

[0033] S303: According to 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 series, calculate the energy consumption contribution amount of each type of operation behavior, combine the calculation results into a structured sequence table, and generate the acquisition result of the operation behavior energy consumption.

[0034] As a further solution of the present invention, the steps for obtaining the environmental interference label annotation result are as follows:

[0035] S401: Obtain the environmental factors within the time period when the energy consumption collection result of the operation behavior is located, collect the sea surface wind speed direction data, sea current direction data, and tidal level change height data of the corresponding sea area, calculate the included angle differences between the sea current direction and the wind speed direction and the hull direction respectively, and obtain a disturbance included angle difference sequence;

[0036] S402: Call the disturbance included angle difference sequence and the corresponding tidal level change height sequence, and according to the coupling strength between the included angle value of the wind speed direction and the sea current direction, the tidal level change height value, and the included angle value of the hull operation direction per unit time, use the formula:

[0037]

[0038] Perform operations to obtain the influence value of the resultant force of the disturbance direction in this time period, calculate the disturbance influence amplitude range, and divide the disturbance level according to the interference reference level value to obtain the disturbance level identification result;

[0039] Among them, R d represents the influence value of the resultant force of the disturbance direction, N d represents the total number of time slices in this time period, θ w,j represents the wind direction angle of the j-th time slice, θ c,j represents the sea current direction angle of the j-th time slice, θ s,j represents the hull operation direction angle of the j-th time slice, H j represents the tidal level change height of the j-th time slice, H max represents the maximum tidal level change height within the analysis time period;

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

[0041] As a further solution of the present invention, the steps for obtaining the cost accounting data stream collection result are:

[0042] S501: Based on the environmental disturbance label annotation result, obtain the operation timestamp, operation geographical location, behavior type number, total fuel consumption, and disturbance level data within the corresponding time period, sort by time and integrate each item of data to generate a behavior identification index;

[0043] S502: Split the behavior identification index into items, obtain the cost accounting data stream of each operation record, establish a data structure table according to the behavior identification index and the cost accounting data stream, and save it to obtain the cost accounting data stream collection result.

[0044] An intelligent real-time cost accounting data collection system for marine fishery, the system includes:

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

[0046] The fuel dynamic analysis module calls the start and end timestamps of the operation section identification result, matches the fuel flow data with the liquid level value, filters out the record segments where the liquid level change amount meets the standard, compares the flow increment with the difference in liquid level trend, calculates the total sum, and generates the fuel consumption dynamic sequence record result;

[0047] The behavior energy consumption aggregation module extracts the electric winch duration, trawl tension frequency, and net deployment records based on the fuel consumption dynamic sequence record result, maps them to the time axis and identifies the behavior types, calculates the ratios, and generates the operation behavior energy consumption acquisition result;

[0048] The environmental interference assessment module extracts the wind direction, sea current difference, and tide level height within the time period of the operation behavior energy consumption acquisition result, calculates the included angle between the parameter and the hull direction, obtains the resultant force value through weighting, matches the interference level, and generates the environmental interference label annotation result;

[0049] The cost data stream integration module calls the timestamp, location, behavior type, total fuel consumption, and level of the environmental interference label annotation result, sorts them and sets the field order, constructs an index to generate entries, and generates the cost accounting data stream acquisition result.

[0050] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0051] In the present invention, the persistent operation interval is identified through the speed change rate, and the effective fuel consumption is screened through the cross-verification of the fuel liquid level and flow data, reducing the statistical deviation. Through the dynamic mapping of equipment operation parameters and energy consumption output, the refined splitting of operation behaviors and energy losses is realized, and the ability to identify abnormal working conditions is improved. The environmental parameters such as sea current and wind speed are introduced to calculate the resultant disturbance force, quantify the actual impact of natural conditions on energy consumption, and correct the cost assessment error. The multi-dimensional indexed data stream is constructed by integrating time and space, behavior, energy consumption, and interference labels, strengthening the correlation of multi-source information, supporting real-time dynamic analysis and resource optimization decision-making, and promoting the transformation of cost accounting from experience-driven to data-intelligence-driven. Description of the Drawings

[0052] Figure 1 is the method flow chart of the present invention;

[0053] Figure 2 is the acquisition flow chart of the operation section identification result of the present invention;

[0054] Figure 3It is a flowchart for obtaining the dynamic sequence record result of the fuel consumption of the present invention;

[0055] Figure 4 It is a flowchart for obtaining the acquisition result of the energy consumption of the operation behavior of the present invention;

[0056] Figure 5 It is a flowchart for obtaining the annotation result of the environmental interference label of the present invention;

[0057] Figure 6 It is a flowchart for obtaining the acquisition result of the cost accounting data stream of the present invention. Detailed implementation manners

[0058] Next, the technical solutions in the present invention will be described with reference to the accompanying drawings.

