A method and system for evaluating the status of fishery resources based on multi-level trend analysis

Abstract

This invention relates to the field of fishery resource evaluation technology, and discloses a method and system for evaluating the status of fishery resources based on multi-level trend analysis. The method acquires time-series data of the resource index of the target fishery resource. A first-level analysis is used to diagnose the long-term trend of the time series and determine the long-term trend type. Under the constraint of the long-term trend type, a second-level analysis is used to evaluate the recent change characteristics within a preset time window to determine the current dynamic type. Subsequently, the long-term trend type and the current dynamic type are used as combined inputs, and according to a pre-established one-to-one mapping rule, the corresponding fishery resource status type is determined, thereby completing the comprehensive evaluation of the target fishery resource. This invention, by constructing a hierarchical multi-level analysis structure, achieves the organic coupling of change characteristics at different time scales. The evaluation results are deterministic and repeatable, and do not rely on complex biological parameters, making it suitable for dynamic monitoring and management applications of fishery resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fishery resource evaluation, and particularly relates to a fishery resource condition evaluation method and system based on multi-level trend analysis. BACKGROUND

[0002] Fishery resources are an important component of marine ecosystems, and their quantity changes and state evolution are directly related to the safety of fishery production, the development of coastal economy, and the stability of marine ecosystems. With the continuous increase of fishing intensity, the intensification of marine environmental changes, and the superposition of human activities, many fishery resources show obvious temporal fluctuation characteristics. Continuous, scientific monitoring and evaluation of them have become an important basis for fishery resource management and sustainable utilization.

[0003] In the practice of fishery resource management, the results of resource condition evaluation are usually used to support the formulation of fishing quotas, the arrangement of fishing bans, the division of protected areas, and the risk warning of management decisions. Therefore, the scientificity, objectivity, and operability of the evaluation method directly affect the effectiveness and implementation effect of management measures.

[0004] (I) Quantitative resource evaluation method based on model In the prior art, the relatively mature fishery resource evaluation method mainly includes quantitative evaluation techniques based on population dynamics models, such as model methods based on age structure, biomass dynamics, or recruitment relationship. This kind of method usually needs long-term, continuous and high-quality catch data, catch per unit effort (CPUE) data, and relies on complete biological parameter information such as growth parameters, natural mortality, fecundity, and recruitment function.

[0005] Although the above method can theoretically reflect the dynamic change process of fishery resources more comprehensively, it still has obvious limitations in practical application. On the one hand, it has a high degree of dependence on data integrity and accuracy, and in the case of data missing or statistical instability, the reliability of the evaluation results is difficult to guarantee; on the other hand, the model construction, parameter estimation and result interpretation process is complex, and the requirements for professional technical personnel and computing conditions are high, which limits the popularization and application of this kind of method in grassroots fishery management departments, small-scale fisheries, and data-poor species.

[0006] (II) Trend analysis method based on time series In view of the situation of insufficient data or difficulty in constructing complex models, researchers have proposed various trend analysis methods based on time series data, which identify the overall trend direction of resource changes by performing statistical tests on resource indices, CPUE or biomass indicators. This kind of method usually uses non-parametric trend test or slope estimation techniques, has relatively low requirements for data distribution assumptions, and has the characteristics of relatively simple implementation and wide application range.

[0007] However, most of the existing trend analysis methods focus on the description of the characteristics of single time scale changes, and often only give conclusions such as "up", "down" or "no significant trend". This analysis method mainly reflects the average change state of resources in a long time scale, and it is difficult to effectively describe the response of resources to environmental changes or management measures in the near term, and it is also difficult to identify the turning point signal or the phased risk of resource state in a timely manner.

[0008] (Three) Resource state classification and experience determination method In actual management application, in order to facilitate decision-making implementation, it is often necessary to further summarize the analysis results into different resource state types, such as "resource good", "resource recession" or "need to pay attention to" and the like. In the existing technology, such resource state classification method usually relies on fixed threshold setting, single index judgment or expert experience determination.

[0009] Due to the differences in data characteristics of different research objects, different regions and different evaluation periods, the fixed threshold and the experience determination method have deficiencies in applicability and repeatability, which can easily lead to inconsistent evaluation results of the same resource under different evaluation conditions. In addition, this kind of classification method lacks clear rule constraints and has strong subjective factors, which is not conducive to long-term comparative analysis and standardized management of resource state.

[0010] (Four) Comprehensive deficiencies of existing technology From the above existing technology, it can be seen that the current fishery resource evaluation methods generally have the following problems: 1. Single analysis dimension Most methods focus on long-term average trends and lack systematic analysis of recent dynamic changes, making it difficult to reflect the phased changes in resource state in a timely manner.

[0011] 2. Lack of structured coupling mechanism of multiple time scales Long-term trend analysis and recent change analysis are often independent of each other, without forming a clear hierarchical relationship and logical constraints, making it difficult to comprehensively interpret the analysis results.

[0012] 3. Subjectivity of resource state determination Resource condition classification relies on experience judgment or fixed threshold, lacks deterministic determination rules based on analysis result combination, and affects the stability and repeatability of evaluation results.

