A waterway transportation safety supervision calculation method and system

Abstract

The present invention discloses a waterway transportation safety supervision calculation method and system, which relates to the technical field of waterway transportation. It includes collecting multi-dimensional data related to waterway transportation safety supervision and performing preprocessing to generate a multi-dimensional feature vector matrix; based on the multi-dimensional feature vector matrix, constructing a multi-dimensional input-output model, calculating the safety benefits of each sub-industry, and integrating them to obtain the overall industry safety benefits; using the overall industry safety benefits to construct an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, analyzing the comprehensive benefits of safety supervision on multi-level economic nodes, and generating comprehensive benefit data; by constructing an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, quantitatively analyzing the diffusion benefits of safety supervision among network nodes and its comprehensive impact on multi-level economic nodes, thereby revealing the global value of safety supervision to the industry ecosystem.

Description

Technical Field

[0001] The present invention relates to the technical field of waterway transportation, and particularly to a method and system for calculating and supervising waterway transportation safety. Background Art

[0002] With the rapid development of the waterway transportation industry, the importance of safety supervision has become increasingly prominent. In recent years, the frequent occurrence of various accidents has not only caused huge economic losses but also had a serious impact on the ecological environment and social stability. Against this background, data-driven waterway transportation safety supervision technology has gradually become a research hotspot. By collecting and analyzing multi-dimensional data (such as capital investment, safety technology investment, historical accident records, and environmental risk data), it is possible to more comprehensively evaluate the impact of safety supervision on the economic and ecological benefits of the industry. However, existing technologies usually only focus on the safety supervision effect of a single dimension, such as the accident reduction rate or direct economic benefits, and it is difficult to comprehensively reflect the multi-level benefits of safety supervision in a complex economic and ecological network.

[0003] The existing technologies have the following deficiencies: First, they fail to effectively integrate multi-dimensional data to construct a dynamic input-output model, resulting in a lack of comprehensiveness in the evaluation of safety supervision benefits; second, they lack the economic and ecological network analysis of the waterway transportation industry and its upstream and downstream industrial chains, and cannot quantify the diffusion benefits of safety supervision in the industrial chain. In addition, the existing methods have insufficient dynamic adjustment ability for risk prediction and cannot optimize the safety benefit distribution mechanism according to future risk parameters. Therefore, how to comprehensively evaluate the multi-level benefits of safety supervision and optimize the benefit distribution of the upstream and downstream industrial chains through multi-dimensional data analysis and dynamic modeling has become an urgent technical problem to be solved. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for calculating and supervising waterway transportation safety to solve the problem of dynamically optimizing the safety benefits of the waterway transportation industry and their diffusion benefits in the economic and ecological network based on multi-dimensional data and risk prediction.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for calculating and supervising waterway transportation safety, which includes,

[0008] Collecting multi-dimensional data related to waterway transportation safety supervision and performing preprocessing to generate a multi-dimensional feature vector matrix;

[0009] Based on the multi-dimensional feature vector matrix, constructing a multi-dimensional input-output model, calculating the safety benefits of each sub-industry, and integrating to obtain the overall industry safety benefit;

[0010] Utilize the overall safety benefits of the industry to construct an economic ecological network for the waterway transportation industry and its upstream and downstream industrial chains, analyze the comprehensive benefits of safety supervision on multi-level economic nodes, and generate comprehensive benefit data;

[0011] Based on the comprehensive benefit data and risk-related data in the multi-dimensional data, construct a risk prediction model to predict future risk parameters;

[0012] According to the predicted future risk parameters, dynamically update the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network, and generate the final measurement report on waterway transportation safety supervision.

[0013] As a preferred solution of the waterway transportation safety supervision measurement method described in the present invention, wherein: the multi-dimensional data includes industry input-output data, economic ecological network data, and risk-related data;

[0014] The industry input-output data includes capital investment, safety technology investment, transportation volume, and total output value; the economic ecological network data includes capital flow and safety supervision impact data; the risk-related data includes historical accident records and environmental risk data;

[0015] The preprocessing includes data standardization, data denoising, and missing value filling;

[0016] The generation of the multi-dimensional feature vector matrix refers to transforming the preprocessed data to form a multi-dimensional feature vector matrix.

