A method and apparatus for dynamic and iterative allocation of crew positions

Abstract

The application discloses a kind of dynamic iteration allocation method and device of crew post, the method includes: under the preset crew post arrangement information set, ship equipment operation information set is obtained by acquisition;The crew post arrangement information set includes several crew allocation schemes;The crew post arrangement information set and ship equipment operation information set are subjected to regression analysis modeling processing, and the ship equipment operation efficiency optimization model is obtained;The ship equipment operation efficiency optimization model is solved, and crew post optimization configuration information is obtained;The crew post optimization configuration information includes the crew serial number value arranged by all ship equipment.

Description

Technical Field

[0001] This invention relates to the fields of industrial data processing, strategy optimization, and data modeling, and specifically to a method and apparatus for dynamic iterative allocation of crew positions. Background Technology

[0002] During ship navigation and operation, the allocation of crew positions directly affects the operational safety, stability, and overall operational efficiency of ship equipment. Ship equipment typically includes various types, and each type involves multiple technical indicators such as power, efficiency, vibration, and reliability. The operational status of these indicators is directly affected by the skill level, operating habits, and job suitability of the operators. Therefore, the proper matching of crew members to their positions is crucial for maximizing equipment effectiveness.

[0003] Currently, most crew allocation methods in the shipping industry adopt a static allocation strategy, which pre-determines allocation plans based on crew members' basic qualifications (such as certificates and years of service) and makes few adjustments during the ship's sailing cycle. This static allocation method has obvious limitations: First, it does not incorporate real-time data on the actual operating status of ship equipment, making it difficult to promptly detect abnormal equipment operating indicators caused by a decline in crew-job fit. For example, when a crew member operates a specific piece of equipment, the equipment's vibration index may remain consistently high, but this cannot be improved through reassignment adjustments. Second, the evaluation dimensions of equipment operating status are relatively singular, usually focusing only on one or two key indicators (such as output power), without comprehensively considering the synergistic effects of multiple technical indicators. This results in an incomplete assessment of the overall operating efficiency of the equipment, and consequently, it cannot provide an accurate basis for optimizing job allocation.

[0004] Meanwhile, existing modeling methods for the correlation between crew positions and equipment operating efficiency are relatively simple. Most of them directly establish a linear relationship between crew allocation schemes and single equipment indicators, without considering the category differences between different crew allocation schemes, and without fully integrating the dynamic characteristics of equipment operating data (such as the changing trend of indicators over time). This results in poor adaptability of the model to complex ship operation scenarios, insufficient accuracy of the job configuration information obtained, and difficulty in meeting the actual needs of efficient and safe operation of ship equipment. Summary of the Invention

[0005] This invention primarily addresses the problem that the correlation modeling methods for crew positions and equipment operating efficiency in crew position allocation are relatively simple. Most of these methods directly establish a linear relationship between crew allocation schemes and single equipment indicators, without considering the category differences between different crew allocation schemes or fully integrating the dynamic characteristics of equipment operating data (such as the changing trend of indicators over time). This results in poor adaptability of the model to complex ship operating scenarios. This invention discloses a dynamic iterative allocation method and device for crew positions.

[0006] In a first aspect, this invention discloses a method for dynamic iterative allocation of crew positions, comprising: S1, Under a preset set of crew position arrangement information, a set of ship equipment operation information is collected; the set of crew position arrangement information includes several crew allocation schemes; the crew allocation scheme includes the crew sequence number value assigned to all ship equipment; the set of ship equipment operation information includes a subset of ship equipment operation status information corresponding to each crew allocation scheme; the subset of ship equipment operation status information includes a sequence of operation status values ​​for all technical indicators of each ship equipment; S2, perform regression analysis and modeling on the set of crew position arrangement information and the set of ship equipment operation information to obtain the ship equipment operation efficiency optimization model; S3, Solve the ship equipment operation efficiency optimization model to obtain crew position optimization configuration information; the crew position optimization configuration information includes the crew number values ​​assigned to all ship equipment.

[0007] The regression analysis and modeling process performed on the set of crew position assignment information and the set of ship equipment operation information yields a ship equipment operation efficiency optimization model, including: S21, Perform a quantitative assessment of the technical status of the ship equipment operation information set to obtain a comprehensive technical status value set of the ship equipment; the comprehensive technical status value set of the ship equipment includes the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme; S22, perform relevant function modeling on the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information to obtain the ship equipment operation efficiency optimization model.