[0059] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0060] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, the meanings they express are the same.

[0061] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When their differences are not emphasized, the meanings they express are the same.

[0062] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0063] Please refer to Figure 1 , the present invention provides a technical solution: a real-time acquisition method for intelligent cost accounting data of marine fishery, including the following steps:

[0064] S1: Obtain the continuous longitude and latitude positioning sequence during the ship operation, calculate the time interval and speed change rate between adjacent sequence points, screen the time periods with the speed change rate lower than the set change rate threshold, judge the operation persistence within the corresponding time periods, and reconstruct the operation continuous interval sequence according to the judgment result to generate the operation section identification result;

[0065] S2: Call the start and end timestamps corresponding to the time period in the job section identification result, match the flow data and fuel level in the fuel record sequence, filter the fuel record sequence with the liquid level change amount not exceeding the set liquid level change amount threshold, compare the flow increment change and the liquid level trend difference in the same sequence, and calculate the fuel consumption of the same sequence to generate the fuel consumption dynamic sequence record result;

[0066] S3: Based on the fuel consumption dynamic sequence record result, extract the running duration of the electric winch, the tension change frequency of the trawl device, and the net deployment action record data during the ship operation in the corresponding time period, perform homing mapping on each type of equipment behavior parameter in chronological order, and perform behavior category identification. Calculate the behavior unit energy consumption output according to the fuel consumption contribution ratio corresponding to the behavior identification to generate the job behavior energy consumption collection result;

[0067] S4: Extract the sea surface wind speed direction value, sea current direction difference, and tidal level change height during the time period of the job behavior energy consumption collection result, analyze the included angle difference between the offset of each parameter and the hull operation direction, calculate the influence range of the disturbance direction resultant force and divide the interference level, and label the interference level and the corresponding job behavior data segment to generate the environmental interference label annotation result;

[0068] S5: According to the environmental interference label annotation result, call the job timestamp, job geographical location, behavior type number, total fuel consumption, and interference level in the corresponding time period, integrate and set the continuous storage field order in chronological order, construct a behavior identification index, and generate entries according to the behavior identification to generate the cost accounting data stream collection result.

[0069] The job section identification result includes the interval start and end timestamps, the geographical coordinate sequence of the job area, and the continuous status determination label. The fuel consumption dynamic sequence record result includes the instantaneous flow increment difference, the liquid level trend deviation value, and the segmented cumulative fuel consumption. The job behavior energy consumption collection result includes the winch operation cycle duration distribution set, the trawl tension fluctuation frequency spectrum, the net action trigger times set, and the behavior type energy consumption weight coefficient. The environmental interference label annotation result includes the wind speed direction offset vector angle, the sea current included angle fluctuation range, the tidal level height disturbance interval, and the hull disturbance level classification parameter. The cost accounting data stream collection result includes the behavior type coding index, the fuel consumption time series distribution table, and the environmental interference level mapping relationship.

[0070] Please refer to Figure 2 , and the steps for obtaining the job section identification result are as follows:

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

[0072] For every two adjacent longitude and latitude points, calculate the time interval, that is, subtract the timestamp of the previous point from the timestamp of the next point to obtain the time difference. Suppose the timestamp of the first point is 12:00:00 and the second point is 12:00:10, then the time interval is 10 seconds. Next, calculate the distance between these two points using the longitude and latitude calculation formula: d = R×arccos[sin(φ1)×sin(φ2)+cos(φ1)×cos(φ2)×cos(λ2 - λ1)], where R is the radius of the earth, approximately 6371 km, and φ1, λ1 and φ2, λ2 are the latitudes and longitudes of the two points respectively. Suppose the longitudes and latitudes of the two points are (30.0000°N, 120.0000°E) and (30.0005°N, 120.0005°E) respectively. Substitute into the formula to calculate, and the distance is approximately 55.5 meters. Then, calculate the speed. The speed is equal to the distance divided by the time interval, that is, 55.5 meters / 10 seconds = 5.55 meters per second. Next, calculate the change value of adjacent speeds, that is, subtract the previous speed from the next speed. Suppose the previous speed is 5.00 meters per second and the next speed is 5.55 meters per second, then the speed change value is 0.55 meters per second. Subsequently, calculate the speed change rate. The speed change rate is equal to the speed change value divided by the time interval, that is, 0.55 meters per second / 10 seconds = 0.055 meters per second². Repeat the above steps to traverse the entire longitude and latitude sequence to construct a speed change rate sequence. Then, set a speed change rate threshold, for example, 0.1 meters per second², and filter out the time periods with a speed change rate lower than this threshold. Suppose within the time period from 12:00:00 to 12:05:00, the speed change rate is lower than 0.1 meters per second², then mark this time period as a low variable speed time period to obtain a low variable speed time period sequence.