[0017] As a preferred solution of the waterway transportation safety supervision measurement method described in the present invention, wherein: based on the multi-dimensional feature vector matrix, construct a multi-dimensional input-output model, calculate the safety benefit contribution values of each sub-industry, and integrate to obtain the overall safety benefit of the industry, including the following steps,

[0018] Extract capital flow data, total output value, safety input intensity, and transportation volume from the multi-dimensional feature vector matrix;

[0019] Based on the extracted capital flow data and total output value, construct an input coefficient matrix;

[0020] Integrate the extracted safety input intensity and transportation volume to form a safety supervision input vector;

[0021] Based on historical market demand, construct a demand vector;

[0022] Construct a multi-dimensional input-output model, and based on the input coefficient matrix, safety supervision input vector, and demand vector, calculate the total output of the sub-industry. The expression is:

[0023] X=(I - A) -1 (Y + B·S);

[0024] Among them, X represents the total output of the sub - industry, I represents the identity matrix, A represents the input - coefficient matrix, B represents the vector of safety - supervision input, Y represents the demand vector, and S represents the total amount of safety - supervision input;

[0025] According to the total output of the sub - industry, the value - added benefit is defined as the growth of the industry output value brought by safety supervision, and the expression is:

[0026] C v =δ v ·(X v -E v );

[0027] Among them, C v represents the value - added benefit of the v - th sub - industry, δ v represents the value - added coefficient of the v - th sub - industry, X v represents the total output of the v - th sub - industry under the action of safety supervision, and E v represents the benchmark output of the v - th sub - industry without the action of safety supervision;

[0028] The loss - reduction benefit is defined as the reduced value of the economic loss brought by reducing accidents due to safety supervision, and the expression is:

[0029]

[0030] Among them, D v represents the loss - reduction benefit of the v - th sub - industry, P k represents the occurrence probability of the k - th type of accident without safety supervision, F k represents the occurrence probability of the k - th type of accident with safety supervision, represents the average economic loss caused by the k - th type of accident to the v - th sub - industry, and m represents the total number of accidents;

[0031] Add the value - added benefit and the loss - reduction benefit to obtain the safety - benefit contribution of each sub - industry;

[0032] Sum up the safety - benefit contribution values of all sub - industries to obtain the overall safety benefit of the industry.

[0033] As a preferred scheme of the water - transportation safety - supervision measurement method described in the present invention, wherein: using the overall safety benefit of the industry, constructing the economic - ecological network of the water - transportation industry and its upstream and downstream industrial chains includes the following steps,

[0034] Define the water - transportation sub - industry and the main upstream and downstream industries as network nodes;

[0035] According to the network nodes and the industry safety benefit, calculate the influence - propagation intensity between network nodes in safety supervision, and the expression is:

[0036]

[0037] Among them, A ij represents the intensity of the safety supervision impact of node i on node j, and R i represents the overall safety benefit of node i, represents the diffusion coefficient of safety supervision, and W ij represents the original relationship strength between node i and node j, and B k represents the safety benefit contribution value of the k-th node;

[0038] Based on the intensity of the safety supervision impact between nodes in the network, an adjacency matrix is constructed to form an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains.

[0039] As a preferred solution of the waterway transportation safety supervision measurement method described in the present invention, wherein: analyzing the comprehensive benefits of safety supervision on multi-level economic nodes and generating comprehensive benefit data includes the following steps

[0040] Using the network diffusion model, calculate the benefits of safety supervision input in the economic ecological network, and the expression is:

[0041]

[0042] Among them, represents the comprehensive benefit of node j after the t-th diffusion, n represents the total number of nodes in the network, α represents the diffusion attenuation coefficient, represents the benefit of node i in the (t - 1)-th diffusion, and β j represents the self-generation benefit coefficient of node j, and R j represents the overall safety benefit of node j;

[0043] Based on the benefits of safety supervision input in the economic ecological network, divide the economic ecological network into a direct impact layer, an indirect impact layer, and an external social benefit layer, and calculate the benefits of each layer;

[0044] Integrate the benefits of all layers to obtain the comprehensive benefit data of safety supervision.

[0045] As a preferred solution of the waterway transportation safety supervision measurement method described in the present invention, wherein: based on the comprehensive benefit data and the risk-related data in the multi-dimensional data, constructing a risk prediction model to predict future risk parameters includes the following steps

[0046] Based on the comprehensive benefit data of safety supervision and the historical accident records and environmental risk data in the risk-related data, calculate the comprehensive risk prediction value, and the expression is:

[0047]

[0048] Among them, H t represents the comprehensive risk prediction value at time t, and Pt-1,k The historical probability of the occurrence of the k-th type of accident at time t-1, L k represents the average economic loss value of the k-th type of accident, M represents the comprehensive benefit of safety supervision, Q represents the benchmark safety benefit of the industry, ∈ represents the smoothing coefficient, λ k (t) represents the influence weight of the risk of the k-th type of accident at time t on the comprehensive risk, φ represents the external environment influence coefficient, G t represents the environmental risk data at time t.