[0008] The process of performing a quantitative assessment of the technical status of the ship's equipment operation information set to obtain a comprehensive set of ship equipment technical status values ​​includes: S211, For each crew allocation scheme in the ship equipment operation information set, the technical status of the corresponding ship equipment operation status information subset is quantitatively evaluated to obtain the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme. S212, using the comprehensive technical status values ​​of ship equipment corresponding to all crew allocation schemes, a set of comprehensive technical status values ​​of ship equipment is constructed.

[0009] The technical status quantification assessment is performed on each subset of ship equipment operation status information corresponding to each crew allocation scheme in the ship equipment operation information set to obtain the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme, including: S2111, obtain the status standard values ​​of all technical indicators for each ship's equipment; S2112, For each technical indicator of a ship equipment in the ship equipment operation status information subset corresponding to each crew allocation scheme, the sequence of operation status values ​​is subtracted from the status standard value of the corresponding technical indicator of the ship equipment to obtain the difference sequence of the technical indicator; S2113, Perform a status assessment on the difference sequence of all technical indicators of the ship equipment to obtain the status assessment value of the ship equipment; S2114, perform a fusion calculation on the status assessment values ​​of all ship equipment to obtain the comprehensive technical status value of the ship equipment corresponding to the crew allocation scheme.

[0010] The condition assessment of the ship's equipment by performing a condition assessment on the difference sequence of all technical indicators of the ship's equipment, to obtain the condition assessment value of the ship's equipment, includes: S21131, For the difference sequence of each technical indicator of the ship equipment, perform segmented trend decomposition processing to obtain a number of corresponding segmented sequences; S21132, perform least squares fitting on the data points in each segmented sequence to obtain the corresponding linear polynomial; S21133, For each linear polynomial, perform local feature calculation to obtain the corresponding local features; S21134, calculate the health index of the local features of each linear polynomial to obtain the corresponding health index; S21135, using the indicator health of all linear polynomials of the difference sequence of a technical indicator, a sequence of indicator health is constructed; S21136, Calculate the liquidity value of the health sequence of the indicator to obtain the liquidity value of the technical indicator; S21137, using the health sequence of each technical indicator as a row vector, a health matrix is ​​constructed. S21138, Perform singular value decomposition on the indicator health matrix to obtain a set of singular values; each singular value in the set of singular values ​​corresponds to each row vector of the indicator health matrix; S21139, using the set of singular values ​​as weighting values, the liquidity values ​​of all technical indicators are weighted and summed to obtain the condition assessment value of the ship equipment.

[0011] The calculation expression for the local features is as follows: in, Let be the value of the i-th linear polynomial at time t. This represents the time corresponding to the median or mean of a linear polynomial. This represents the local feature corresponding to the i-th linear polynomial; The expression for calculating the health status of the indicator is as follows: in, Let i be the health index of the i-th linear polynomial. To prevent division by zero, a first preset constant, For the i-th linear polynomial at time... The value of .

[0012] The process of performing relevant function modeling on the set of comprehensive technical status values ​​of ship equipment and the set of crew job assignment information yields a ship equipment operation efficiency optimization model, including: S221, perform cluster analysis on the set of crew position arrangement information to obtain a set of arrangement information categories; the set of arrangement information categories includes several arrangement information categories and the crew allocation schemes contained therein; S222, based on the set of arrangement information categories, a family of related functions is constructed; S223, Based on the aforementioned family of related functions, a ship equipment operation estimation function is constructed; S224, using the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information, the unknown parameters of the ship equipment operation estimation function are solved to obtain the solved ship equipment operation estimation function; S225, Based on the ship equipment operation estimation function, a ship equipment operation efficiency optimization model is constructed.

[0013] A second aspect of the present invention discloses a dynamic iterative allocation device for crew positions, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the dynamic iterative allocation method for crew positions.

[0014] In a third aspect, the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a computer, are used to execute the dynamic iterative allocation method for crew positions.

[0015] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the dynamic iterative allocation method for crew positions.

