Cable laying risk assessment method and system based on optimal state closeness

Abstract

The invention discloses a cable laying risk assessment method and system based on optimal state closeness. The method comprises the following steps: acquiring multi-dimensional laying state evaluation index data of a plurality of cable laying devices in a cable laying process; based on the obtained evaluation index data, calculating subjective and objective weights of each evaluation index, and performing combined weighting of the subjective weights and the objective weights to obtain an optimal combined weight; calculating the optimal state close degree of each cable laying device based on the optimal combination weight; and performing grading evaluation on the cable laying risk according to the optimal state close degree. The cable laying risk can be effectively evaluated, and cable laying faults and accidents are prevented.

Description

Technical Field

[0001] The invention belongs to the technical field of cable laying safety, and relates to a cable laying risk assessment method and system based on optimal state proximity. Background Art

[0002] During the construction of power cables, there are many devices involved and the work process is complex. The mechanical and electrical strength of the cable transmission, guidance, tightening and other links is high. The laying environment may have harsh and complex conditions such as high temperature and high humidity, which leads to risks in cable laying. The laying control method is only an open-loop mode of forward-reverse-start-stop, and the control basis depends on the judgment of the workers. The operation accuracy is low and the coordination between conveyors is poor. In terms of control completeness, the system lacks effective control of key variables such as side pressure and traction, the construction quality reliability is low, the emergency response capability is poor, and risk assessment is difficult.

[0003] At present, the risk assessment of the cable laying process mainly adopts the method of data over-limit alarm and manual judgment, which is unresponsive and inefficient. It is urgent to study the risk safety assessment of cable laying, so as to reduce the probability of cable laying failures and accidents and improve the safety of cable laying. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a cable laying risk assessment method and system based on optimal state proximity.

[0005] The present invention adopts the following technical solution.

[0006] A first aspect of the present invention provides a cable laying risk assessment method based on optimal state proximity, comprising:

[0007] S1. Obtain multi-dimensional laying status evaluation index data of multiple cable laying equipment during the cable laying process;

[0008] S2. Based on the obtained evaluation index data, the subjective and objective weights of each evaluation index are calculated, and the subjective weight and the objective weight are combined and weighted to obtain the optimal combined weight;

[0009] S3. Calculate the optimal state closeness of each cable laying equipment based on the optimal combination weight;

[0010] S4. Conduct a graded assessment of cable laying risks based on the degree of proximity to the optimal state.

[0011] Preferably, the evaluation indicators include traction, lateral pressure, service life, historical failure rate, operating environment temperature, and operating environment humidity.

[0012] Preferably, in S2, the subjective weight calculation process of each evaluation index is:

[0013] 1.1) Compare the importance of the evaluation indicators and construct the fuzzy complementary matrix A;

[0014] A=(a ij ) n×n (1)

[0015] Where: a ij is the importance value of index i over index j;

[0016] n is the number of evaluation indicators;

[0017] 1.2) Use the following formula to sum each row of the fuzzy complementary matrix A and calculate the fuzzy consistency matrix R:

[0018]

[0019] Where: r i is the sum of the i-th row of the fuzzy complementary matrix A;

[0020] r j is the sum of the jth row of the fuzzy complementary matrix A;

[0021] rr ij is the consistency result of row i and row j;

[0022] 1.3) After obtaining the fuzzy consistent matrix R, the subjective weight of each evaluation index is calculated using formula (5):

[0023]

[0024] Where α is the linear conversion coefficient: α=(n-1) / 2.

[0025] Preferably, in S2, the objective weight calculation process of each evaluation index is:

[0026] 2.1) Based on the multi-dimensional laying status evaluation index data of multiple cable laying equipment, an evaluation matrix is ​​constructed:

[0027]

[0028] Among them, f mn Data representing the nth equipment status evaluation index of the mth cable laying equipment;

[0029] 2.2) The types of state evaluation indicators are divided into positive indicators, negative indicators and interval indicators. The larger the value of the positive indicator, the better the indicator, and the opposite is true for the negative indicator. The interval indicator is the best indicator within the interval;

[0030] 2.3) Perform the following normalization processing on the various evaluation index data in the evaluation matrix:

[0031] Positive indicators, the normalized indicator data is:

[0032]

[0033] Negative indicators, the normalized indicator data is:

[0034]