[0049] As a preferred solution of the waterway transportation safety supervision measurement method described in the present invention, wherein: according to the predicted future risk parameters, dynamically update the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network, and the steps for generating the final measurement report of waterway transportation safety supervision include the following

[0050] Map the predicted future risk parameters to the input-output model and the economic ecological network model;

[0051] Combined with the dynamic changes of the risk parameters, adjust the input coefficients between sub-industries in the input-output model in real time, and measure the dynamic contribution of different safety inputs to the economic benefits of the industry;

[0052] Dynamically optimize the distribution mechanism of multi-level benefits in the economic ecological network, and update the comprehensive benefits of the upstream and downstream industrial chains in real time;

[0053] Integrate the results of the input-output model and the economic ecological network model after dynamic adjustment to generate the final measurement report of waterway transportation safety supervision.

[0054] In a second aspect, the present invention provides a waterway transportation safety supervision measurement system, including

[0055] A data processing module, which collects multi-dimensional data related to waterway transportation safety supervision and performs preprocessing to generate a multi-dimensional feature vector matrix;

[0056] A benefit calculation module, which constructs a multi-dimensional input-output model based on the multi-dimensional feature vector matrix, calculates the safety benefits of each sub-industry, and integrates to obtain the overall safety benefit of the industry;

[0057] A network analysis module, which uses the overall safety benefit of the industry to construct an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, analyzes the comprehensive benefits of safety supervision on multi-level economic nodes, and generates comprehensive benefit data;

[0058] A risk prediction module, which constructs a risk prediction model based on the comprehensive benefit data and the risk-related data in the multi-dimensional data, and predicts the future risk parameters;

[0059] A dynamic update module dynamically updates the safety benefit contribution values of each sub - industry and the multi - level benefit data of the ecological network according to the predicted future risk parameters, and generates a final measurement report for waterway transportation safety supervision.

[0060] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the waterway transportation safety supervision measurement method described in the first aspect of the present invention is implemented.

[0061] In a fourth aspect, the present invention provides a computer - readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the waterway transportation safety supervision measurement method described in the first aspect of the present invention is implemented.

[0062] The beneficial effects of the present invention are as follows: By collecting and pre - processing multi - dimensional data related to waterway transportation safety supervision, a multi - dimensional feature vector matrix is generated, providing a standardized and complete data basis for subsequent modeling; Based on the constructed multi - dimensional input - output model, not only can the safety benefit contribution values of each sub - industry be calculated, but also the overall safety benefit of the industry can be integrated, providing a scientific basis for evaluating the direct economic benefits of safety supervision; By constructing an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, the diffusion benefits of safety supervision among network nodes and its comprehensive impact on multi - level economic nodes are quantitatively analyzed, thus revealing the overall value of safety supervision to the industry ecosystem; A risk prediction model is constructed, which can dynamically predict future risk parameters and optimize the input - output model and the economic ecological network accordingly. Description of the Drawings

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0064] Figure 1 It is a flowchart of the waterway transportation safety supervision measurement method in Embodiment 1.

[0065] Figure 2 It is a system diagram of the waterway transportation safety supervision measurement system in Embodiment 1. Detailed Embodiments

[0066] In order to make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0067] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Persons skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0068] Secondly, as used herein, "an embodiment" or "embodiments" refer to specific features, structures, or characteristics that may be included in at least one implementation of the present invention. The appearances of "in an embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other.

[0069] Example 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a method for calculating and supervising waterway transportation safety, including the following steps:

[0070] S1. Collect multi-dimensional data related to waterway transportation safety supervision and perform preprocessing to generate a multi-dimensional feature vector matrix.

[0071] S1.1. The multi-dimensional data includes industry input-output data, economic ecological network data, and risk-related data.

[0072] S1.1.1 The industry input-output data includes capital investment, safety technology investment, transportation volume, and total output value; the economic ecological network data includes capital flow and safety supervision impact data; the risk-related data includes historical accident records and environmental risk data.