[0016] The beneficial effects of this invention are as follows: This invention achieves a deep correlation between crew assignment and equipment operating status by collecting a set of ship equipment operating information based on a pre-set set of crew position allocation information, breaking the limitations of traditional static allocation. Instead of relying on fixed crew qualification information to formulate allocation schemes, this invention uses actual equipment operating data as the core basis, enabling real-time capture of changes in equipment technical indicators under different crew allocation schemes. This provides a dynamic and accurate data source for subsequent optimization of job allocation, ensuring that job allocation adjustments always align with equipment operating needs.

[0017] This invention, when quantitatively assessing the technical status of ship equipment operation information sets, comprehensively considers multiple technical indicators such as equipment power, efficiency, vibration, and reliability. It collects operational status value sequences of these technical indicators at preset time intervals using sensors, fully exploring the dynamic changes of these indicators over time. This invention performs segmented trend decomposition and linear fitting on the difference sequences of technical indicators, calculates indicator health based on local features, and reflects the dynamic changes in indicator status through flow values. Furthermore, it uses singular value decomposition to weightedly sum the flow values ​​of multiple technical indicators, forming a comprehensive ship equipment status assessment value. This avoids the bias caused by single-indicator or static data assessments, enabling the comprehensive technical status value of the equipment to accurately reflect the actual operating level.

[0018] This invention, in constructing a ship equipment operation efficiency optimization model, first performs cluster analysis on the set of crew position allocation information, grouping crew allocation schemes with similar characteristics into one category. Then, based on these categories, it constructs a family of correlation functions, effectively considering the category differences between different allocation schemes. When calculating the fusion distance value of the correlation function family, it comprehensively combines the Euclidean and Frobenius norm features of the corresponding vectors of the crew allocation schemes, incorporating the mean information of both norms. This allows the correlation functions to more accurately characterize the relationship between crew allocation schemes and equipment operation efficiency. Through this refined modeling approach, this invention significantly improves the adaptability and accuracy of the ship equipment operation estimation function, thereby ensuring that the obtained optimized crew position configuration information accurately matches the needs for improving equipment operation efficiency. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0020] To better understand the content of this invention, an embodiment is provided here.

[0021] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0022] In a first aspect, this invention discloses a method for dynamic iterative allocation of crew positions, comprising: S1, Under the preset set of crew position arrangement information, the set of ship equipment operation information is collected; S2, perform regression analysis and modeling on the set of crew position arrangement information and the set of ship equipment operation information to obtain the ship equipment operation efficiency optimization model; S3, Solve the ship equipment operation efficiency optimization model to obtain crew position optimization configuration information; the crew position optimization configuration information includes the crew number values ​​assigned to all ship equipment; The set of crew assignment information includes several crew allocation schemes; the crew allocation scheme includes the crew sequence number values ​​assigned to all ship equipment. The set of ship equipment operation information includes a subset of ship equipment operation status information corresponding to each crew allocation scheme; the subset of ship equipment operation status information includes a sequence of operation status values ​​for all technical indicators of each ship equipment. The process of collecting a set of ship equipment operation information under a preset set of crew position arrangement information involves collecting a subset of ship equipment operation status information corresponding to each crew member operation plan under each crew member allocation plan within the preset set of crew position arrangement information.

[0023] All technical specifications of the ship's equipment, including power specifications, efficiency specifications, vibration specifications, and reliability specifications, etc.; the power specifications include input power and output power; the efficiency specifications include thermal efficiency and volumetric efficiency, etc.

[0024] The sequence of operating status values ​​of all technical indicators of the ship's equipment is a sequence composed of several operating status values ​​of the technical indicators collected by corresponding types of sensors at preset time intervals. The regression analysis and modeling process performed on the set of crew position assignment information and the set of ship equipment operation information yields a ship equipment operation efficiency optimization model, including: S21, Perform a quantitative assessment of the technical status of the ship equipment operation information set to obtain a comprehensive technical status value set of the ship equipment; the comprehensive technical status value set of the ship equipment includes the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme; S22, perform relevant function modeling on the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information to obtain the ship equipment operation efficiency optimization model.

[0025] The process of performing a quantitative assessment of the technical status of the ship's equipment operation information set to obtain a comprehensive set of ship equipment technical status values ​​includes: S211, For each crew allocation scheme in the ship equipment operation information set, the technical status of the corresponding ship equipment operation status information subset is quantitatively evaluated to obtain the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme. S212, using the comprehensive technical status values ​​of ship equipment corresponding to all crew allocation schemes, a set of comprehensive technical status values ​​of ship equipment is constructed.