[0035] For interval indicators, the normalized indicator data is:

[0036]

[0037] M=max{L l -min{f 1i ,…,f mi},max{f 1i ,…,f mi}-L h} (10)

[0038] Where L l , L h The values ​​represent the left and right boundaries of the interval, respectively;

[0039] 2.4) Obtain objective weights through indicator proportion and information entropy calculation:

[0040] The weight of the i-th indicator of the k-th cable laying equipment is:

[0041]

[0042] The information entropy of the i-th indicator is:

[0043]

[0044] The objective weight of the i-th indicator is:

[0045]

[0046] Preferably, in S2, the combination of subjective weight and objective weight is achieved by solving the following formula to obtain the optimal combination weight:

[0047] min||w * -w|| (14)

[0048]

[0049] Among them, |||| is the Euclidean distance between vectors;

[0050] w* is the optimal combination weight;

[0051] β1 and β2 are linear indicator coefficients;

[0052] w is the subjective weight vector, w′ is the objective weight vector;

[0053] w=(w1,w2,…,w n ), w′=(w′1,w′2,…,w′ n ), n is the number of evaluation indicators.

[0054] Preferably, in S3, the process of calculating the optimal state closeness of each cable laying equipment based on the optimal combination weight is:

[0055] 1) According to the standardization of the multi-dimensional laying status evaluation index data of multiple cable laying equipment, the standardized matrix Z is obtained:

[0056]

[0057] Among them, z ki is the standardized value of the i-th condition evaluation index data of the k-th cable laying equipment;

[0058] 2) Calculate the Euclidean distance of each evaluation index to the positive and negative ideal solutions based on matrix Z

[0059] In the formula, is a positive ideal solution; is a negative ideal solution; is the optimal combination weight;

[0060] 3) Calculate the optimal state closeness S of the k-th cable laying equipment k :

[0061]

[0062] Preferably, the positive ideal solution and the negative ideal solution are:

[0063]

[0064]

[0065] μ ki and σ ki are the mean value and standard deviation of the i-th condition evaluation index of the k-th cable laying equipment within one year of operation.

[0066] Preferably, in S4, the cable laying risk is graded and evaluated according to the optimal state proximity, including:

[0067] According to the optimal state closeness S of each cable laying equipment kThe risk level of each cable laying equipment is divided into the following values:

[0068] S k If it is greater than 0.7 and less than 1, the risk level of the corresponding cable laying equipment is considered to be extremely high;

[0069] S k If it is greater than 0.3 and less than or equal to 0.7, the risk level of the corresponding cable laying equipment is considered to be medium;

[0070] S k If it is less than or equal to 0.3, the risk level of the corresponding cable laying equipment is considered to be extremely low.

[0071] Preferably, in S4, the cable laying risk is graded and evaluated according to the optimal state proximity, including:

[0072] According to the optimal state closeness of each cable laying equipment, the optimal state closeness of the entire cable laying project is obtained:

[0073]

[0074] Among them, S k is the optimal state closeness of the k-th cable laying equipment;

[0075] S 总 If it is greater than 0.5 and less than 1, the risk level of the entire cable laying project is considered to be extremely high;

[0076] S 总 If it is greater than 0.2 and less than or equal to 0.5, the risk level of the entire cable laying project is considered to be medium;

[0077] S 总 If it is less than or equal to 0.2, the risk level of the entire cable laying project is considered to be extremely low.

[0078] The second aspect of the present invention proposes a cable laying risk assessment system based on optimal state proximity, comprising:

[0079] A data acquisition module, used to obtain multi-dimensional laying status evaluation index data of multiple cable laying equipment during the cable laying process;

[0080] A combined weighting module is used to calculate the subjective and objective weights of each evaluation index based on the acquired evaluation index data, and to perform combined weighting of the subjective weight and the objective weight to obtain the optimal combined weight;

[0081] A proximity calculation module, used to calculate the optimal state proximity of each cable laying equipment based on the optimal combination weight;

[0082] The risk assessment module is used to perform a graded assessment on the cable laying risk according to the degree of proximity to the optimal state.

[0083] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0084] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0085] Compared with the prior art, the beneficial effects of the present invention include at least:

[0086] The cable laying status evaluation index system of the present invention can comprehensively analyze the laying status detection data during the cable laying process, such as traction force and lateral pressure, the laying equipment operating condition data, such as service life and historical failure rate, and the operating environment data, such as temperature and humidity, to effectively evaluate the cable laying risks and avoid problems such as untimely response and low efficiency caused by manual judgment.