[0073] Furthermore, the industry input-output data is obtained through statistical yearbooks, industry association reports, enterprise financial statements, and other means.

[0074] The economic ecological network data is obtained by analyzing the capital flow between enterprises, researching the supply chain, and evaluating the impact of safety supervision policies to obtain the upstream and downstream capital flow and supervision impact data.

[0075] The risk-related data is obtained by the historical accident record database, ship monitoring systems (such as AIS data), and meteorological, tidal, and traffic density data provided by environmental monitoring agencies to obtain historical accident and environmental risk data.

[0076] S1.2. The preprocessing includes data standardization, data denoising, and missing value filling; generating a multi-dimensional feature vector matrix means transforming the preprocessed data to form a multi-dimensional feature vector matrix.

[0077] Furthermore, the standardization process is to normalize and unify the units of the data so that data with different dimensions can be used in the same model.

[0078] Data denoising refers to cleaning outliers and noise to reduce data errors.

[0079] Missing value completion is to complete the missing values in the data to ensure data integrity.

[0080] S2. Based on the multi-dimensional feature vector matrix, construct a multi-dimensional input-output model, calculate the safety benefit contribution values of each sub-industry, and integrate to obtain the overall industry safety benefit.

[0081] S2.1. Extract the cash flow data, total output value, safety input intensity, and transportation volume from the multi-dimensional feature vector matrix; based on the extracted cash flow data and total output value, construct an input coefficient matrix.

[0082] It should be noted that the input coefficient matrix describes the economic connections between sub-industries and provides a basic framework for calculating safety benefits; it clarifies the proportional relationship between cash flow and total output value and quantifies the economic dependence degree between sub-industries.

[0083] S2.2. Integrate the extracted safety input intensity and transportation volume to form a safety supervision input vector; based on historical market demand, construct a demand vector; construct a multi-dimensional input-output model, and based on the input coefficient matrix, safety supervision input vector, and demand vector, calculate the total output of the sub-industry. The expression is:

[0084] X = (I - A) -1 (Y + B·S);

[0085] Among them, X represents the total output of the sub-industry, I represents the identity matrix, A represents the input coefficient matrix, B represents the safety supervision input vector, Y represents the demand vector, and S represents the total safety supervision input.

[0086] It should be noted that the safety supervision input vector clearly quantifies the input efforts of different sub-industries for safety supervision, accurately reflects changes in market demand, provides an output estimate based on safety input and market demand, and dynamically reflects the impact of safety supervision on industry output.

[0087] S2.3. According to the total output of the sub-industry, define the value-added benefit as the industry output value growth brought by safety supervision. The expression is:

[0088] C v = δ v ·(X v - E v );

[0089] Among them, C v represents the value-added benefit of the v-th sub-industry, δ v represents the value-added coefficient of the v-th sub-industry, X vDenote the total output of the \(v\)-th sub-industry under safety supervision as \(E\). v Denote the benchmark output of the \(v\)-th sub-industry without safety supervision.

[0090] Define the loss reduction benefit as the reduced economic loss caused by the reduction of accidents due to safety supervision, and the expression is:[[]]END]]

[0091]

[0092] where \(D\) v denotes the loss reduction benefit of the \(v\)-th sub-industry, \(P\) k denotes the occurrence probability of the \(k\)-th type of accident without safety supervision, \(F\) k denotes the occurrence probability of the \(k\)-th type of accident under safety supervision, represents the average economic loss caused by the \(k\)-th type of accident to the \(v\)-th sub-industry, and \(m\) represents the total number of accidents.

[0093] It should be noted that clarifying the direct economic benefits of safety supervision on the growth of industrial output is conducive to analyzing the positive incentive effect of supervision policies, and the loss reduction benefit intuitively quantifies the reduction effect of safety supervision on accident risks.

[0094] S2.4. Add the value-added benefit and the loss reduction benefit to obtain the safety benefit contribution of each sub-industry.

[0095] Specifically, the expression of the safety benefit contribution value of the sub-industry is:[[]]END]]

[0096] \(B\) v \(=\)\(C\) v \(+\)\(D\) v ;

[0097] where \(B\) v denotes the safety benefit contribution value of the \(v\)-th sub-industry.

[0098] S2.5. Aggregate the safety benefit contribution values of all sub-industries to obtain the overall safety benefit of the industry.