[0013] 4. Limited scope of application Many methods have high requirements for data types and integrity, and are difficult to apply in data-poor fishery resources or non-target species evaluation.

[0014] Therefore, there is an urgent need for a new method for assessing the status of fishery resources. This method should be able to perform hierarchical analysis of the characteristics of fishery resource changes at different time scales based on time-series resource index data without relying on complex biological parameters. Furthermore, it should be able to transform the analysis results into standardized resource status types through clear rules, thereby improving the objectivity, interpretability, and management applicability of the resource assessment results. Summary of the Invention

[0015] Given that existing fishery resource status assessment methods mostly focus on single-time-scale analysis and lack hierarchical constraints and unified judgment mechanisms between analysis results of different time scales, which easily leads to problems such as strong subjectivity, insufficient stability and comparability of assessment results, the purpose of this invention is to provide a fishery resource status assessment method based on multi-level trend analysis. By constructing a hierarchical multi-level analysis structure, it realizes the collaborative analysis of long-term change characteristics and recent dynamic characteristics of fishery resources, and completes a deterministic assessment of resource status based on a rule-based mapping mechanism.

[0016] To achieve the above objectives, the present invention provides the following technical solution: In one embodiment of the present invention, a method for evaluating the status of fishery resources based on multi-level trend analysis is provided, the method comprising the following steps: S1: Obtain time series data of resource index of the target fishery resources over multiple consecutive years; S2: Perform a first-level analysis on the time series data of the resource index to conduct long-term trend diagnosis. Determine the long-term trend type corresponding to the target fishery resource by determining the significance of the trend, identifying the trend direction, and quantifying the intensity of interannual fluctuations. S3: Using the long-term trend type as the analysis constraint, select the most recent preset time window in the resource index time series data, perform the second-level analysis on the target fishery resources, and determine the current dynamic type of the target fishery resources by comparing the relationship between the change characteristics within the preset time window and the long-term trend type. S4: Using the long-term trend type and the current dynamic type as combined input parameters, and according to the pre-established one-to-one correspondence mapping rules, determine the fishery resource status type corresponding to the combination, thereby completing the comprehensive evaluation of the target fishery resources; The first-level analysis, the second-level analysis, and the comprehensive evaluation constitute a progressive and non-interchangeable multi-level analysis structure, with the execution logic and judgment results of the subsequent analysis taking the output of the previous analysis as a prerequisite.

[0017] Furthermore, in the multi-level analysis structure, the analysis results of each level are used as input parameters for subsequent analysis steps to limit the judgment range and judgment rules of subsequent analysis, so as to avoid the parallel superposition or independent judgment of analysis results at different time scales.

[0018] Furthermore, the long-term trend diagnosis includes performing trend significance testing, trend direction determination, and interannual fluctuation intensity quantification on the resource index time series data, and classifying the long-term trend type into at least one of rising, falling, statistical fluctuation, or relatively stable based on the above analysis results.

[0019] Preferably, the interannual fluctuation intensity is quantified by a statistical dispersion index, and the judgment threshold used to distinguish between statistical fluctuation and relative stability is dynamically determined based on the distribution characteristics of the interannual fluctuation intensity of multiple fishery resources in the same assessment batch.

[0020] Furthermore, the current dynamic assessment includes calculating the direction and magnitude of change within the preset time window, and comparing the direction and magnitude of change with the long-term change characteristics corresponding to the long-term trend type to determine whether the current dynamic type is consistent with the long-term trend, deviates from it, or shows a trend reversal.

[0021] Preferably, when the current dynamic change direction is opposite to the change direction of the long-term trend type, the current dynamic type is further classified into strong divergence or weak divergence based on the proportional relationship between the current change amplitude and the long-term change amplitude, and in conjunction with a preset classification threshold.

[0022] Optionally, the current dynamic assessment may also include detecting abrupt changes in the resource index time series data. When a significant abrupt change is detected within the most recent preset time range, the current dynamic type is determined to be a turning point.

[0023] Furthermore, the mapping rule between the long-term trend type and the current dynamic type is a pre-established deterministic rule used to map different long-term trend types and current dynamic types to corresponding fishery resource status types.

[0024] Furthermore, the types of fishery resource status include at least one of the following: good growth, recovery, decline, slowing decline, volatile and fragile, stable and sustainable, or alert.

[0025] Furthermore, the method evaluates the status of fishery resources solely based on time-series resource index data, without relying on the age structure parameters, growth parameters, or natural mortality parameters of the target fishery resources.

[0026] Furthermore, the determined fishery resource status type is used to trigger the corresponding fishery resource management strategy, monitoring frequency, or early warning level setting.

[0027] In one embodiment of the present invention, a fishery resource status assessment system is also provided, including a processor and a memory, wherein the processor is configured to execute program instructions stored in the memory to implement the method described in any of the above embodiments.

[0028] Based on the above technical solutions, this invention proposes a method for evaluating the status of fishery resources based on multi-level trend analysis. By constructing a progressive multi-level analysis structure consisting of long-term trend analysis, current dynamic assessment, and resource status mapping, it achieves a systematic characterization and standardized evaluation of the change process of fishery resources.