[0073] S102: Based on the low variable speed time period sequence, determine whether the interval time continuity of the longitude and latitude sequence points and the number of sequence points in each time period exceed the set operation persistence standard to obtain an operation continuous time period interval sequence;

[0074] Determine whether the time intervals of the longitude and latitude sequence points are continuous within each time period, that is, whether the time intervals of adjacent points are within the set range. For example, set the standard for time interval continuity to not exceed 15 seconds. Suppose within a certain time period, the time intervals of adjacent points are all 10 seconds, then this time period is considered time continuous. Next, count the number of sequence points within this time period. Suppose a certain time period contains 30 points. Subsequently, set the operation persistence standard. For example, the number of sequence points within the time period is not less than 20 and the time intervals are continuous. Filter out the time periods that meet this standard. Suppose a certain time period contains 30 points and the time intervals are continuous, then this time period is considered to meet the operation persistence standard. Finally, obtain an operation continuous time period interval sequence.

[0075] S103: Invoke the job duration time interval sequence. According to the speed fluctuation level, average speed, duration, and number of sequence points within the same interval, use the formula:

[0076]

[0077] Calculate the composite stability index value of the job interval through operations. Extract the time period and sequence number based on the index value to obtain the job section identification result;

[0078] Among them, represents the standard deviation of the speed values in the i-th section, represents the average speed of the i-th section, T i represents the duration of the i-th section, n i represents the number of sequence points included in the i-th section, represents the total number of points under all m sections, L i is the composite stability index value of the i-th job section;

[0079] Calculate the standard deviation of the speed within this interval. The standard deviation formula is: Among them, n i is the number of points within the interval, v ij is the speed of the j-th point, is the average speed within the interval. Assume there are 5 speed values within the interval, which are 5.0, 5.2, 5.1, 5.3, and 5.2 m / s respectively. Calculate the average speed: Calculate the standard deviation:

[0080]

[0081] Calculate the duration of the interval. Assume the start time of the interval is 12:00:00 and the end time is 12:05:00, then the duration is 5 minutes, that is, 300 seconds. Assume the obtained T i = 300 seconds, n i = 5, Calculate the composite stability index value and substitute it into the calculation:

[0082]

[0083] Set the stability reference value, for example, 1.0. Determine whether the composite stability index value is greater than this reference value. If it is greater, extract the time period and sequence number of this interval. Finally, obtain the job section identification result;

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

[0085] Please refer to Figure 3 , and the steps for obtaining the fuel consumption dynamic sequence record results are as follows:

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

[0087] Assume that the start time is 08:00:00 on April 1, 2025, and the end time is 10:00:00 on April 1, 2025. Extract all records within this time range from the fuel record sequence. Assume that data is recorded once per minute within this time period, so a total of 120 records are extracted. Each record contains a timestamp, flow data, and fuel liquid level data. Set the liquid level change amount threshold, for example, set it to ±0.5%, which means that within this time period, the change range of the fuel liquid level cannot exceed 0.5%. Calculate the fuel liquid level change amount of each record relative to the previous record. Assume that the fuel liquid level of the r-th record is h r , and the fuel liquid level of the (r - 1)-th record is h r-1 , then the change amount Δh r = h r - h r-1 , if the absolute value of Δh r does not exceed the set threshold, that is, |Δh r | ≤ 0.5%, then retain this record, otherwise eliminate it. Through the above screening, a record segment with stable liquid level change is obtained. Assume that 80 records are finally screened out to generate a liquid level stable record segment.

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

[0089]

[0090] Perform operations to obtain the sequence offset fluctuation value, measure the difference intensity between the fuel flow change and the liquid level trend change, and generate the offset detection result;

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

[0092] For example, assume that the recording time in the segment is once per minute, and a total of 5 records are extracted. Each record is in chronological order: 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 And the maximum liquid level h max , where f max = 470 L / h, h max = 940 mm, the number of records R t = 4, calculate the offset value for each pair of records in turn:

[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] Average the offsets of the 4 pairs of records to obtain the sequence offset fluctuation value:

[0103]

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

[0105] S203: Call the record segments in the offset detection results where the offset value is lower than the synchronization deviation threshold, calculate the cumulative sum of the fuel flow rate values of all record points within the segment according to the time difference between records, establish the correspondence between the total fuel consumption per unit time and the time stamp for each segment, and obtain the fuel consumption dynamic sequence record result;

[0106] Suppose the synchronization deviation threshold is set to 0.02, that is, when the sequence offset fluctuation value ΔQ s is less than 0.02, it is considered that the synchronization of the flow rate change and the liquid level change meets the requirements. Suppose the ΔQ s obtained in the above calculation is 0.015, which is less than the set threshold. This record segment is considered valid. For all record points within this record segment, calculate the fuel consumption according to the time difference between records. Suppose the record interval is 1 minute, that is, the time difference is 60 seconds. For the r - th record, the fuel flow rate value is f r , with the unit of L / h, then the fuel consumption within this time interval is (f r / 3600)·60 = f r / 60 L. Suppose f r is 450 L / h, then the fuel consumption within this time interval is 450 / 60 = 7.5 L. Calculate the fuel consumption for all record points within the segment in turn, obtain the fuel consumption for each time interval, accumulate these fuel consumptions, and obtain the total fuel consumption of this segment. Suppose there are 80 records in the segment, then the total fuel consumption is Record the start time stamp of each time interval and establish the fuel consumption dynamic sequence record result.