[0026] The technical status quantification assessment is performed on each subset of ship equipment operation status information corresponding to each crew allocation scheme in the ship equipment operation information set to obtain the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme, including: S2111, obtain the status standard values ​​of all technical indicators for each ship's equipment; S2112, For each technical indicator of a ship equipment in the ship equipment operation status information subset corresponding to each crew allocation scheme, the sequence of operation status values ​​is subtracted from the status standard value of the corresponding technical indicator of the ship equipment to obtain the difference sequence of the technical indicator; S2113, Perform a status assessment on the difference sequence of all technical indicators of the ship equipment to obtain the status assessment value of the ship equipment; S2114, perform a fusion calculation on the status assessment values ​​of all ship equipment to obtain the comprehensive technical status value of the ship equipment corresponding to the crew allocation scheme.

[0027] The expression for the fusion calculation is: Where zj is the overall technical status value of the ship's equipment corresponding to the crew allocation scheme, and N is the total number of ship equipment. Let i be the condition assessment value of the i-th ship equipment. This is the average of the condition assessment values ​​for all ship equipment. The variance of the condition assessment values ​​for all ship equipment.

[0028] By integrating the calculation expression, the status assessment values ​​of all ship equipment are taken into account comprehensively, and the mean and variance of all equipment status assessment values ​​are introduced for adjustment. This expression, using an exponential function, effectively balances the impact of different equipment status assessment values ​​on the overall result, avoiding excessive interference from abnormal status assessment values ​​of individual equipment. This ensures that the calculated comprehensive technical status value of ship equipment corresponding to each crew allocation scheme truly reflects the overall operational level of all ship equipment under that scheme, providing accurate and comprehensive basic data support for the subsequent construction of ship equipment operational efficiency optimization models.

[0029] The condition assessment of the ship's equipment by performing a condition assessment on the difference sequence of all technical indicators of the ship's equipment, to obtain the condition assessment value of the ship's equipment, includes: S21131, For the difference sequence of each technical indicator of the ship equipment, perform segmented trend decomposition processing to obtain a number of corresponding segmented sequences; S21132, perform least squares fitting on the data points in each segmented sequence to obtain the corresponding linear polynomial; S21133, For each linear polynomial, perform local feature calculation to obtain the corresponding local features; S21134, calculate the health index of the local features of each linear polynomial to obtain the corresponding health index; S21135, using the indicator health of all linear polynomials of the difference sequence of a technical indicator, a sequence of indicator health is constructed; S21136, Calculate the liquidity value of the health sequence of the indicator to obtain the liquidity value of the technical indicator; S21137, using the health sequence of each technical indicator as a row vector, a health matrix is ​​constructed. S21138, Perform singular value decomposition on the indicator health matrix to obtain a set of singular values; each singular value in the set of singular values ​​corresponds to each row vector of the indicator health matrix; S21139, using the set of singular values ​​as weighting values, the liquidity values ​​of all technical indicators are weighted and summed to obtain the condition assessment value of the ship equipment.

[0030] Using the set of singular values ​​as weighting values, the liquidity values ​​of all technical indicators are weighted and summed. This is done by multiplying the singular value corresponding to each row vector of the indicator health matrix with the liquidity value of the indicator health sequence corresponding to the row vector, and then summing all the multiplication results to obtain the state assessment value of the ship equipment.

[0031] The linear polynomial is expressed as y = ax + b; The segmented trend decomposition process can be performed using the sliding linear regression method.

[0032] The calculation expression for the local features is as follows: in, Let be the value of the i-th linear polynomial at time t. This represents the time corresponding to the median or mean of a linear polynomial. This represents the local feature corresponding to the i-th linear polynomial; The local feature calculation expression calculates the derivative of the linear polynomial fitted to each segmented sequence at a specific time (the time corresponding to the median or mean of the linear polynomial). This expression can accurately capture the rate of change of the linear polynomial at key time points, thereby reflecting the dynamic trend of the corresponding technical indicator difference sequence at that stage, such as whether the indicator difference is accelerating or gradually decreasing. This provides crucial dynamic feature basis for subsequent calculation of the health of technical indicators, extending the assessment of the status of technical indicators from static values ​​to dynamic changes.