[0029] Compared to existing evaluation methods that rely solely on trend analysis or empirical judgment at a single time scale, this invention explicitly layers the analysis process across different time scales, using the results of the previous layer as constraints for the next. This creates an organic coupling between long-term change characteristics and recent dynamic responses, thus avoiding judgment biases caused by the fragmentation or simple superposition of different analysis results. This multi-layered analysis structure enables the timely identification of recent changes in fishery resources while maintaining stable long-term trends, improving the timeliness and consistency of resource status identification.

[0030] Furthermore, this invention uses long-term trend types and current dynamic types as combined inputs and employs pre-established one-to-one mapping rules to transform complex multidimensional analysis results into corresponding fishery resource status types. This avoids the problem of relying on fixed thresholds or expert experience for subjective judgment in traditional resource assessment processes, resulting in higher objectivity, consistency, and repeatability of the assessment results. This mapping mechanism ensures the comparability of assessment results across different resources and assessment periods, facilitating the continuous implementation of long-term monitoring and management decisions.

[0031] Furthermore, the method of this invention can assess resource status solely based on time-series resource index data without relying on complex biological parameters such as the age structure, growth parameters, or natural mortality rate of the target fishery resource. This significantly reduces the dependence on data completeness and professional modeling capabilities, making it applicable to fishery resources with relatively scarce data, non-target species, and grassroots fishery management scenarios. In this way, the applicability of resource assessment methods can be effectively expanded to different regions, species, and management conditions.

[0032] In summary, this invention, by constructing a multi-level trend analysis and evaluation mechanism with clear hierarchy, logical progression, and defined rules, achieves scientific diagnosis and standardized classification of fishery resource status, improves the practicality and reliability of resource evaluation results in fishery resource monitoring, risk warning, and management decision-making, and provides effective technical support for the sustainable utilization and refined management of fishery resources. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the overall process of a fishery resource status evaluation method based on multi-level trend analysis according to the present invention. The attached diagram schematically illustrates the overall process relationship between the input of resource index time series data, the first layer of long-term trend analysis, the second layer of current dynamic assessment, and the comprehensive evaluation of resource status.

[0034] Figure 2 This is a schematic diagram of the processing flow for the first layer of analysis in this invention—long-term trend analysis; The attached figure schematically illustrates the process of performing trend significance analysis, trend direction identification, and interannual fluctuation intensity quantification on resource index time series data, and determining the long-term trend type accordingly.

[0035] Figure 3 This is a schematic diagram of the processing flow for the second layer of analysis in this invention—the current dynamic evaluation. The attached diagram schematically illustrates the process of analyzing the direction and magnitude of recent changes and determining the current dynamic type within a preset time window, under the constraint of long-term trend type. Optionally, it includes a mutation point detection step.

[0036] Figure 4 This is a schematic diagram illustrating the mapping relationship for the comprehensive evaluation of fishery resource status according to the present invention; The attached diagram schematically illustrates the logical relationship between long-term trend type and current dynamic type as combined inputs, and outputting the corresponding fishery resource status type through a pre-established one-to-one mapping rule. Detailed Implementation

[0037] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the following embodiments are only used to explain the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art can make corresponding adjustments or equivalent substitutions to the order of steps, parameter settings, or implementation forms in the specific embodiments without departing from the technical concept of the present invention, and all such adjustments should fall within the scope of protection of the present invention.

[0038] In the following embodiments, resource index time series data will be used as input, and the target fishery resources will be subjected to long-term trend analysis, current dynamic assessment, and comprehensive evaluation of resource status in sequence according to the multi-level trend analysis process. The execution method and logical relationship of each analysis level will be explained with reference to the accompanying drawings to facilitate those skilled in the art to understand and implement the present invention.

[0039] I. General Description of the Implementation Examples 1.1 Overall Process Description of the Implementation Example See Figure 1 In one embodiment of the present invention, a schematic diagram of the overall implementation process of a fishery resource status evaluation method based on multi-level trend analysis is provided. For example... Figure 1 As shown, the method in this embodiment is used to systematically evaluate the status of target fishery resources. Its overall process includes steps such as acquiring resource index time series data, first-level long-term trend analysis, second-level current dynamic assessment, and comprehensive evaluation of fishery resource status.

[0040] Specifically, the first step is to obtain time-series data of the resource index of the target fishery resources over several consecutive years, which will serve as the basis for subsequent analysis. The time-series data of the resource index is arranged in chronological order to reflect the changes in the target fishery resources over a longer period.

[0041] After data acquisition, the first level of analysis, namely long-term trend analysis, is performed on the resource index time series data. By diagnosing the overall trend characteristics of resource changes, the long-term trend type corresponding to the target fishery resource is determined. The long-term trend type reflects the basic change state of the target fishery resource over a longer time scale and is an important prerequisite for subsequent analysis.