[0107] Please refer to Figure 4 , and the steps to obtain the acquisition result of the operation behavior energy consumption are as follows:

[0108] S301: Based on the fuel consumption dynamic sequence record result, extract the running duration of the electric winch, the tension change frequency of the trawl device, and the net - laying action record data within the corresponding time period, re - arrange and merge each type of data in the order of the acquisition time respectively, establish the mapping relationship between each operation equipment behavior and the time node, and obtain the operation equipment time series mapping result;

[0109] For example, during the period from 08:00 to 10:00 on April 1, 2025, the ship operation involves three types of equipment: an electric winch, a trawl device, and net deployment. The operating status data of the electric winch is collected in real time by a power meter and a start-stop switch status sensor, 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 device is collected by a tension sensor, and the number of tension wave peaks is statistically counted every 30 seconds in a sliding window manner to form a tension change frequency sequence. The net deployment action record data is recorded by the action status sensor of the deployment machinery in the form of timestamps for the net deployment action and the retrieval action. Each record contains the action type and the occurrence time. The three types of data are sorted and merged according to their respective timestamps. By constructing a unified time axis, a mapping matrix is constructed with a resolution of 1 minute, corresponding to the presence or absence, numerical size, and change trend of the behavior status of each equipment within each minute. A behavior identification coding system is set up. For example, the operation of the electric winch is identified as 1, the trawl tension fluctuation is identified as 2, and the net deployment is identified as 3. The behavior status within each minute is mapped to a coding sequence. If the electric winch is operating and the tension fluctuation frequency is higher than 2 times per minute within the minute of 08:03, it is mapped to [1, 2]. If only the net deployment action appears, it is mapped to [3]. After completing the unified mapping, the system establishes a behavior sequence matrix in chronological order to form a complete mapping result of the equipment behavior time series.

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

[0111]

[0112] Calculate to obtain the relative power value of the unit behavior category, establish an association with the corresponding behavior identifier, and obtain the equipment behavior unit energy consumption estimation sequence;

[0113] where ε c represents the unit energy consumption estimation of the operation behavior category c, U c is the number of time periods under the behavior category c, F c,u represents the corresponding equipment pulling force value in the time period numbered u, ΔD c,u represents the equipment displacement change amount within this time period, ΔT c,u represents the duration of this time period, P c represents the rated power of the equipment corresponding to the behavior category c;

[0114] For example, the tension data within a certain minute is recorded as [510, 495, 480, 499] N. Calculate its arithmetic mean F as 496 N. The displacement change value is the actual towing distance recorded by the displacement sensor installed on the ship. If the winch towed 3.5 meters within this minute, then ΔD = 3.5 m. At the same time, the duration of the recording period is 60 seconds. The rated power P of the equipment is provided by the factory calibration. For example, for an electric winch, it is 1800 W, and for a trawl device, it is 1500 W. Assume that the behavior category contains 4 time periods. Period 1: F = 496 N, ΔD = 3.5 m, ΔT = 60 s, P = 1800 W; Period 2: F = 515 N, ΔD = 3.0 m, ΔT = 60 s, P = 1800 W; Period 3: F = 500 N, ΔD = 4.2 m, ΔT = 60 s, P = 1800 W; Period 4: F = 490 N, ΔD = 3.8 m, ΔT = 60 s, P = 1800 W;

[0115] Substitute into the formula for multi-level calculation as follows:

[0116] Period 1:

[0117] Period 2:

[0118] Period 3:

[0119] Period 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 on average completes the unit behavior operation at 1.68% of the rated power, and finally obtains the equipment behavior unit energy consumption estimation sequence.

[0122] S303: According to 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 amount of each type of operation behavior, combine the calculation results into a structured sequence table, and generate the operation behavior energy consumption acquisition result;

[0123] For example, an electric winch behavior occurred for a total of 10 minutes during the operation, a trawl tension fluctuation behavior occurred for 15 minutes, and a net laying behavior occurred for 8 minutes. The corresponding unit energy consumption values are 0.01677, 0.01245, and 0.00780 respectively, and the rated powers of the equipment are 1800 W, 1500 W, and 800 W respectively. According to the behavior energy consumption total formula: E = ε c ×P c ×T c, the energy consumption of the electric winch behavior is calculated as: 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 behavior is 0.01245×1500×900 = 16706.25 J, and the energy consumption of the net deployment behavior is 0.00780×800×480 = 2995.2 J. The total energy consumption of each type of behavior is sorted into a structured table, including five items: behavior category, unit energy consumption value, duration, rated power, and total energy consumption, to establish the final acquisition result of the energy consumption of the operation behavior.