[0033] The expression for calculating the health status of the indicator is as follows: in, Let i be the health index of the i-th linear polynomial. To prevent division by zero, a first preset constant, For the i-th linear polynomial at time... The value of .

[0034] The expression for calculating the health of the technical indicators combines the local characteristics (rate of change) of a linear polynomial with the value of that polynomial at a specific moment, and introduces a preset constant to prevent division by zero. This expression combines the dynamic trend of the technical indicators with the current state value to quantify the health level of the technical indicators. For example, when local characteristics show that the differences in indicators are slowing down and the current value is small, the health level will be higher. At the same time, the introduction of the preset constant avoids calculation errors caused by the polynomial value being zero, ensuring that the health level of the indicators corresponding to each linear polynomial can be calculated stably and accurately, laying a reliable foundation for the subsequent construction of the indicator health level sequence.

[0035] The liquidity value is the variance of the first derivative of the FFT sequence corresponding to the sequence divided by the variance of the sequence. The process of performing relevant function modeling on the set of comprehensive technical status values ​​of ship equipment and the set of crew job assignment information yields a ship equipment operation efficiency optimization model, including: S221, perform cluster analysis on the set of crew position arrangement information to obtain a set of arrangement information categories; the set of arrangement information categories includes several arrangement information categories and the crew allocation schemes contained therein; S222, based on the set of arrangement information categories, a family of related functions is constructed; S223, Based on the aforementioned family of related functions, a ship equipment operation estimation function is constructed; S224, using the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information, the unknown parameters of the ship equipment operation estimation function are solved to obtain the solved ship equipment operation estimation function; S225, Based on the ship equipment operation estimation function, a ship equipment operation efficiency optimization model is constructed.

[0036] The clustering analysis of the crew position arrangement information set involves treating each crew allocation scheme in the crew position arrangement information set as a vector, and using vector clustering analysis to perform clustering analysis on all vectors to obtain a set of arrangement information categories; each element in the crew allocation scheme is treated as an element in the vector. The expression for the relevant family of functions is: in, Let x be the k-th correlation function in the correlation function family, and let x represent the input vector of the correlation function family. The first unknown parameter of the k-th correlation function is represented by the elements of the input vector, which characterize the crew number values ​​assigned to each ship's equipment. Let be the geometric center vector of the corresponding vectors of all crew allocation schemes contained in the k-th arrangement information category in the arrangement information category set. The fusion distance value is the corresponding vector of all crew allocation schemes included in the k-th arrangement information category; This invention utilizes a family of related functions to construct an exponential function based on the geometric center vector of each arrangement information category obtained through clustering, combined with the fusion distance value of that category and unknown parameters. This expression accurately characterizes the correlation strength between different crew allocation schemes (input vectors) and equipment operating performance. The closer the crew allocation scheme is to the category center vector, the stronger the correlation, consistent with the real-world pattern where similar allocation schemes have similar impacts on equipment performance. Furthermore, the introduction of unknown parameters reserves space for subsequent model solving using actual data, enabling the family of related functions to adapt to different ship equipment types and crew configurations, thus improving the model's scenario adaptability.

[0037] The expression for calculating the fusion distance value is: Where N1 is the total number of crew allocation schemes included in the k-th arrangement information category. This represents the vector corresponding to the i-th crew allocation scheme contained in the k-th arrangement information category. and They represent The Euclidean norm and the Frobenius norm, and Let represent the mean of the Euclidean norm and the mean of all Frobenius norms of the corresponding vectors of all crew allocation schemes included in the k-th arrangement information category, respectively.

[0038] The expression for calculating the fusion distance value incorporates the Euclidean and Frobenius norms of the vectors corresponding to crew allocation schemes, and combines the means of both norms, adjusting their weights through an exponential function. This expression comprehensively utilizes the advantages of both norms. The Euclidean norm reflects the overall magnitude characteristics of the vector, while the Frobenius norm reflects the distribution characteristics of the vector elements, comprehensively describing the attributes of the crew allocation scheme vectors. The introduction of the means of both norms balances the differences between different allocation scheme vectors, and the use of the exponential function ensures a reasonable weight allocation of the two norms during fusion, avoiding a single norm dominating the calculation results. The final fusion distance value accurately characterizes the features of each arrangement information category, providing reliable parameter support for the relevant function family and improving the model's accuracy in depicting the relationship between crew allocation schemes and equipment performance.