[0042] Based on the long-term trend type, a second layer of analysis, namely the current dynamic assessment, is performed on the resource index time series data. This step, using the long-term trend type as a constraint, selects the most recent preset time window from the resource index time series data to analyze the recent changes in the target fishery resources, thereby determining the current dynamic type corresponding to the target fishery resources.

[0043] After completing the long-term trend analysis and current dynamic assessment, the long-term trend type and the current dynamic type are used as combined inputs. Following a pre-established one-to-one mapping rule, the corresponding fishery resource status type is determined, thus completing a comprehensive evaluation of the target fishery resources. Under the constraints of the mapping rule, the determined fishery resource status type is output.

[0044] like Figure 1As shown, the above steps constitute a progressive multi-level analysis process. Each subsequent analysis takes the output of the previous analysis as a prerequisite. There is a clear logical connection between the analysis steps, rather than they being independent or set up in parallel.

[0045] 1.2 Composition and Explanation of Input Data In this embodiment, the input data for the fishery resource status assessment method is resource index time series data. The resource index time series data is used to characterize the quantity or abundance changes of the target fishery resources over several consecutive years, and its specific form can be determined based on actual monitoring conditions or statistical methods.

[0046] In one possible implementation, the resource index time series data is resource index data obtained by annual statistics, with the data from each year forming a continuous time series in chronological order. The resource index can be obtained from fishery resource monitoring, survey statistics, or other data acquisition methods that can reflect resource change trends, but the present invention does not specifically limit it in this regard.

[0047] It should be noted that the method of this invention does not rely on biological parameters such as age structure parameters, growth parameters, or natural mortality rates of the target fishery resources. It can perform multi-level trend analysis and complete resource status assessment solely based on the resource index time series data. Therefore, this method is applicable to fishery resources with relatively limited data and has good versatility and applicability.

[0048] Furthermore, the time length of the resource index time series data can be set according to actual conditions. As long as it can reflect the changing characteristics of the target fishery resources within a certain time range, it can meet the needs of the implementation of this invention. The description of data sources, data accuracy, and data collection methods in this embodiment is only used to illustrate the implementation process of this invention and does not constitute a limitation on the scope of protection of this invention.

[0049] In one alternative implementation, before performing the first-level analysis, the resource index time series data may be subjected to consistency checks and preprocessing to reduce the impact of missing values, outliers, or differences in caliber on subsequent analyses.

[0050] II. First-level analysis: Implementation methods for long-term trend analysis 2.1 Execution Location and Object of the First-Level Analysis See Figure 2 , Figure 2 The diagram illustrates the processing flow of the first-level analysis of this invention, namely long-term trend analysis. In this embodiment, the first-level analysis is used to diagnose the changing characteristics of target fishery resources over a longer time scale, and its execution target is complete resource index time series data.

[0051] Specifically, the resource index time series data consists of continuous multi-year data arranged in chronological order, used to reflect the overall changes in the target fishery resources within the observation period. The first-level analysis is directly based on the complete time series data without truncating or segmenting the data, thereby ensuring that the long-term trend diagnosis results can truly reflect the basic changing trends of the target fishery resources over a longer period of time.

[0052] like Figure 2 As shown, the first-level analysis serves as the initial analysis layer in the multi-level analysis structure, and its output will be used as the input condition for the subsequent second-level analysis. Therefore, the execution object and analysis scope of this analysis layer play a fundamental role in the entire evaluation process.

[0053] 2.2 Analysis process of trend significance and direction See Figure 2 In the first analysis sub-step of this invention, in one embodiment, the time series data of the resource index is analyzed for trend significance and direction of change to determine whether there is a statistically significant trend of change in the target fishery resources over a long-term scale.

[0054] Specifically, by performing trend significance analysis on the time series data of the resource index, it is determined whether the time series exhibits significant monotonic variation characteristics. When the trend significance analysis results indicate the presence of a significant trend, the direction of this trend is further determined to ascertain whether the target fishery resources are showing an upward or downward trend on a long-term scale.

[0055] In one possible implementation, statistical trend analysis methods can be used to process the resource index time series. For example, statistical tests can be performed on the relationship between the changes in each observation in the time series to determine whether the trend is significant and the direction of change. However, this invention does not limit the specific trend analysis algorithm used. Any analytical method that can determine the significance and direction of long-term trends can be applied to the first-level analysis process of this invention.

[0056] Through the above analysis steps, we can determine whether the changing trend of the target fishery resources is significant over a long timescale and the direction of its change, providing basic information for the comprehensive determination of the subsequent trend type.

[0057] 2.3 Quantification process of interannual fluctuation intensity See Figure 2 The second analysis sub-step, after completing the trend significance and change direction analysis, further quantifies the interannual fluctuation intensity of the resource index time series data to reflect the stability characteristics of the target fishery resources during the change process.

[0058] Specifically, by statistically analyzing the variation range of the annual observations in the resource index time series, the degree of fluctuation of the target fishery resources between different years is quantified. The interannual fluctuation intensity is used to characterize whether there are large unstable fluctuations in the target fishery resources during long-term changes, rather than making a judgment based solely on the direction of change.