[0124] Please refer to Figure 5 , the steps to obtain the annotation result of the environmental interference label are as follows:

[0125] S401: Obtain the environmental factors within the time period of the acquisition result of the energy consumption of the operation behavior, collect the data of the sea surface wind speed direction, sea current direction, and tidal level change height in the corresponding sea area, calculate the included angle differences between the sea current direction and the wind speed direction and the hull direction respectively, and obtain the disturbance included angle difference sequence;

[0126] Assume that the recorded wind direction is 45° north by east, then it is assigned as 45°. The sea current direction is recorded by a Doppler current meter installed at the bottom of the ship, and the direction vector is updated every minute, and the unit is also °. If the sea current direction points to 120° in the current sampling period, then let φ vc = 120°. The tidal level change height is sampled by a tide gauge every minute, and the water level difference between this minute and the previous minute is used as the tidal level change amount, and the unit is meters. For example, the tidal level in the previous minute is 2.3 m and this minute is 2.5 m, then Δh = 0.2 m. At the same time, obtain the hull operation direction data, which is provided by the heading angle recorded by the ship's inertial navigation system or GPS compass, with an accuracy of 0.1°, a sampling period of 1 second, and the average value in the 60-second time period is taken as the hull operation direction value of this minute. For example, if the average heading value in this time period is 105°, then let φ vs = 105°. Synchronize the time alignment of the data within each minute, construct a sampling interval in minutes, and each interval contains four groups of parameters: wind direction angle φ vw 、sea current direction angle φ vc 、hull heading angle φ vs 、tidal level change height h td , arrange and record them in sequence according to time, construct a data sequence for disturbance impact analysis, and finally obtain the disturbance included angle difference sequence.

[0127] S402: Call the disturbance included angle difference sequence and the corresponding tidal level change height sequence, and according to the coupling strength between the included angle value of the wind speed direction and the sea current direction per unit time, the tidal level change height value and the hull operation direction included angle value, use the formula:

[0128]

[0129] Calculate the resultant influence value of the disturbance direction during this time period, calculate the disturbance influence amplitude range, and divide the disturbance level based on the interference reference level value to obtain the disturbance level recognition result;

[0130] Among them, R d represents the resultant influence value of the disturbance direction, N d represents the total number of time slices within this time period, θ w,j represents the wind direction angle of the j-th time slice, θ c,j represents the ocean current direction angle of the j-th time slice, θ s,j represents the hull operation direction angle of the j-th time slice, H j represents the tidal level change height of the j-th time slice, H max represents the maximum tidal level change height within the analysis time period;

[0131] Perform numerical calculations on the angles between the wind direction, the flow direction, and the hull direction. The calculation method for the wind direction angle is: α w =|φ vw -φ vs |, and the flow direction angle is: α c =|φ vc -φ vs |. Then substitute the two into the expression of the angle influence factor to calculate |cos(α w )| and |cos(α c )| respectively. The value range is [0,1]. When the angle is 0°, it means completely in the same direction and the cos value is 1. When the angle is 90°, the cos value is 0, indicating a perpendicular influence. In an actual example, if the wind direction is 75° and the ship direction is 105° within a certain minute, then α w =30°, and the corresponding cos value is approximately 0.866. The ocean current direction is 140°, α c =35°, and the cos value is approximately 0.819; the tidal level change value takes the collected value h td,e during this time period and is normalized with respect to the maximum tidal level change amplitude h td,max within this analysis period. 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; in addition, calculate the angle difference α wf =|φ vw -φ vc | and perform normalization processing by dividing by 180 to obtain the direction divergence term. For example, |75 - 140| / 180 = 0.361;

[0132] Taking 5-minute data as an example, the wind direction, flow direction, ship direction, and tidal level change height per minute are set as follows: For the 1st minute: 75°, 140°, 105°, 0.2 m; for the 2nd minute: 80°, 130°, 100°, 0.4 m; for the 3rd minute: 95°, 110°, 105°, 0.3 m; for the 4th minute: 60°, 150°, 100°, 0.5 m; for the 5th minute: 70°, 120°, 90°, 0.25 m;

[0133] The maximum tidal level change is set to 0.9 m. After substituting it into the formula and calculating item by item and then summarizing to find the average value, the resultant influence value R of the disturbance direction is finally obtained e = 1.504. Compare this value with the reference value of the interference level. The reference interval is 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 divided into level III, and finally the interference level recognition result is obtained.

