Internet of things based ship equipment whole life cycle intelligent management system and method

Abstract

The application provides a ship equipment full life cycle intelligent management system and method based on Internet of Things. The system comprises an identification management module, an edge sensing module, a data hub module and a verification execution module. First, laser coding is read, basic data of different ship equipment is collected in real time, abnormal working conditions are determined and abnormal ship equipment position coordinates are locked. Based on a block chain database, combined with associated data and health state evaluation results, priority score is calculated and the optimal maintenance scheme is screened. After the feasibility of the maintenance scheme is verified by a three-dimensional simulation model, deviation simulation is performed, operation instructions are generated to execute maintenance. The application stores information in a block chain database, screens the optimal maintenance scheme and adopts a double verification mechanism, thereby improving the one-time success rate of the maintenance scheme. The whole process does not require manual intervention, and the problems of ship equipment identity tampering, manual decision lag and easy maintenance rework are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ship equipment, in particular to a ship equipment full life cycle intelligent management system and method based on Internet of Things. BACKGROUND

[0002] As the core element of modern shipping system, ship equipment covers multiple functional units such as power system, navigation device and auxiliary machinery. During the equipment service period, the manufacturing information and environmental parameter information increase geometrically, and the data forms cover structured monitoring values and unstructured maintenance records. In industry practice, equipment management is evolving from traditional phased independent operation to full-process digital integration, and a full life cycle management system covering design verification, production assembly, operation monitoring, maintenance decision and scrap evaluation needs to be built to realize equipment state transparency, maintenance strategy precision and resource scheduling optimization.

[0003] The existing ship equipment intelligent management system generally has the defect of disconnection between physical identity and digital information in ship equipment management. Traditional identification technology is easy to wear and tamper, which makes it difficult to trace the real history when the equipment is replaced. At the same time, real-time data such as vibration and temperature collected by sensors and historical maintenance records are stored in different systems, forming a data island, which makes maintenance decision rely on manual experience to integrate information, resulting in low efficiency and easy misjudgment. In addition, the single chain structure is commonly used in the storage of blockchain database, and the equipment basic data and maintenance records are mixedly stored, which affects the retrieval and verification efficiency of key data.

[0004] The existing ship equipment intelligent management method generates maintenance scheme based on simple threshold alarm and manual experience, lacks dynamic risk assessment and simulation verification mechanism, and the maintenance scheme often fails to execute due to the size deviation of spare parts or environmental mutation, which cannot predict the equipment operation state after maintenance. In addition, the environmental interference compensation adopts fixed coefficient correction, and the three-dimensional simulation model is not established through the information of ship equipment, which makes the working condition deviation evaluation distorted. SUMMARY

[0005] In order to solve the technical problems mentioned in the background art, the present application proposes a ship equipment full life cycle intelligent management system and method based on Internet of Things.

[0006] Therefore, the technical scheme adopted by the present application is as follows:

[0007] The ship equipment full life cycle intelligent management system based on Internet of Things, characterized in that the system comprises:

[0008] An identification management module stores a ship equipment factory parameter; a ship equipment surface is engraved with a laser code to establish a unique physical identity information of the ship equipment; a laser code is read in real time and verified through an intelligent identification system; the laser code is transmitted to an edge perception module and a data hub module;

[0009] The edge perception module receives the verified laser code; basic data of different ship equipment is collected through a sensor group, and a digital tag is inserted to generate tagged data, the digital tag being provided by the data hub module; whether the real-time working condition of the ship equipment is abnormal is identified according to the basic data in the tagged data and in combination with historical maintenance records, and the tagged data is input into the data hub module;

[0010] The data hub module receives the laser code and converts the laser code into a digital tag; a blockchain database is built-in to store the tagged data and historical maintenance records of the ship equipment; a health status of the ship equipment is evaluated, and the basic data in the tagged data is associated with the historical maintenance records to generate associated data of the ship equipment; in combination with the associated data and the health status evaluation result, an optimal maintenance scheme is screened based on a maintenance scheme priority score; the optimal maintenance scheme is transmitted to a verification execution module;

[0011] The verification execution module, after receiving the optimal maintenance scheme, constructs a three-dimensional simulation model based on the factory parameter and the position coordinates in the digital tag; the optimal maintenance scheme is verified for feasibility based on the three-dimensional simulation model, and after the verification is passed, a simulation operation is further performed to generate an operation instruction; the ship equipment is maintained according to the operation instruction, and the maintenance record of this time is uploaded as the latest historical maintenance record to the blockchain database.

[0012] Further, the laser code is a three-segment dynamic encryption code, the first segment is a hash value of a ship classification society certification number, the middle segment records a ship equipment factory parameter, and the last segment marks a ship equipment installation position coordinate and an installation timestamp;

[0013] The intelligent identification system verifies the laser code of the ship equipment, initiates a verification request to the blockchain database, and the blockchain database extracts the position coordinates and the installation timestamp stored in the digital tag from the tagged data of the corresponding ship equipment;

[0014] If the position coordinates in the laser code do not match the position coordinates in the digital tag, or the installation timestamp conflicts with the ship equipment service life in the factory parameter, an audible and light alarm is triggered and the ship equipment operation permission is frozen, if the position coordinates are consistent and the installation timestamp does not conflict, the laser code is transmitted to the edge perception module and the data hub module.