[0059] In this embodiment, interannual fluctuation intensity, as an important component of long-term trend diagnosis, participates in the determination of long-term trend type along with the results of trend significance analysis and change direction analysis. By introducing quantitative analysis of interannual fluctuation intensity, we can avoid simply classifying resource change states based solely on trend direction, thereby improving the comprehensiveness and reliability of long-term trend determination results.

[0060] 2.4 Determining the Long-Term Trend Type (First-Level Output) See Figure 2 At the output end, after completing the trend significance analysis, the determination of the direction of change, and the quantification of the interannual fluctuation intensity, the above analysis results are comprehensively judged to determine the long-term trend type corresponding to the target fishery resources.

[0061] Specifically, the first step is to determine whether there is a significant long-term trend in the target fishery resources based on the results of the trend significance analysis: when there is a significant trend, the long-term trend type is determined to be either upward or downward based on the results of the direction of change; when there is no significant trend, the long-term trend type is determined to be either statistical fluctuation or relatively stable based on the quantitative results of the interannual fluctuation intensity.

[0062] Therefore, the first-level analysis outputs the long-term trend type corresponding to the target fishery resources. This long-term trend type, as the main output of the first-level analysis, summarizes the basic changes in the target fishery resources over a long timescale. In the multi-level analysis structure of this invention, the long-term trend type serves as the input condition for the second-level analysis—the current dynamic assessment—to limit the judgment logic and analysis scope of subsequent analyses, thereby achieving hierarchical constraints and organic connections between analysis results at different timescales.

[0063] In one alternative implementation, the threshold used to distinguish between statistical volatility and relative stability is dynamically determined based on the interannual volatility intensity distribution of multiple fishery resources in the same assessment batch. Preferably, the threshold can be taken as the percentile threshold of the interannual volatility intensity distribution; alternatively, the threshold can be determined based on the mean and standard deviation, or determined using robust statistics. The dynamic determination method of the above threshold does not affect the basic process of the first-level analysis, but is only used to improve the adaptability and robustness of long-term trend type determination under different resource objects and data conditions.

[0064] III. Second-Level Analysis: Current Implementation Methods of Dynamic Evaluation 3.1 Prerequisites for Second-Level Analysis See Figure 3 , Figure 3 The diagram illustrates the processing flow of the second-level analysis of the present invention, namely the current dynamic assessment. In this embodiment, the current dynamic assessment is performed after the first-level analysis has been completed and the long-term trend type corresponding to the target fishery resource has been determined.

[0065] Specifically, the second-level analysis is not conducted independently of the first-level analysis. Instead, it uses the long-term trend type output by the first-level analysis as an analytical constraint, and further assesses the recent changes in the target fishery resources based on this constraint. In other words, the long-term trend type defines the analytical background and judgment logic for the current dynamic assessment, enabling the second-level analysis to provide a reasonable explanation of recent dynamics within the established long-term change framework.

[0066] Through the above settings, a clear hierarchical relationship is formed between the second-level analysis and the first-level analysis, avoiding the parallel processing or separation of analysis results at different time scales, thereby ensuring the overall consistency and logical coherence of the multi-level analysis structure.

[0067] In one optional implementation, the current dynamic assessment does not indiscriminately determine all types of change. Instead, it constrains the determination logic of the second-level analysis based on the long-term trend type obtained from the first-level analysis. For example, when the long-term trend type is upward, the second-level analysis focuses on identifying whether recent changes continue or weaken this upward trend; when the long-term trend type is downward, the second-level analysis focuses on identifying whether recent changes show signs of mitigation or reversal. This approach makes the current dynamic determination logic specific to different long-term trend backgrounds, thereby avoiding the mechanical superposition of analysis results from different time scales.

[0068] 3.2 Selection of preset time window See Figure 3 In the time window selection step, in this embodiment, the current dynamic assessment analyzes the most recent preset time window in the resource index time series data to reflect the change characteristics of the target fishery resources in the recent stage.

[0069] Specifically, data from the most recent few years is extracted from the complete resource index time series data according to preset rules and used as an analysis window. The length of the time window can be set according to actual assessment needs, as long as it can reflect the recent trend of the target fishery resources to a certain extent, it can meet the needs of the implementation of this invention.

[0070] It should be noted that the time window is a pre-set analysis parameter, selected to highlight recent changes rather than to reassess long-term trends. By using a pre-set time window, the sensitivity to recent resource changes can be improved while maintaining the stability of long-term trend analysis results.

[0071] In one optional implementation, the length of the preset time window is less than the length of the time series used for long-term trend analysis, and the time window covers multiple recent consecutive observation periods to ensure that the current dynamic assessment can reflect recent change characteristics without affecting the stability of the long-term trend analysis results.

[0072] In one alternative implementation, the preset time window is the most recent consecutive N observation periods, where N is a preset positive integer. Preferably, N is 3 to 5; alternatively, N is 6 to 10. The N can be adjusted according to data availability, resource lifecycle characteristics, or management cycle settings.

[0073] 3.3 Calculation of Recent Change Characteristics See Figure 3 The core calculation step involves selecting a preset time window and then analyzing the resource index time series data within that time window to calculate the recent change characteristics of the target fishery resources.