[0099] Specifically, the expression of the overall safety benefit of the industry is:[[]]END]]

[0100]

[0101] where \(M\) denotes the overall safety benefit of the industry, and \(n\) denotes the total number of sub-industries.

[0102] S3. Utilize the overall safety benefit of the industry to construct the economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, analyze the comprehensive benefits of safety supervision on multi-level economic nodes, and generate comprehensive benefit data.

[0103] S3.1. Define the waterway transportation sub - industry and its main upstream and downstream industries as network nodes; calculate the influence propagation intensity between network nodes in safety supervision according to the network nodes and industry safety benefits. The expression is:

[0104]

[0105] Among them, A ij represents the safety supervision influence intensity of node i on node j, R i represents the overall safety benefit of node i, represents the diffusion coefficient of safety supervision, W ij represents the original relationship intensity between node i and node j, B k represents the safety benefit contribution value of the k - th node;

[0106] Based on the safety supervision influence intensity between nodes in the network, construct an adjacency matrix to form the economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains.

[0107] It should be noted that the economic ecological network completely describes the economic associations of the waterway transportation industry and its upstream and downstream industries, providing a structured input for the subsequent diffusion model; the calculation of network attributes helps to identify core nodes and key connections, and optimize safety supervision strategies.

[0108] S3.2. Use the network diffusion model to calculate the benefits of safety supervision input in the economic ecological network. The expression is:

[0109]

[0110] Among them, represents the comprehensive benefit of node j after the t - th diffusion, N represents the total number of nodes in the network, α represents the diffusion attenuation coefficient, represents the benefit of node i in the (t - 1) - th diffusion, β j represents the self - generating benefit coefficient of node j, R j represents the overall safety benefit of node j.

[0111] Specifically, the network diffusion model performs iterative calculations to obtain the comprehensive benefit values of each node in the network. The operations are as follows:

[0112] Set the initial benefit of the node to zero, and perform iterations at time steps until the final benefit of the node converges to obtain the comprehensive benefit values of each node in the network.

[0113] It should be noted that the dynamic diffusion model accurately captures the propagation process of safety supervision benefits in the economic ecological network; considering attenuation and self - generating benefits, the model results are closer to the actual economic system.

[0114] S3.3. Divide the economic ecological network into a direct impact layer, an indirect impact layer, and an external social benefit layer based on the benefits of safety supervision input in the economic ecological network, and calculate the benefits of each layer; integrate the benefits of all layers to obtain the comprehensive benefit data of safety supervision.

[0115] Specifically, the direct impact layer refers to the upstream and downstream industries directly associated with the core nodes of the waterway transportation industry (such as logistics, energy).

[0116] The indirect impact layer refers to the industries further spread through the direct impact layer (such as consumer goods, financial services).

[0117] The external social benefit layer refers to the social fields spread to those indirectly related to the economic system (such as employment, environmental protection).

[0118] The calculation of the benefits of each layer is as follows:

[0119] The benefit of the direct impact layer aggregates the comprehensive benefit values of the nodes in this layer.

[0120] The benefit of the indirect impact layer aggregates the comprehensive benefit values of the nodes in this layer.

[0121] The benefit of the external social benefit layer quantifies the indirect contribution of safety supervision to social benefits, such as reducing the damage of accidents to the environment and public resources.

[0122] S4. Based on the comprehensive benefit data and the risk-related data in the multi-dimensional data, construct a risk prediction model to predict future risk parameters.

[0123] S4.1. Based on the comprehensive benefit data of safety supervision and the historical accident records and environmental risk data in the risk-related data, calculate the comprehensive risk prediction value, and the expression is:

[0124]

[0125] Among them, H t represents the comprehensive risk prediction value at time t, P t-1,k represents the historical probability of the occurrence of the k-th type of accident at time t-1, L k represents the average economic loss value of the k-th type of accident, M represents the comprehensive benefit of safety supervision, Q represents the benchmark safety benefit of the industry, ∈ represents the smoothing coefficient, λ k (t) represents the influence weight of the risk of the k-th type of accident at time t on the comprehensive risk, φ represents the external environment influence coefficient, G t represents the environmental risk data at time t.

[0126] Furthermore, the environmental risk data includes meteorological conditions (typhoon, heavy rain, etc.), waterway environment (siltation, congestion, etc.), and traffic flow, etc.

[0127] It should be noted that the combination of comprehensive benefits, historical accident probability, and environmental risk provides a comprehensive data basis for risk prediction.