[0015] Further, the sensor group collects real-time data of the real-time working condition of the ship equipment in real time;

[0016] The real-time working conditions include vibration spectrum, temperature change and oil metal particle concentration, and the basic data is extracted from the original data of the real-time working conditions;

[0017] The basic data includes the main vibration frequency in the vibration frequency amplitude, the temperature change rate per hour, the metal particle concentration growth rate and the ratio of the cumulative operating hours of the ship equipment;

[0018] The edge perception module simultaneously identifies the real-time working conditions of the ship equipment in real time, and triggers local alarm when the real-time working conditions are identified to be abnormal;

[0019] Each kind of basic data is provided with an abnormal threshold, and when the corresponding abnormal threshold is exceeded, it is determined that the real-time working conditions are abnormal;

[0020] When it is determined that the real-time working conditions are not abnormal, the sensor group continues to collect and generate basic data;

[0021] When it is determined that the real-time working conditions are abnormal, sound and light alarms are triggered immediately, and based on the digital tag, the position coordinates of the ship equipment where the real-time working conditions are abnormal are locked.

[0022] Further, when the ship equipment is replaced, the old digital tag is abolished and a new digital tag is generated, and the intelligent identification system is triggered to scan the laser code again, and the installation position coordinates and installation time stamp of the last section of the laser code of the ship equipment are double-checked;

[0023] The tagged data is written into the blockchain database, and when writing, it is checked whether it carries the digital tag, and the basic data without the digital tag is returned to the edge perception module, and the digital tag bound to the current basic data is re-inserted, so that only the tagged data can be written into the equipment gene chain.

[0024] Further, the blockchain database is composed of the equipment gene chain of each ship equipment;

[0025] The equipment gene chain is composed of tagged data and historical maintenance records;

[0026] The historical maintenance records include health state evaluation results and optimal maintenance schemes.

[0027] Further, the generation process of the health state evaluation result is as follows:

[0028] The data hub module receives the tagged data and extracts the historical maintenance records from the blockchain database, the tagged data and the historical maintenance records are combined to evaluate the health state of the ship equipment, generate the health state evaluation result, and the health state evaluation result is measured by a health state evaluation index, and the formula is:

[0029]

[0030] Wherein, H is the health state evaluation index of the ship equipment, M is the matching degree of historical maintenance record, t is the time from the last similar failure, is the weight coefficient, W i is the weight of the ith basic data, n is the number of basic data, V i is the ith basic data processed by the edge perception module; V d,i is the design threshold corresponding to the basic data, stored in the ship equipment factory parameter;

[0031] The health state evaluation result is returned to the blockchain database.

[0032] Further, the screening process of the optimal maintenance scheme is as follows:

[0033] Combined with the health state evaluation result and the associated data, a maintenance scheme is generated and the priority score of the maintenance scheme is calculated, and the calculation formula is:

[0034]

[0035] Wherein, P is the priority score, H is the health state evaluation index of the ship equipment, is the position coordinate parameter, S is the historical maintenance success rate of similar failures, W is the maintenance experience credibility weight, is the historical experience attenuation coefficient, t is the time, R d is the current failure standard maintenance difficulty, R a is the mechanical maintenance equipment capacity value, is the balance coefficient;

[0036] According to the priority score, the optimal maintenance scheme is screened out, and the optimal maintenance scheme is written into the blockchain database;

[0037] The labeled optimal maintenance scheme is received and optimized, and the optimal maintenance scheme is regenerated, and the latest optimal maintenance scheme is uploaded to the verification execution module.

[0038] Further, the feasibility verification process is as follows:

[0039] The three-dimensional simulation model calls the historical maintenance record in the blockchain database, judges the maintenance success rate of the same type, and combines the real-time working condition of the ship equipment to calculate the failure risk value of the optimal maintenance scheme through a mathematical model, and the mathematical model formula is:

[0040]

[0041] Wherein, F is the optimal maintenance scheme failure risk value, S is the success rate of historical maintenance records of the same type, H is the health state evaluation index of the ship equipment, H c is the preset health state critical threshold of the ship equipment, is the temperature change rate, t1-t0 is the predicted implementation time span of the maintenance scheme, is the smoothing constant;

[0042] The optimal maintenance scheme failure risk value is judged by a risk threshold to infer whether the optimal maintenance scheme is allowed to be executed;

[0043] If the maintenance scheme failure risk value is less than the set risk threshold, execution is allowed, the optimal maintenance scheme is simulated, and the optimal maintenance scheme is fed back to the blockchain database;

[0044] Otherwise, execution is not allowed, and the data hub module triggers to re-screen the optimal maintenance scheme.