[0074] Specifically, by processing the changes in the resource index for each year within a preset time window, the direction and magnitude of changes in the target fishery resources in the recent period are calculated. The direction of change characterizes whether the resource index generally shows an upward, downward, or basically stable trend in the recent period, while the magnitude of change characterizes the strength of this trend.

[0075] The recent change characteristics, as an important analytical result of the current dynamic assessment, will be used to compare and analyze with the long-term trend types obtained from the first-level analysis in order to identify the dynamic performance of the target fishery resources in the recent stage.

[0076] 3.4 Determination of the current dynamic type See Figure 3 The main output step is to compare and analyze the recent change characteristics with the long-term trend type determined in the first layer analysis after obtaining the recent change direction and magnitude, thereby determining the current dynamic type of the target fishery resource.

[0077] Specifically, by judging whether the recent direction of change is consistent with the direction of change reflected by the long-term trend type, it can be determined whether the target fishery resources continue the long-term trend in the recent stage; when the recent direction of change is inconsistent with the direction of change reflected by the long-term trend type, it can be determined whether the target fishery resources have deviated in the recent stage; in addition, the trend reversal of the target fishery resources can be judged by combining the changes in the recent characteristics.

[0078] Through the above comparative analysis, the current dynamic type of the target fishery resource was finally determined. This current dynamic type summarizes the recent changes in the target fishery resource, is the output of the second-level analysis, and will be used together with the long-term trend type as input parameters for subsequent comprehensive evaluation of the resource status.

[0079] 3.5 Optional methods for mutation point detection ( Figure 3 (Optional path in) See Figure 3 In one optional embodiment of the present invention, the current dynamic assessment may also include mutation point detection of resource index time series data to identify whether the target fishery resources have undergone significant structural changes in the recent period.

[0080] Specifically, after selecting a preset time window and calculating recent change characteristics, abrupt change point detection analysis can be performed on the resource index time series data to determine whether there are significant time nodes in the time series indicating a transition from one state of change to another. This abrupt change point detection is used to supplement the trend comparison analysis results, thereby improving the ability to identify turning points in resource changes.

[0081] In one possible implementation, when the mutation point detection results indicate a significant mutation within a preset recent timeframe, the change process of the target fishery resource can be considered to have undergone a significant change in the recent stage. In this case, the judgment is no longer based solely on the consistency or divergence between the recent change direction and the long-term trend type, but rather on identifying the current dynamic type of the target fishery resource as a turning point.

[0082] It should be noted that mutation point detection is an optional analytical step in the current dynamic assessment, and its execution does not affect the basic workflow structure of the second-level analysis. When mutation point detection is not performed, or when the mutation point detection results do not show significant mutations, the current dynamic type can still be determined based on the comparison between recent change characteristics and long-term trend types.

[0083] By introducing the aforementioned optional mutation point detection implementation methods, the ability to identify potential turning points in recent changes of target fishery resources can be enhanced while maintaining the stability of the multi-level analysis structure, thereby making the current dynamic assessment results more comprehensive and reliable.

[0084] IV. Implementation Methods for Comprehensive Evaluation of Resource Status 4.1 Formation of Combined Inputs See Figure 4 , Figure 4 A schematic diagram illustrating the mapping relationship of the comprehensive resource status evaluation stage of this invention is shown. In this embodiment, the comprehensive resource status evaluation is performed based on the completion of the first-level long-term trend analysis and the second-level current dynamic assessment.

[0085] Specifically, the long-term trend type output from the first-level analysis and the current dynamic type output from the second-level analysis together constitute the combined input parameters for the comprehensive evaluation of resource status. The long-term trend type is used to characterize the basic change status of the target fishery resources over a long timescale, while the current dynamic type is used to characterize the change characteristics of the target fishery resources in the recent stage.

[0086] By using the above two types of analysis results as combined inputs, rather than using them separately or independently, the long-term change background and recent dynamic performance of the target fishery resources can be considered simultaneously in the comprehensive evaluation stage, providing complete and structured input conditions for the subsequent determination of resource status types.

[0087] 4.2 Execution process of mapping rules See Figure 4 In this embodiment, the comprehensive evaluation of resource status maps the combination input of the long-term trend type and the current dynamic type to the corresponding fishery resource status type by executing pre-established mapping rules.

[0088] Specifically, the mapping rules are pre-set in the system, establishing a one-to-one correspondence between different combinations of long-term trend types and current dynamic types and fishery resource status types. These mapping rules are deterministic, meaning that given a set of input combinations of long-term trend types and current dynamic types, a definite evaluation result can be output, without relying on human judgment or experience-based adjustments.

[0089] By adopting the above mapping rules, the multidimensional information obtained in the multi-level analysis process can be transformed into clear and standardized resource status judgment results, thereby reducing the risk of inconsistent evaluation results caused by subjective judgment differences among different evaluators or in different application scenarios.