[0134] S403: According to the interference level recognition result, match and label the interference level corresponding to each time period with the corresponding time period in the collected result of the energy consumption of the operation behavior on the time axis, establish a multi-dimensional information combination field, and obtain the environmental interference label annotation result;

[0135] If a certain behavior segment spans 3 minutes, the interference levels within these 3 minutes are respectively obtained, and the maximum interference level is taken as the corresponding level of this behavior segment. For example, if a certain behavior segment is from 08:01 to 08:03, and the corresponding interference levels are II, II, and III, then it is assigned as III. Add this interference level as a field to the behavior record, synchronously generate three parallel records of the behavior identifier, energy consumption value, and interference level, and finally integrate them into an energy consumption data structure table with an interference level field to establish the environmental interference label annotation result.

[0136] Please refer to Figure 6 , and the steps for obtaining the acquisition result of the cost accounting data stream are as follows:

[0137] S501: Based on the environmental interference label annotation result, 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 and integrate each item of data to generate a behavior identifier index;

[0138] Suppose to extract all job records between 0:00 on October 1, 2024 and 23:59 on October 7, 2024. The timestamp is precisely matched in the format of "year-month-day hour:minute:second". The fields of "job start time", "job end time", "job longitude and latitude coordinates", "behavior type number", "fuel consumption (unit: L)", and "interference level (level 1 - level 5)" need to be read from the corresponding records. Import the data fields into the data cleaning module, perform field integrity verification, and use interpolation at neighboring points for missing values. Uniformly convert the field units to the International System of Units (SI), such as converting longitude and latitude to decimal representation and retaining two decimal places for fuel quantity. Sort the records according to the timestamp field, arrange all job records in ascending order of "job start time", and determine whether there are duplicate coordinate points, duplicate behavior types, or abnormal fuel fluctuations between adjacent records (if the fuel quantity difference between two adjacent records exceeds 50L, mark it as an abnormal record) for investigation and filtering. Generate a unique job behavior identifier for the cleaned data. The naming method is to concatenate the "behavior number + timestamp + coordinate ID" to generate a unique index ID. For example: "A03_202410021215_115", where "A03" is the behavior number, 202410021215 is the timestamp format (December 2, 2024, 12:15), and 115 is the coordinate hash index number. Then write this identifier as the primary key into the behavior identifier index table for subsequent field matching operations. An example of this process is as follows: 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", marked as a valid behavior entry.

[0139] S502: Split the behavior identifier index to generate entries, obtain the cost accounting data stream for each job record, establish a data structure table and save it based on the behavior identifier index and the cost accounting data stream to obtain the cost accounting data stream collection result;

[0140] Retrieve the original operation record file according to the combined search of the three fields of "behavior number + timestamp + coordinate ID", extract the original data values of the corresponding records, and the corresponding fields include the operation time period (such as 12:15–12:35), coordinate points (such as 121.48°E, 31.22°N), behavior type number (such as A03), fuel consumption value (such as 38.5L), and interference level (such as 3). Then input this record into the cost data processing module, call the operation duration field (operation end time minus operation start time, such as 20 minutes), fuel consumption field (such as 38.5L), and interference level (level 3, quantified as a coefficient of 1.3 according to the rules), read and combine them item by item to generate the cost accounting data stream for this entry. The data stream structure is organized in JSON format, such as: {"behavior ID": "A03_202410021215_115", "time period": 20, "fuel": 38.5, "interference coefficient": 1.3}; Subsequently, perform the same operation on all behavior entries, batch generate the cost accounting data streams for multiple entries, write them into an independent table, and establish an association mapping with the aforementioned behavior identification index table using the behavior ID as the primary key, establish a field mapping structure, and list the fields "behavior ID", "operation time period", "coordinate number", "fuel consumption", and "quantified interference level value" as structural fields to organize into a data structure table. For example: behavior ID = A03_202410021215_115, time period = 20 minutes, coordinate number = 115, fuel = 38.5L, interference level coefficient = 1.3; After the structure table is completed, save it in the standard CSV format and upload it to the database to obtain the cost accounting data stream collection result.

[0141] An intelligent real-time cost accounting data acquisition system for marine fisheries, the system includes:

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

[0143] The fuel dynamic analysis module calls the start and end timestamps of the operation section identification result, matches the fuel flow data with the liquid level value, filters out the record sections where the liquid level change amount meets the standard, compares the flow increment with the difference in liquid level trend, and calculates the sum to generate the fuel consumption dynamic sequence record result;

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

[0145] The environmental interference evaluation module extracts the wind direction, sea current difference, and tide level height within the time period of the collected results of the energy consumption of the operation behavior, calculates the included angle between the parameter and the hull direction, obtains the resultant force value through weighting, matches the interference level, and generates an environmental interference label to mark the results;

[0146] The cost data stream integration module calls the time stamp, location, behavior type, total fuel consumption, and level of the environmental interference label marking results, sorts them and sets the field order, constructs an index to generate entries, and generates the collected results of the cost accounting data stream.