[0087] The present invention performs weight calculation on data indicators, combines the subjective weight calculated by the fuzzy hierarchical analysis method with the objective weight calculated by the entropy weight method through game theory to obtain the optimal combination weight, and avoids the shortcomings of inaccurate calculation by a single weighting method. The present invention adopts a combined weighting method to obtain the optimal combination weight, and through weight combination, the deviation between the optimal combination weight and the subjective weight vector is minimized, the loss of information is minimized, the deviation between the combination weight and other weights is reduced as much as possible, and the weighted result is as close to the actual result as possible, thereby achieving the purpose of screening the optimal combination weight.

[0088] The present invention calculates the distance between good and bad solutions of data indicators based on the optimal combination weight, and performs graded assessment of cable laying risks according to the degree of proximity to the optimal state, which can effectively assess the cable laying risks to prevent cable laying failures and accidents.

[0089] The optimal state closeness of the present invention uses the optimal combination weight, comprehensively considers the subjective and objective weights, and improves the accuracy of the evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 The present invention is a flowchart of a cable laying risk assessment method based on optimal state proximity. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0092] Embodiment 1 of the present invention provides a cable laying risk assessment method based on optimal state proximity, such as Figure 1 As shown, the method includes:

[0093] S1. Obtain multi-dimensional laying status evaluation index data of multiple cable laying equipment during the cable laying process;

[0094] Further preferably, the laying status detection data of multiple devices during the cable laying process are obtained, such as traction force f1(t), side pressure f2(t), laying equipment operating condition data such as service life f3(t), historical failure rate f4(t), operating environment data such as temperature f5(t), humidity f6(t), and f1~f6 together constitute a risk assessment index system.

[0095] Assuming that m devices (i.e., m evaluation targets) are used in the cable laying process, f11 represents the traction force of the first device, fm1 represents the traction force of the mth device, and so on.

[0096] S2. Based on the obtained evaluation index data, the subjective and objective weights of each evaluation index are calculated, and the subjective weight and the objective weight are combined and weighted to obtain the optimal combined weight;

[0097] Further preferably, the fuzzy analytic hierarchy process and entropy weight method are used to calculate the weight of each indicator, and then game theory is used to perform subjective and objective combined weighting;

[0098] 1) Fuzzy analytic hierarchy process adds fuzzy mathematics theory to the analytic hierarchy process and constructs a fuzzy consistent judgment matrix to compare the importance of each indicator, which can reduce the influence of subjective factors.

[0099] 1.1) According to the constructed cable laying risk assessment index system, the risk indicators are constructed into a fuzzy complementary matrix A according to the indicator importance comparison principle in Table 1.

[0100] Table 1 Principles of importance comparison

[0101]

[0102] A=(a ij ) n×n (1)

[0103] Where: a ij is the importance value of indicator i to indicator j. According to Table 1, according to the degree of importance comparison, the latter is extremely important than the former, the latter is obviously more important than the former, the former is equally important as the latter, the former is obviously more important than the latter, and the former is extremely important than the latter. The values ​​are 0.1, 0.3, 0.5, 0.7, and 0.9 respectively;

[0104] n is the number of evaluation indicators, and in this embodiment n=6.

[0105] 1.2) Use the following formula to sum each row of the fuzzy complementary matrix A and calculate the fuzzy consistency matrix R:

[0106]

[0107] R=(rr ij ) n×n (4)

[0108] Where: r i is the sum of the i-th row of the fuzzy complementary matrix A; r j is the sum of the jth row of the fuzzy complementary matrix A;

[0109] rr ij is the consistency result of the i-th row and the j-th row, forming the fuzzy consistency matrix R;

[0110] 1.3) After obtaining the fuzzy consistent matrix R, use formula (5) to calculate the weight of each index:

[0111]

[0112] Where α is the linear conversion coefficient: α=(n-1) / 2.

[0113] 2) Entropy weight method is an objective weighting method, which determines the weight of the indicator according to the amount of information contained in the indicator. The smaller the entropy of the indicator, the greater its degree of variation, the more information it can provide, and the greater its weight in the comprehensive evaluation.