[0128] S5. According to the predicted future risk parameters, dynamically update the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network, and generate the final measurement report for waterway transportation safety supervision.

[0129] S5.1. Map the predicted future risk parameters to the input-output model and the economic-ecological network model; combine the dynamic changes of the risk parameters, adjust the input coefficients between sub-industries in the input-output model in real time, measure the dynamic contribution of different safety inputs to the economic benefits of the industry; dynamically optimize the distribution mechanism of multi-level benefits in the economic-ecological network, and update the comprehensive benefits of the upstream and downstream industrial chains in real time; integrate the results of the dynamically adjusted input-output model and the economic-ecological network model to generate the final measurement report for waterway transportation safety supervision.

[0130] It should be noted that by mapping the risk parameters to the input model, the key variables of the model are dynamically adjusted, enabling it to have real-time response capabilities to risk changes; the risk-driven dynamic adjustment enhances the flexibility of the model and the accuracy of prediction; the dynamically adjusted input-output model is more in line with the actual operation status of the industry and reflects the actual impact of risks on the economic connections between upstream and downstream.

[0131] This embodiment also provides a waterway transportation safety supervision measurement system, including:

[0132] A data processing module that collects and preprocesses multi-dimensional data related to waterway transportation safety supervision to generate a multi-dimensional feature vector matrix; a benefit calculation module that constructs a multi-dimensional input-output model based on the multi-dimensional feature vector matrix, calculates the safety benefits of each sub-industry, and integrates them to obtain the overall safety benefit of the industry; a network analysis module that uses the overall safety benefit of the industry to construct an economic-ecological network of the waterway transportation industry and its upstream and downstream industrial chains, analyzes the comprehensive benefits of safety supervision on multi-level economic nodes, and generates comprehensive benefit data; a risk prediction module that constructs a risk prediction model based on the comprehensive benefit data and the risk-related data in the multi-dimensional data to predict future risk parameters; a dynamic update module that dynamically updates the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network according to the predicted future risk parameters, and generates the final measurement report for waterway transportation safety supervision.

[0133] This embodiment also provides a computer device applicable to the situation of the waterway transportation safety supervision measurement method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the waterway transportation safety supervision measurement method proposed in the above embodiment.

[0134] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0135] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the waterway transportation safety supervision calculation method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disks or optical discs.

[0136] In summary, the present invention collects and preprocesses multi-dimensional data related to waterway transportation safety supervision to generate a multi-dimensional feature vector matrix, providing a standardized and complete data basis for subsequent modeling; based on this, a multi-dimensional input-output model is constructed, which can not only calculate the safety benefit contribution values of each sub-industry, but also integrate to obtain the overall safety benefit of the industry, providing a scientific basis for evaluating the direct economic benefits of safety supervision; by constructing an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, quantitatively analyzes the diffusion benefits of safety supervision among network nodes and its comprehensive impact on multi-level economic nodes, thus revealing the overall value of safety supervision to the industry ecosystem; constructs a risk prediction model, which can dynamically predict future risk parameters and optimize the input-output model and economic ecological network accordingly.

[0137] Example 2. Referring to Table 1, this is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the waterway transportation safety supervision calculation method is given.

[0138] An experimental scenario based on simulated data was designed. The experiment simulated the performance of the waterway transportation industry under two conditions:

[0139] No safety supervision mode: No safety supervision measures are implemented, and only rely on the traditional management mode.

[0140] Implement safety supervision mode: Carry out supervision calculation and optimization according to the method of the present invention.

[0141] Specifically as shown in Table 1 below:

[0142] Table 1 Experimental data table of different supervision modes

[0143]

[0144] It can be seen from the table data that the method described in the present invention significantly improves the economic benefits of the waterway transportation industry when implementing safety supervision, and effectively reduces the accident rate, showing the following advantages:

[0145] After implementing safety supervision, the capital investment and safety technology investment in each sub-industry have increased significantly. For example, the capital investment in sub-industry 1 has increased from 5 million yuan to 6 million yuan, and the safety technology investment has increased from 500,000 yuan to 800,000 yuan. These investments directly bring the growth of transportation volume and total output value. For example, the transportation volume of sub-industry 1 has increased from 8 million tons to 8.5 million tons, and the total output value has increased from 1 billion yuan to 1.2 billion yuan.