[0147] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. A real-time data acquisition method for intelligent cost accounting in marine fishery, characterized in that, It includes the following steps: S1: Obtain the longitude and latitude sequence of the ship operation, calculate the time interval and speed change rate between adjacent sequence points, judge the operation persistence according to the change rate and reconstruct the continuous interval segment, and generate the operation section identification result; S2: Call the start and end timestamps in the operation section identification result, match the flow rate and liquid level of the fuel record, compare the change in flow rate increment with the difference in liquid level trend, and calculate the fuel consumption to generate the fuel consumption dynamic sequence record result; S3: Based on the fuel consumption dynamic sequence record result, map the operation duration of the electric winch, the change frequency of the trawl tension, and the net deployment record during the ship operation, calculate the energy consumption per behavior unit according to the fuel consumption contribution ratio, and generate the operation behavior energy consumption acquisition result; S4: Extract the sea surface wind speed, the difference in sea current direction, and the tidal level change during the time period of the operation behavior energy consumption acquisition result, label the corresponding operation behavior data segment, and generate the environmental interference label annotation result; S5: According to the environmental interference label annotation result, sort by time and integrate the timestamp, geographical location, behavior type, total fuel consumption, and interference level, construct a behavior identification index and split it into entries to obtain the cost accounting data stream acquisition result.

2. The real-time data acquisition method for intelligent cost accounting of marine fishery according to claim 1, characterized in that: The operation section identification result includes the start and end timestamps of the interval, the geographical coordinate sequence of the operation area, and the persistence status determination label. The fuel consumption dynamic sequence record result includes the instantaneous flow rate increment difference, the liquid level trend deviation value, and the segmented cumulative fuel consumption. The operation behavior energy consumption acquisition result includes the winch operation cycle duration distribution set, the trawl tension fluctuation frequency spectrum, the net action trigger times set, and the behavior type energy consumption weight coefficient. The environmental interference label annotation result includes the wind speed direction offset vector angle, the sea current included angle fluctuation range, the tidal level height disturbance interval, and the ship hull disturbance level classification parameter. The cost accounting data stream acquisition result includes 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 fishery according to claim 1, wherein: The steps for obtaining the operation section identification result are as follows: S101: Obtain the continuous longitude and latitude positioning sequence during the ship operation, calculate the time interval and speed change value between adjacent sequence points, call the ratio of the speed change value to the time interval to construct a speed change rate sequence, and obtain the low-speed change time period sequence; S102: Based on the low-speed change time period sequence, judge whether the interval time continuity of the longitude and latitude sequence points and the number of sequence points in each time period exceed the set operation persistence standard to obtain the operation continuous time period interval sequence; S103: Call the operation continuous time period interval sequence, and according to the speed fluctuation level, average speed, duration, and number of sequence points in the same interval, use the formula: Operate to obtain the composite stability index value of the operation interval, extract the time period and sequence number according to the index value, and obtain the operation section identification result; Among them, represents the standard deviation of the speed values within the i-th section, represents the average speed of the i-th section, T i represents the duration of the i-th section, n i represents the number of sequence points included in the i-th section, represents the total number of points under all m sections, L i is the composite stability index value of the i-th operation section.

4. The real-time acquisition method of intelligent cost accounting data for marine fishery according to claim 1, characterized in that: The steps for obtaining the fuel consumption dynamic sequence record result are as follows: S201: Based on the start and end timestamps of the time period corresponding to the operation section identification result, match the timestamp of each record in the fuel record sequence, and screen the data in the time period where the liquid level change amount does not exceed the liquid level change amount threshold to generate a liquid level stable record segment; S202: Call the flow rate data and liquid level data in the liquid level stability record segment, compare the flow rate change rate and liquid level change rate between adjacent records in the same record sequence, calculate the deviation degree of the difference, and use the formula: Perform operations to obtain the sequence offset fluctuation value, measure the difference intensity between the fuel flow rate change and the liquid level trend change, and generate the offset detection result; Among them, f r represents the fuel flow value of the r-th record, h r represents the fuel level value of the r-th record, f max represents the maximum value of the flow rate in the current segment, h max represents the maximum value of the liquid level in the current segment, R t represents the number of record pairs in the current fuel record sequence, ΔQ s is the sequence offset fluctuation value; S203: Call the record segment in the offset detection result where the offset value is lower than the synchronous offset threshold, perform cumulative calculation on the fuel flow rate values of all record points in the segment according to the time difference between records, establish the corresponding relationship between the total fuel consumption per unit time and the time stamp of each segment, and obtain the fuel consumption dynamic sequence record result.