[0090] In one alternative implementation, the mapping rules are not arbitrarily set, but are pre-designed based on the logical relationship between long-term trend types and current dynamic types, so that resource status types under different combinations of conditions can reflect the phased characteristics of resource changes. When establishing the mapping rules, they can be based on historical monitoring data, resource assessment practices, or management objectives, and can be solidified through expert knowledge and historical case summarization, or after consistency verification of historical data. They can be used stably in the same management area or the same resource category to ensure the interpretability and consistency of the evaluation results.

[0091] 4.3 Output of Fishery Resource Status Types See Figure 4 The output terminal, after completing the execution of the mapping rules, outputs the fishery resource status type corresponding to the combined input, thereby completing the comprehensive evaluation of the target fishery resources.

[0092] In this embodiment, the fishery resource status type is used to summarize the overall status of the target fishery resources in the current assessment period. The type may include, but is not limited to: good growth, decline, recovery, slowing decline, volatile and fragile, stable and sustainable, or alert.

[0093] It should be noted that the fishery resource status type, as an output of the comprehensive resource status evaluation stage, is an evaluation result automatically determined under the constraints of established mapping rules, and thus possesses determinism and repeatability. This output result can be directly used for subsequent fishery resource management decisions, monitoring strategy formulation, or risk warning settings without requiring further manual interpretation of the analysis process.

[0094] V. Technical Effects of the Embodiments 5.1 Summary of Overall Implementation Results Combination Figures 1-4 As can be seen from the implementation process shown, this embodiment achieves a systematic diagnosis of the changing state of the target fishery resources by constructing a multi-level analysis structure consisting of a first-level long-term trend analysis, a second-level current dynamic assessment, and a comprehensive evaluation of resource status.

[0095] like Figure 1 As shown, the method in this embodiment takes resource index time series data as input, performs long-term trend analysis and current dynamic assessment sequentially, and outputs the fishery resource status type through mapping rules. There is a clear hierarchical progression between each analysis step. This structural design allows change information at different time scales to be processed within a unified framework, avoiding the problems of fragmented or simply superimposed analysis results.

[0096] like Figure 2 and Figure 3As shown, the first-level analysis identifies the basic changing trends of the target fishery resources over a long-term timescale. The second-level analysis, constrained by the long-term trend type, assesses the recent change characteristics, thus reflecting the dynamic changes of the resources in the recent stage while maintaining the stability of the long-term trend judgment. Through this hierarchical analysis method, the evaluation results have both a long-term perspective and take into account the recent response.

[0097] like Figure 4 As shown, after completing the multi-level analysis, by combining the long-term trend type and the current dynamic type as inputs and executing a pre-established one-to-one mapping rule, the multi-dimensional analysis results can be transformed into a clear and definite type of fishery resource status, thereby completing a comprehensive evaluation of the target fishery resources. Thus, this embodiment achieves a complete closed loop from time series analysis to resource status determination.

[0098] 5.2 Summary of Technical Advantages Based on the above implementation process, this embodiment has at least the following technical effects and advantages: First, by adopting a multi-level analysis structure with layered implementation, long-term trends and recent dynamic changes are distinguished and organically linked, so that the analysis results at different time scales can be reasonably interpreted within a unified logical framework, thereby improving the structural clarity of resource status assessment results.

[0099] Secondly, by introducing a mapping rule based on the combination of long-term trend type and current dynamic type in the comprehensive evaluation stage of resource status, the rule-based determination of resource status type is realized, avoiding the problem of relying on human experience or subjective judgment for classification, and making the evaluation results more objective and consistent.

[0100] Furthermore, since the mapping rules are deterministic and one-to-one, the same evaluation results can be obtained repeatedly under the same input conditions, thereby improving the repeatability and comparability of the fishery resource status evaluation results, which is conducive to the long-term monitoring and management of resources.

[0101] In summary, this embodiment, through the combination of a multi-level analysis structure and a rule mapping mechanism, achieves a clear diagnosis and standardized evaluation of the status of fishery resources without increasing complex data dependencies, and has good practical application value.

[0102] VI. System Implementation Method (Optional) 6.1 System Composition Description See Figure 1 In one embodiment of the present invention, a system for implementing the above-described method for assessing the status of fishery resources is also provided. The system includes at least a processor and a memory.

[0103] The memory is used to store computer program instructions and data required to execute the method; the processor is communicatively connected to the memory and is used to call and execute the program instructions stored in the memory to realize the fishery resource status evaluation method of the present invention.

[0104] In this embodiment, the processor can be a general-purpose processor, a special-purpose processor, or other processing unit with data processing capabilities; the memory can be any form of computer-readable storage medium used to store program instructions and intermediate calculation results. This invention does not limit the specific type of processor or the specific implementation of the memory.

[0105] 6.2 Corresponding System Execution Flow and Method Steps In this embodiment, when the processor executes the program instructions, the system implements a system execution flow that is consistent with... Figure 1 The steps shown correspond one-to-one.

[0106] Specifically, when the processor executes program instructions, it first controls the system to acquire the resource index time series data of the target fishery resources and uses the data as input data for subsequent analysis. Then, the processor performs a first-level analysis based on the resource index time series data to conduct a long-term trend analysis of the target fishery resources and outputs the long-term trend type.