[0146] Under the no safety supervision mode, the accident rates of all sub-industries are relatively high. For example, the accident rate of sub-industry 1 is 5.5%, and that of sub-industry 2 is 4.8%. Under the implementation of the safety supervision mode, the accident rates have dropped to 2.8% and 2.5% respectively, reflecting the effectiveness of the method of the present invention in safety management.

[0147] Under the no safety supervision mode, the value-added income and loss reduction income of all sub-industries are zero. While under the implementation of the safety supervision mode, the value-added income of sub-industry 1 is 200 million yuan, and the loss reduction income is 150 million yuan; the value-added income of sub-industry 2 is 250 million yuan, and the loss reduction income is 200 million yuan. This shows that the method of the present invention realizes significant economic benefits by increasing the economic output value and reducing accident losses.

[0148] The present invention constructs an economic ecological network, analyzes the comprehensive impact of safety supervision on the upstream and downstream industrial chains, and calculates the benefits from three dimensions: the direct impact layer, the indirect impact layer, and the external social benefit layer. The experimental results show that after the implementation of safety supervision, the overall safety benefits of the industry are significantly improved, and its impact spreads to upstream and downstream enterprises through the industrial chain, driving the optimization of the overall economic ecology.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A waterway transportation safety supervision calculation method, characterized in that: including collecting multi-dimensional data related to waterway transportation safety supervision and preprocessing it to generate a multi-dimensional feature vector matrix; constructing a multi-dimensional input-output model based on the multi-dimensional feature vector matrix, calculating the safety benefit contribution values of each sub-industry, and integrating them to obtain the overall industry safety benefit; using the overall industry safety benefit to construct an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, analyzing the comprehensive benefits of safety supervision on multi-level economic nodes, and generating comprehensive benefit data; constructing a risk prediction model based on the comprehensive benefit data and risk-related data in the multi-dimensional data to predict future risk parameters; dynamically updating the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network according to the predicted future risk parameters, and generating the final measurement report of waterway transportation safety supervision; constructing a multi-dimensional input-output model based on the multi-dimensional feature vector matrix, calculating the safety benefit contribution values of each sub-industry, and integrating them to obtain the overall industry safety benefit, including the following steps extracting capital flow data, total output value, safety input intensity, and transportation volume from the multi-dimensional feature vector matrix; constructing an input coefficient matrix based on the extracted capital flow data and total output value; integrating the extracted safety input intensity and transportation volume to form a safety supervision input vector; constructing a demand vector based on historical market demand; constructing a multi-dimensional input-output model, and calculating the total output of the sub-industry based on the input coefficient matrix, safety supervision input vector, and demand vector; defining value-added benefits and loss reduction benefits according to the total output of the sub-industry, and adding the value-added benefits and loss reduction benefits to obtain the safety benefit contribution of each sub-industry; summarizing the safety benefit contribution values of all sub-industries to obtain the overall industry safety benefit.

2. The waterway transportation safety supervision measurement method according to claim 1, wherein: The multi-dimensional data includes industry input-output data, economic ecological network data, and risk-related data; The industry input-output data includes capital investment, safety technology investment, transportation volume, and total output value; the economic ecological network data includes capital flow and safety supervision impact data; the risk-related data includes historical accident records and environmental risk data; The preprocessing includes data standardization, data denoising, and missing value filling; Generating the multi-dimensional feature vector matrix refers to transforming the preprocessed data to form a multi-dimensional feature vector matrix.

3. The waterway transportation safety supervision calculation method according to claim 2, wherein: The expression for the total output of the sub-industry is: X = (I - A) -1 (Y + B·S); where X represents the total output of the sub-industry, I represents the identity matrix, A represents the input coefficient matrix, B represents the safety supervision input vector, Y represents the demand vector, and S represents the total safety supervision input; Defining the value-added benefit according to the total output of the sub-industry, the expression is: C v = δ v ·(X v - E v ); Among them, C v represents the value-added income of the v-th sub-industry, and δ v represents the value-added coefficient of the v-th sub-industry, X v represents the total output of the v-th sub-industry under the role of safety supervision, and E v represents the benchmark output of the v-th sub-industry without the role of safety supervision; Defining the loss reduction benefit, the expression is: Among them, D v represents the loss of revenue of the v-th sub-industry, P k represents the occurrence probability of the k-th type of accident without safety supervision, F k represents the occurrence probability of the k-th type of accident with safety supervision, L k,v represents the average economic loss caused by the k-th type of accident to the v-th sub-industry, and m represents the total number of accidents.