5. The real-time data acquisition method for intelligent cost accounting in marine fishery according to claim 1, wherein: The steps for obtaining the acquisition result of the energy consumption of the operation behavior are as follows: S301: Based on the fuel consumption dynamic sequence record result, extract the running duration of the electric winch, the tension change frequency of the trawl device, and the net deployment action record data within the corresponding time period, respectively rearrange each type of data in the order of the acquisition time, establish the mapping relationship between each operation equipment behavior and the time node, and obtain the operation equipment time series mapping result; S302: Call the equipment operation data sequence corresponding to the behavior type in the operation equipment time series mapping result, identify and judge the operation behavior types under the same time period number, map the behavior data in each time period to the fixed category code, and use the formula: Perform operations to obtain the relative power value of the unit behavior category, associate it with the corresponding behavior identifier, and obtain the equipment behavior unit energy consumption estimation sequence; Among them, ε c represents the estimated unit energy consumption of the operation behavior category c, U c is the number of time periods under the behavior category c, F c,u represents the corresponding equipment pulling force value in the time period numbered u, ΔD c,u represents the equipment displacement change amount within this time period, ΔT c,u represents the duration of this time period, P c represents the rated power of the equipment corresponding to the behavior category c; S303: According to 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 series, calculate the energy consumption contribution amount of each type of operation behavior, combine the calculation results into a structured sequence table, and generate the acquisition result of the operation behavior energy consumption.

6. The real-time data acquisition method for intelligent cost accounting of marine fishery according to claim 1, characterized in that: The steps for obtaining the acquisition result of the environmental interference label annotation are as follows: S401: Obtain the environmental factors during the time period when the acquisition result of the operation behavior energy consumption is located, collect the sea surface wind speed direction data, sea current direction data, and tidal level change height data of the corresponding sea area, calculate the included angle difference between the sea current direction and the wind speed direction and the hull direction respectively, and obtain the disturbance included angle difference sequence; S402: Call the disturbance included angle difference sequence and the corresponding tidal level change height sequence, and according to the coupling strength between the included angle value of the wind speed direction and the sea current direction per unit time, the tidal level change height value, and the included angle value between the hull operation direction, use the formula: Perform operations to obtain the resultant influence value of the disturbance direction during this time period, calculate the disturbance influence amplitude range, and divide the disturbance level according to the interference reference level value to obtain the disturbance level identification result; Among them, R d represents the resultant influence value of the disturbance direction, N d represents the total number of time slices within this time period, θ w,j represents the wind direction angle of the j-th time slice, θ c,j represents the ocean current direction angle of the j-th time slice, θ s,j represents the hull operation direction angle of the j-th time slice, H j represents the tidal level change height of the j-th time slice, H max represents the maximum tidal level change height within the analysis time period; S403: According to the disturbance level identification result, match and label the disturbance level corresponding to each time period with the corresponding time period in the acquisition result of the operation behavior energy consumption on the time axis, establish a multi-dimensional information combination field, and obtain the environmental interference label annotation result.

7. The real-time data acquisition method for intelligent cost accounting of marine fishery according to claim 1, characterized in that: The steps for obtaining the acquisition result of the cost accounting data stream are as follows: S501: Based on the environmental interference label annotation results, obtain the operation timestamps, operation geographical locations, behavior type numbers, total fuel consumption, and interference level data within the corresponding time period, sort them by time, and integrate each item of data to generate a behavior identification index. S502: Split the behavior identification index into items, obtain the cost accounting data stream of each operation record, establish a data structure table based on the behavior identification index and the cost accounting data stream, and save it to obtain the cost accounting data stream collection result.

8. An intelligent cost accounting data real-time acquisition system for marine fishery, characterized in that, Execute according to the real-time collection method of marine fishery intelligent cost accounting data described in any one of claims 1-7. The system includes: The operation section identification module obtains the ship's longitude and latitude positioning sequence, calculates the time interval and speed change rate between adjacent points, filters out the time periods with a speed change rate lower than the set threshold, judges the compliance with the persistence threshold, reconstructs the start and end timestamps of the operation section, and generates the operation section identification result. The fuel dynamic analysis module calls the start and end timestamps of the operation section identification result, matches the fuel flow data with the liquid level value, filters out the record segments with qualified liquid level change amounts, compares the flow increment with the difference in liquid level trend, calculates the sum, and generates the fuel consumption dynamic sequence record result. The behavior energy consumption aggregation module extracts the electric winch duration, trawl tension frequency, and net deployment records based on the fuel consumption dynamic sequence record result, maps them to the time axis, identifies the behavior type, calculates the ratio, and generates the operation behavior energy consumption collection result. The environmental interference assessment module extracts the wind direction, sea current difference, and tide level height within the time period of the operation behavior energy consumption collection result, calculates the included angle between the parameter and the hull direction, obtains the resultant force value through weighting, matches the interference level, and generates the environmental interference label annotation result. The cost data stream integration module calls the timestamps, locations, behavior types, total fuel consumption, and levels of the environmental interference label annotation result, sorts them and sets the field order, constructs an index to generate items, and generates the cost accounting data stream collection result.

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