[0114] 2.1) For n indicators (6 indicators in this embodiment) of m devices (i.e. m evaluation objects) in the cable laying process, the evaluation matrix is:

[0115]

[0116] 2.2) Due to the different measurement units of some indicators in the evaluation system, they can be divided into positive indicators, negative indicators and interval indicators. The larger the indicator value of the positive indicator, the better the indicator, and the opposite is true for the negative indicator. The interval indicator is the best indicator within the interval. In this embodiment, traction, lateral pressure, service life, operating environment temperature, and operating environment humidity are interval indicators, and the historical failure rate is a negative indicator.

[0117] 2.3) For various indicators, they need to be normalized before comprehensive evaluation to reduce the impact of different indicator measurements on the evaluation results.

[0118] Positive indicators:

[0119]

[0120] Negative indicators:

[0121]

[0122] Interval indicators:

[0123]

[0124] M=max{L l -min{f 1i ,…,f mi},max{f 1i ,…,f mi}-L h} (10)

[0125] Where L l , L h The values ​​represent the left and right boundaries of the interval, respectively;

[0126] 2.4) The weight of the i-th indicator of the k-th cable laying equipment (k-th evaluation target) is

[0127]

[0128] The information entropy of the i-th indicator is:

[0129]

[0130] The objective weight of the i-th indicator is:

[0131]

[0132] 3) To avoid the shortcomings of the above two single weighting methods, the present invention uses game theory to combine the subjective weights calculated by the fuzzy hierarchical analysis method with the objective weights calculated by the entropy weight method. The basic idea of ​​the game theory combined weighting method is to minimize the deviation between the combined weights and other weights as much as possible, so as to achieve the purpose of screening the optimal combined weights. Specifically, consistency and compromise are sought between different weights to minimize the deviation between w* and w, that is:

[0133] min||w * -w|| (14)

[0134]

[0135] Among them, |||| is the Euclidean distance between vectors;

[0136] w* is the optimal combination weight;

[0137] β1 and β2 are linear index coefficients; their values ​​can be calculated using optimization methods such as gradient descent so that the deviation between w* and w is minimized when equation (14) is satisfied.

[0138] w is the subjective weight vector, w′ is the objective weight vector;

[0139] w=(w1,w2,…,w n ), w′=(w′1,w′2,…,w′ n ), n is the number of evaluation indicators.

[0140] S3. Calculate the optimal state closeness of each cable laying equipment based on the optimal combination weight;

[0141] Further preferably, the distance and closeness of the good and bad solutions of the data indicators are calculated based on the optimal combination weights, as follows:

[0142] 1) Standardize the judgment matrix composed of the normalized original data, and let the standardized matrix be Z.

[0143]

[0144] Among them, z ki is the standardized value of the i-th condition evaluation index data of the k-th cable laying equipment;

[0145] 2) Euclidean distance from each evaluation index to the positive and negative ideal solutions for:

[0146]

[0147] In the formula is a positive ideal solution; is a negative ideal solution; is the optimal combination weight of the i-th state evaluation index, and the subscripts represent the components of the vector;

[0148] For the traction, lateral pressure, service life, historical failure rate, operating environment temperature, and operating environment humidity indicators, the average value of the equipment indicator within one year of operation μ ki and standard deviation σ ki , then the positive ideal solution and negative ideal solution are:

[0149]

[0150] 3) Use closeness S k Indicates the degree to which each cable laying equipment is close to the optimal state. The closeness S of the kth cable laying equipment k for:

[0151]

[0152] The closeness is usually between 0 and 1. k =1 indicates that the cable laying equipment is in the best operating state. k When =0, it indicates that the operating status of the cable laying equipment is the worst.

[0153] S4. Conduct a graded assessment of cable laying risks based on proximity, including:

[0154] 1) According to the proximity value, the risk level of each cable laying equipment is divided into two levels. k If it is greater than 0.7 and less than 1, the risk level of the corresponding cable laying equipment is considered to be extremely high; S k If it is greater than 0.3 and less than or equal to 0.7, the risk level of the corresponding cable laying equipment is considered to be medium; S k If it is less than or equal to 0.3, the risk level of the corresponding cable laying equipment is considered to be extremely low.