4. The waterway transportation safety supervision calculation method according to claim 3, characterized in that: Using the overall industry safety benefit to construct an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, including the following steps defining the waterway transportation sub-industry and the main upstream and downstream industries as network nodes; calculating the influence propagation intensity between network nodes in safety supervision according to the network nodes and industry safety benefits, the expression is: Among them, A ij represents the intensity of the safety supervision influence of node i on node j, R i represents the overall safety benefit of node i, ρ represents the diffusion coefficient of safety supervision, W ij represents the original relationship intensity between node i and node j, B k represents the safety benefit contribution value of the k-th node; constructing an adjacency matrix based on the safety supervision influence intensity between nodes in the network to form an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains.

5. The waterway transportation safety supervision measurement method according to claim 4, characterized in that: Analyze the comprehensive benefits of safety supervision for multi-level economic nodes. The steps to generate comprehensive benefit data are as follows: Use the network diffusion model to calculate the benefits of safety supervision input in the economic ecological network. The expression is: Among them, represents the comprehensive benefit of node j after the t-th diffusion, N represents the total number of nodes in the network, α represents the diffusion attenuation coefficient, represents the benefit of node i in the (t-1)-th diffusion, β j represents the self-generation benefit coefficient of node j, R j represents the overall security benefit of node j; Based on the benefits of safety supervision input in the economic ecological network, divide the economic ecological network into a direct impact layer, an indirect impact layer, and an external social benefit layer, and calculate the benefits of each layer. Integrate the benefits of all layers to obtain the comprehensive benefit data of safety supervision.

6. The waterway transportation safety supervision calculation method according to claim 5, characterized in that: Based on the comprehensive benefit data and the risk-related data in the multi-dimensional data, construct a risk prediction model to predict future risk parameters. The steps include: Based on the comprehensive benefit data of safety supervision and the historical accident records and environmental risk data in the risk-related data, calculate the comprehensive risk prediction value. The expression is: Among them, H t represents the comprehensive risk prediction value at time t, P t-1,k represents the historical probability of the occurrence of the k-th type of accident at time t - 1, L k represents the average economic loss value of the k-th type of accident, M represents the comprehensive benefit of safety supervision, Q represents the benchmark safety benefit of the industry, ∈ represents the smoothing coefficient, λ k (t) represents the influence weight of the risk of the k-th type of accident on the comprehensive risk at time t, φ represents the external environment influence coefficient, G t represents the environmental risk data at time t, and m represents the total number of accidents.

7. The waterway transportation safety supervision measurement method according to claim 6, characterized in that: According to the predicted future risk parameters, dynamically update the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network, and generate the final measurement report of waterway transportation safety supervision. The steps include: Map the predicted future risk parameters to the input-output model and the economic ecological network model. Combined with the dynamic changes of the risk parameters, adjust the input coefficients between sub-industries in the input-output model in real time, and measure the dynamic contribution of different safety inputs to the economic benefits of the industry. Dynamically optimize the distribution mechanism of multi-level benefits in the economic ecological network, and update the comprehensive benefits of the upstream and downstream industrial chains in real time. Integrate the results of the dynamically adjusted input-output model and the economic ecological network model to generate the final measurement report of waterway transportation safety supervision.

8. A waterway transportation safety supervision calculation system, based on the waterway transportation safety supervision calculation method according to any one of claims 1 to 7, characterized in that: Including: A data processing module that collects multi-dimensional data related to waterway transportation safety supervision and performs preprocessing to generate a multi-dimensional feature vector matrix. A benefit calculation module that constructs a multi-dimensional input-output model based on the multi-dimensional feature vector matrix, calculates the safety benefits of each sub-industry, and integrates them to obtain the overall safety benefit of the industry. A network analysis module that uses the overall safety benefit of the industry to construct an economic ecological network of the waterway transportation industry and its upstream and downstream industrial chains, analyzes the comprehensive benefits of safety supervision for multi-level economic nodes, and generates comprehensive benefit data. A risk prediction module that constructs a risk prediction model based on the comprehensive benefit data and the risk-related data in the multi-dimensional data to predict future risk parameters. A dynamic update module that dynamically updates the safety benefit contribution values of each sub-industry and the multi-level benefit data of the ecological network according to the predicted future risk parameters, and generates the final measurement report of waterway transportation safety supervision.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the waterway transportation safety supervision measurement method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the waterway transportation safety supervision measurement method according to any one of claims 1 to 7.

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