[0039] The expression for the reaction result information prediction model is: in, Let y be the second unknown parameter of the k-th correlation function, K be the total number of correlation functions, and y be the estimated comprehensive technical status value of the ship's equipment.

[0040] The parameters of the ship equipment operation efficiency optimization model can be solved using the least squares algorithm or the particle filter algorithm.

[0041] The calculation of the geometric center vector can be achieved by using the geometric mean method or by calculating the mean of a vector.

[0042] The process of using the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information to solve for the unknown parameters of the ship equipment operation estimation function, and obtaining the solved ship equipment operation estimation function, involves substituting the set of comprehensive technical status values ​​of ship equipment as the output value y and the set of crew job arrangement information as the input vector x into the ship equipment operation estimation function, using least squares estimation methods to estimate all the first and second unknown parameters, and then substituting the first and second unknown parameters into the ship equipment operation estimation function to obtain the solved ship equipment operation estimation function.

[0043] The expression for the ship equipment operation efficiency optimization model is as follows: in, The information for optimizing the allocation of crew positions is expressed as a vector. Each element of the vector is the crew number assigned to each piece of equipment on the ship. E is a preset modulus threshold, which can be the average of the sum of all crew number values ​​multiplied by 1.2N.

[0044] This invention constructs a ship equipment operation efficiency optimization model and solves it to obtain optimal crew position configuration information. This model assigns each piece of ship equipment to the most suitable crew member for operation, ensuring that the equipment maintains better technical performance under appropriate crew operation. This reduces the risk of equipment failure due to mismatch between personnel and positions, and improves equipment operating efficiency and reliability. Furthermore, the dynamic iterative nature of this invention continuously updates the job configuration based on equipment operation information during ship navigation, ensuring that the overall ship operation efficiency remains at a high level, providing strong support for safe navigation and efficient operation.

[0045] In all embodiments of the present invention, all computational expressions or mathematical functions have undergone dimensionless processing of the variables involved before calculation.

[0046] In all embodiments of the present invention, the values ​​of the independent variables in the input of all computational expressions or mathematical functions meet the reasonable requirements of the input range of the computational expressions or mathematical functions, and can ensure that the computational expressions or mathematical functions can be calculated smoothly without violating physical laws or mathematical rules.

[0047] A second aspect of the present invention discloses a dynamic iterative allocation device for crew positions, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the dynamic iterative allocation method for crew positions.

[0048] In a third aspect, the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a computer, are used to execute the dynamic iterative allocation method for crew positions.

[0049] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the dynamic iterative allocation method for crew positions.

[0050] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for dynamic iterative allocation of crew positions, characterized in that, include: S1, Under a preset set of crew position arrangement information, a set of ship equipment operation information is collected; the set of crew position arrangement information includes several crew allocation schemes; the crew allocation scheme includes the crew sequence number value assigned to all ship equipment; the set of ship equipment operation information includes a subset of ship equipment operation status information corresponding to each crew allocation scheme; the subset of ship equipment operation status information includes a sequence of operation status values ​​for all technical indicators of each ship equipment; S2, perform regression analysis and modeling on the set of crew position arrangement information and the set of ship equipment operation information to obtain the ship equipment operation efficiency optimization model; S3, Solve the ship equipment operation efficiency optimization model to obtain crew position optimization configuration information; the crew position optimization configuration information includes the crew number values ​​assigned to all ship equipment.

2. The dynamic iterative allocation method for crew positions as described in claim 1, characterized in that, The regression analysis and modeling process performed on the set of crew position assignment information and the set of ship equipment operation information yields a ship equipment operation efficiency optimization model, including: S21, Perform a quantitative assessment of the technical status of the ship equipment operation information set to obtain a comprehensive technical status value set of the ship equipment; the comprehensive technical status value set of the ship equipment includes the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme; S22, perform relevant function modeling on the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information to obtain the ship equipment operation efficiency optimization model.