[0107] After obtaining the long-term trend type, the processor further performs a second-level analysis on the resource index time series data under the premise of the long-term trend type as a constraint, performs a current dynamic assessment of the target fishery resources, and outputs the current dynamic type.

[0108] After completing the above two-layer analysis, the processor takes the long-term trend type and the current dynamic type as combined inputs, performs a comprehensive evaluation of resource status according to the pre-established mapping rules, and outputs the fishery resource status type corresponding to the combined inputs, thereby completing the evaluation of the target fishery resource status.

[0109] The above system execution flow demonstrates that the system achieves [interaction / cooperation] through software. Figure 1 The corresponding processing steps of the method shown enable the fishery resource status assessment method of the present invention to operate stably in a systematic manner, which is convenient for deployment and implementation in practical applications.

[0110] In summary, this invention constructs a multi-level analytical structure comprising long-term trend analysis, current dynamic assessment, and comprehensive resource status evaluation. This structure organically integrates resource change information across different time scales and achieves a deterministic determination of fishery resource status through a rule-based mapping mechanism. Compared to existing technologies, this invention's method features a clear and logically progressive analytical structure. It maintains the stability of long-term trend determination while reflecting the dynamic changes in resources in the near term. The evaluation results exhibit good repeatability and consistency, making it suitable for applications such as fishery resource monitoring, assessment, and management decision-making.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any equivalent substitutions, equivalent modifications, or improvements made by those skilled in the art based on the technical solution of the present invention without departing from the technical concept of the present invention shall fall within the scope of protection of the present invention, which is defined by the claims.

Claims

1. A method for evaluating the status of fishery resources based on multi-level trend analysis, characterized in that, Includes the following steps: S1: Obtain time series data of resource index of the target fishery resources over multiple consecutive years; S2: Perform a first-level analysis on the resource index time series data to conduct long-term trend diagnosis. Determine the long-term trend type corresponding to the target fishery resource by determining the significance of the trend, identifying the trend direction, and quantifying the intensity of interannual fluctuations. S3: Using the long-term trend type as the analysis constraint, select the most recent preset time window in the resource index time series data, perform the second-level analysis on the target fishery resources, and determine the current dynamic type of the target fishery resources by comparing the relationship between the change characteristics within the preset time window and the long-term trend type. S4: Using the long-term trend type and the current dynamic type as combined input parameters, and according to the pre-established one-to-one correspondence mapping rules, determine the fishery resource status type corresponding to the combination, thereby completing the comprehensive evaluation of the target fishery resources; The first-level analysis, the second-level analysis, and the comprehensive evaluation constitute a progressive and non-interchangeable multi-level analysis structure, with the execution logic and judgment results of the subsequent analysis taking the output of the previous analysis as a prerequisite.

2. The method according to claim 1, characterized in that, In the multi-level analysis structure, the analysis results of each level are used as input parameters for subsequent analysis steps to limit the judgment range and judgment rules of subsequent analysis, so as to avoid the parallel superposition or independent judgment of analysis results at different time scales.

3. The method according to claim 1, characterized in that, The long-term trend diagnosis includes performing trend significance testing, trend direction determination, and interannual fluctuation intensity quantification on the time series data of the resource index, and classifying the long-term trend type into at least one of rising, falling, statistical fluctuation, or relatively stable based on the above analysis results.

4. The method according to claim 3, characterized in that, The intensity of interannual fluctuations is quantified by a statistical dispersion index, and the threshold for distinguishing between statistical fluctuations and relative stability is dynamically determined based on the distribution characteristics of interannual fluctuation intensity of multiple fishery resources in the same assessment batch.

5. The method according to claim 1, characterized in that, The current dynamic assessment includes calculating the direction and magnitude of change within the preset time window, and comparing the direction and magnitude of change with the long-term change characteristics corresponding to the long-term trend type to determine whether the current dynamic type is consistent with the long-term trend, deviates from it, or shows a trend reversal.

6. The method according to claim 5, characterized in that, When the current dynamic change direction is opposite to the change direction of the long-term trend type, the current dynamic type is further classified into strong divergence or weak divergence based on the ratio between the current change amplitude and the long-term change amplitude, and in conjunction with a preset classification threshold.

7. The method according to claim 5, characterized in that, The current dynamic assessment also includes detecting abrupt change points in the resource index time series data. When a significant abrupt change point is detected within the most recent preset time range, the current dynamic type is determined to be a turning point.

8. The method according to claim 1, characterized in that, The mapping rule between the long-term trend type and the current dynamic type is a pre-established deterministic rule used to map different long-term trend types and current dynamic types to the corresponding fishery resource status type.

9. The method according to claim 1, characterized in that, The fishery resource status type includes at least one of the following: Good growth, recovery, recession, slowing recession, volatile and fragile, stable and sustainable, or alert.

10. A fishery resource status assessment system, comprising a processor and a memory, characterized in that, The processor is configured to execute program instructions stored in the memory to implement the method according to any one of claims 1 to 9.

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