[0155] 2) Evaluate the risk level of the entire cable laying project, multiply the proximity of each device to obtain the risk assessment proximity of the entire cable laying project:

[0156]

[0157] Among them, S k is the optimal state closeness of the k-th cable laying equipment;

[0158] Closeness S 总 If it is greater than 0.5 and less than 1, the risk level of the entire cable laying project is considered to be extremely high;

[0159] S 总 If it is greater than 0.2 and less than or equal to 0.5, the risk level of the entire cable laying project is considered to be medium;

[0160] S 总 If it is less than or equal to 0.2, the risk level of the entire cable laying project is considered to be extremely low.

[0161] The obtained risk assessment closeness can be used for cable laying risk assessment.

[0162] Embodiment 2 of the present invention provides a cable laying risk assessment system based on optimal state proximity, including:

[0163] A data acquisition module, used to obtain multi-dimensional laying status evaluation index data of multiple cable laying equipment during the cable laying process;

[0164] A combined weighting module is used to calculate the subjective and objective weights of each evaluation index based on the acquired evaluation index data, and to perform combined weighting of the subjective weight and the objective weight to obtain the optimal combined weight;

[0165] A proximity calculation module, used to calculate the optimal state proximity of each cable laying equipment based on the optimal combination weight;

[0166] The risk assessment module is used to perform a graded assessment on the cable laying risk according to the degree of proximity to the optimal state.

[0167] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0168] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0169] Compared with the prior art, the beneficial effects of the present invention include at least:

[0170] The cable laying status evaluation index system of the present invention can comprehensively analyze the laying status detection data during the cable laying process, such as traction force and lateral pressure, the laying equipment operating condition data, such as service life and historical failure rate, and the operating environment data, such as temperature and humidity, to effectively evaluate the cable laying risks and avoid problems such as untimely response and low efficiency caused by manual judgment.

[0171] The present invention calculates the weights of data indicators, and combines the subjective weights calculated by the fuzzy hierarchical analysis method with the objective weights calculated by the entropy weight method through game theory to obtain the optimal combined weights, thereby avoiding the disadvantage of inaccurate calculations by a single weighting method.

[0172] The present invention calculates the distance between good and bad solutions of data indicators based on the optimal combination weight, and performs graded assessment of cable laying risks according to the degree of proximity to the optimal state, which can effectively assess the cable laying risks to prevent cable laying failures and accidents.

[0173] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0174] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0175] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0176] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A cable laying risk assessment method based on optimal state proximity, characterized in that: include: S1. Obtain multi-dimensional laying status evaluation index data of multiple cable laying equipment during the cable laying process; S2. Based on the obtained evaluation index data, the subjective and objective weights of each evaluation index are calculated, and the subjective weight and the objective weight are combined and weighted to obtain the optimal combined weight; S3. Calculate the optimal state closeness of each cable laying equipment based on the optimal combination weight; S4. Conduct a graded assessment of cable laying risks based on the degree of proximity to the optimal state.

2. A cable laying risk assessment method based on optimal state proximity according to claim 1, characterized in that: The evaluation indicators include traction, lateral pressure, service life, historical failure rate, operating environment temperature, and operating environment humidity.

3. A cable laying risk assessment method based on optimal state proximity according to claim 1, characterized in that: In S2, the calculation process of the subjective weight of each evaluation index is: 1.1) Compare the importance of the evaluation indicators and construct the fuzzy complementary matrix A; A=(a ij ) n×n (1) Where: a ij is the importance value of index i over index j; n is the number of evaluation indicators; 1.2) Use the following formula to sum each row of the fuzzy complementary matrix A and calculate the fuzzy consistency matrix R: R＝(rr ij ) n×n (4) Where: r i is the sum of the i-th row of the fuzzy complementary matrix A; r j is the sum of the jth row of the fuzzy complementary matrix A; rr ij is the consistency result of row i and row j; 1.3) After obtaining the fuzzy consistent matrix R, the subjective weight of each evaluation index is calculated using formula (5): Where α is the linear conversion coefficient: α=(n-1) / 2.