3. The dynamic iterative allocation method for crew positions as described in claim 2, characterized in that, The process of performing a quantitative assessment of the technical status of the ship's equipment operation information set to obtain a comprehensive set of ship equipment technical status values ​​includes: S211, For each crew allocation scheme in the ship equipment operation information set, the technical status of the corresponding ship equipment operation status information subset is quantitatively evaluated to obtain the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme. S212, using the comprehensive technical status values ​​of ship equipment corresponding to all crew allocation schemes, a set of comprehensive technical status values ​​of ship equipment is constructed.

4. The dynamic iterative allocation method for crew positions as described in claim 3, characterized in that, The technical status quantification assessment is performed on each subset of ship equipment operation status information corresponding to each crew allocation scheme in the ship equipment operation information set to obtain the comprehensive technical status value of the ship equipment corresponding to each crew allocation scheme, including: S2111, obtain the status standard values ​​of all technical indicators for each ship's equipment; S2112, For each technical indicator of a ship equipment in the ship equipment operation status information subset corresponding to each crew allocation scheme, the sequence of operation status values ​​is subtracted from the status standard value of the corresponding technical indicator of the ship equipment to obtain the difference sequence of the technical indicator; S2113, Perform a status assessment on the difference sequence of all technical indicators of the ship equipment to obtain the status assessment value of the ship equipment; S2114, perform a fusion calculation on the status assessment values ​​of all ship equipment to obtain the comprehensive technical status value of the ship equipment corresponding to the crew allocation scheme.

5. The dynamic iterative allocation method for crew positions as described in claim 4, characterized in that, The condition assessment of the ship's equipment by performing a condition assessment on the difference sequence of all technical indicators of the ship's equipment, to obtain the condition assessment value of the ship's equipment, includes: S21131, For the difference sequence of each technical indicator of the ship equipment, perform segmented trend decomposition processing to obtain a number of corresponding segmented sequences; S21132, perform least squares fitting on the data points in each segmented sequence to obtain the corresponding linear polynomial; S21133, For each linear polynomial, perform local feature calculation to obtain the corresponding local features; S21134, calculate the health index of the local features of each linear polynomial to obtain the corresponding health index; S21135, using the indicator health of all linear polynomials of the difference sequence of a technical indicator, a sequence of indicator health is constructed; S21136, Calculate the liquidity value of the health sequence of the indicator to obtain the liquidity value of the technical indicator; S21137, using the health sequence of each technical indicator as a row vector, a health matrix is ​​constructed. S21138, Perform singular value decomposition on the indicator health matrix to obtain a set of singular values; each singular value in the set of singular values ​​corresponds to each row vector of the indicator health matrix; S21139, using the set of singular values ​​as weighting values, the liquidity values ​​of all technical indicators are weighted and summed to obtain the condition assessment value of the ship equipment.

6. The dynamic iterative allocation method for crew positions as described in claim 5, characterized in that, The calculation expression for the local features is as follows: in, Let be the value of the i-th linear polynomial at time t. This represents the time corresponding to the median or mean of a linear polynomial. This represents the local feature corresponding to the i-th linear polynomial; The expression for calculating the health status of the indicator is as follows: in, Let i be the health index of the i-th linear polynomial. To prevent division by zero, a first preset constant, For the i-th linear polynomial at time... The value of .

7. The dynamic iterative allocation method for crew positions as described in claim 2, characterized in that, The process of performing relevant function modeling on the set of comprehensive technical status values ​​of ship equipment and the set of crew job assignment information yields a ship equipment operation efficiency optimization model, including: S221, perform cluster analysis on the set of crew position arrangement information to obtain a set of arrangement information categories; the set of arrangement information categories includes several arrangement information categories and the crew allocation schemes contained therein; S222, based on the set of arrangement information categories, a family of related functions is constructed; S223, Based on the aforementioned family of related functions, a ship equipment operation estimation function is constructed; S224, using the set of comprehensive technical status values ​​of ship equipment and the set of crew job arrangement information, the unknown parameters of the ship equipment operation estimation function are solved to obtain the solved ship equipment operation estimation function; S225, Based on the ship equipment operation estimation function, a ship equipment operation efficiency optimization model is constructed.

8. A dynamic iterative allocation device for crew positions, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the dynamic iterative allocation method for crew positions as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked by a computer, are used to execute the dynamic iterative allocation method for crew positions as described in any one of claims 1 to 7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the dynamic iterative allocation method for crew positions as described in any one of claims 1 to 7.

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