4. A cable laying risk assessment method based on optimal state proximity according to claim 1, characterized in that: In S2, the objective weight calculation process of each evaluation index is: 2.1) Based on the multi-dimensional laying status evaluation index data of multiple cable laying equipment, an evaluation matrix is ​​constructed: Among them, f mn Data representing the nth equipment status evaluation index of the mth cable laying equipment; 2.2) The types of state evaluation indicators are divided into positive indicators, negative indicators and interval indicators. The larger the value of the positive indicator, the better the indicator, and the opposite is true for the negative indicator. The interval indicator is the best indicator within the interval; 2.3) Perform the following normalization processing on the various evaluation index data in the evaluation matrix: Positive indicators, the normalized indicator data is: Negative indicators, the normalized indicator data is: For interval indicators, the normalized indicator data is: M=max{L l -min{f 1i ,…,f mi },max{f 1i ,…,f mi }-L h } (10) Where L l , L h The values ​​represent the left and right boundaries of the interval, respectively; 2.4) Obtain objective weights through indicator proportion and information entropy calculation: The weight of the i-th indicator of the k-th cable laying equipment is: The information entropy of the i-th indicator is: The objective weight of the i-th indicator is:

5. The cable laying risk assessment method based on optimal state proximity according to claim 1 is characterized in that: In S2, the combination of subjective weight and objective weight is realized by solving the following formula to obtain the optimal combination weight: min||in * -in|| (14) Among them, |||| is the Euclidean distance between vectors; w* is the optimal combination weight; β1 and β2 are linear indicator coefficients; w is the subjective weight vector, w′ is the objective weight vector; w=(w1,w2,…,w n ), w′=(w′1,w′2,…,w′ n ), n is the number of evaluation indicators.

6. A cable laying risk assessment method based on optimal state proximity according to claim 1, characterized in that: In S3, based on the optimal combination weight, the process of calculating the optimal state closeness of each cable laying equipment is as follows: 1) According to the standardization of the multi-dimensional laying status evaluation index data of multiple cable laying equipment, the standardized matrix Z is obtained: Among them, z ki is the standardized value of the i-th condition evaluation index data of the k-th cable laying equipment; 2) Calculate the Euclidean distance of each evaluation index to the positive and negative ideal solutions based on matrix Z In the formula, is a positive ideal solution; is a negative ideal solution; is the optimal combination weight; 3) Calculate the optimal state closeness S of the k-th cable laying equipment k :

7. A cable laying risk assessment method based on optimal state proximity according to claim 6, characterized in that: The positive ideal solution and the negative ideal solution are: μ ki and σ ki are the mean value and standard deviation of the i-th condition evaluation index of the k-th cable laying equipment within one year of operation.

8. The cable laying risk assessment method based on optimal state proximity according to claim 1 is characterized in that: In S4, the cable laying risk is graded and evaluated according to the optimal state proximity, including: According to the optimal state closeness S of each cable laying equipment k The risk level of each cable laying equipment is divided into the following values: S k If it is greater than 0.7 and less than 1, the risk level of the corresponding cable laying equipment is considered to be extremely high; S k If it is greater than 0.3 and less than or equal to 0.7, the risk level of the corresponding cable laying equipment is considered to be medium; S k If it is less than or equal to 0.3, the risk level of the corresponding cable laying equipment is considered to be extremely low.

9. The cable laying risk assessment method based on optimal state proximity according to claim 1, characterized in that: In S4, the cable laying risk is graded and evaluated according to the optimal state proximity, including: According to the optimal state closeness of each cable laying equipment, the optimal state closeness of the entire cable laying project is obtained: Among them, S k is the optimal state closeness of the k-th cable laying equipment; S 总 If it is greater than 0.5 and less than 1, the risk level of the entire cable laying project is considered to be extremely high; S 总 If it is greater than 0.2 and less than or equal to 0.5, the risk level of the entire cable laying project is considered to be medium; S 总 If it is less than or equal to 0.2, the risk level of the entire cable laying project is considered to be extremely low.

10. A cable laying risk assessment system based on optimal state proximity, using the method according to any one of claims 1 to 9, characterized in that: The system comprises: A data acquisition module, used to obtain multi-dimensional laying status evaluation index data of multiple cable laying equipment during the cable laying process; A combined weighting module is used to calculate the subjective and objective weights of each evaluation index based on the acquired evaluation index data, and to perform combined weighting of the subjective weight and the objective weight to obtain the optimal combined weight; A proximity calculation module, used to calculate the optimal state proximity of each cable laying equipment based on the optimal combination weight; The risk assessment module is used to perform a graded assessment on the cable laying risk according to the degree of proximity to the optimal state.

11. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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