[0045] Further, the simulation process is as follows:

[0046] In combination with the basic data, the optimal maintenance scheme simulates the maintenance process in the three-dimensional simulation model, and calculates the deviation degree of the optimal maintenance scheme in the simulation process, and the formula is as follows:

[0047]

[0048] Wherein, D is the deviation degree of the optimal maintenance scheme, is the average deviation of the ship equipment parameters and the factory parameters in the simulation process, X std is the factory parameter, is the temperature change rate, H is the health state evaluation index of the ship equipment, H c is the preset health state critical threshold of the ship equipment, and T is the total simulation time;

[0049] When the deviation degree meets the standard, it is determined that the optimal maintenance scheme is effective, operation instructions are directly generated according to the optimal maintenance scheme, and the mechanical maintenance equipment terminal is issued:

[0050] When the deviation degree does not meet the standard, it is determined that the optimal maintenance scheme is invalid, a need-to-optimize mark is written in the optimal maintenance scheme, and the marked optimal maintenance scheme is fed back to the data hub module;

[0051] The mechanical maintenance equipment terminal directly receives the operation instructions and maintains the ship equipment;

[0052] After the actual maintenance is completed, the maintenance time is recorded, the actual maintenance record is generated in combination with the maintenance time, the actual maintenance record is taken as the latest historical maintenance record, and is fed back to the blockchain database.

[0053] The ship equipment whole life cycle intelligent management method based on the Internet of Things, characterized in that the method comprises the following steps:

[0054] Step one, identity management, store the ship equipment factory parameters; the surface of the ship equipment is engraved with laser code to establish the unique physical identity information of the ship equipment; the laser code is read and verified in real time through the intelligent identification system, and the laser code is converted into a digital tag; the laser code is transmitted to step two;

[0055] Step two, edge perception, receive the verified laser code and digital tag; collect the basic data of different ship equipment through the sensor group and insert the digital tag to generate tagged data; according to the tagged data, whether the real-time working condition of the ship equipment is abnormal is identified, and the tagged data is input into step three;

[0056] Step three, data hub, built-in block chain database, store the tagged data and the historical maintenance record of the ship equipment; perform health status evaluation on the ship equipment, and associate the basic data in the tagged data with the historical maintenance record to generate ship equipment associated data; combine the associated data and the health status evaluation result, select the optimal maintenance scheme based on the maintenance scheme priority score, and transmit the optimal maintenance scheme to step four;

[0057] Step four, verification and execution, after receiving the optimal maintenance scheme, a three-dimensional simulation model is constructed based on the factory parameters and position coordinates in the digital tag; the feasibility of the optimal maintenance scheme is verified based on the three-dimensional simulation model, and after the verification, further simulation operation is performed, and operation instructions are generated after the simulation; the ship equipment is maintained according to the operation instructions, the maintenance record of this time is taken as the latest historical maintenance record, and is uploaded to the block chain database.

[0058] Compared with the prior art, the advantages of the present application are:

[0059] 1. Through real-time bidirectional verification of laser code and block chain tag, the physical information and digital identity of the ship equipment are hard bound, effectively solving the problem of fake replacement of spare parts, and the identity information of each ship equipment is automatically triggered for block chain verification during maintenance and replacement, ensuring that the identity cannot be tampered with during the whole life cycle.

[0060] 2. Based on the equipment gene chain in the block chain database, the real-time working condition, health status evaluation result and historical experience are dynamically associated, the manual experience judgment maintenance scheme is evolved into a data generated maintenance scheme, and a three-dimensional simulation model is constructed, through the double verification of failure risk value and deviation degree, the maintenance error and risk are avoided in advance, thereby improving the success rate of maintenance operation.

[0061] 3. From anomaly identification, scheme generation, simulation verification to maintenance execution, the whole process does not need manual intervention, the system locks the abnormal equipment position, issues maintenance instructions and records the results, and is suitable for complex scenes with high reliability requirements. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0063] Figure 1 A process diagram of a ship equipment full life cycle intelligent management system based on the Internet of Things;

[0064] Figure 2 A block chain database composition diagram of the present application;

[0065] Figure 3 An optimal maintenance scheme generation flowchart of the present application. DETAILED DESCRIPTION

[0066] To achieve the above purpose, the present application is realized by the following technical solutions, and the present application provides a ship equipment full life cycle intelligent management system based on the Internet of Things, please refer to Figure 1 The system comprises:

[0067] An identity management module stores ship equipment factory parameters; a laser code is engraved on the surface of the ship equipment to establish the unique physical identity information of the ship equipment; the laser code is read and verified in real time through an intelligent identification system; and the laser code is transmitted to an edge perception module and a data hub module;

[0068] The laser code is a three-segment dynamic encryption code, the first segment is a hash value of a ship classification society certification number, the middle segment records the ship equipment factory parameters, and the last segment marks the ship equipment installation position coordinates and installation time stamp;

[0069] The intelligent identification system verifies the laser code of the ship equipment, initiates a verification request to the block chain database, and the block chain database extracts the position coordinates and installation time stamp stored in the digital tag from the labeled data corresponding to the ship equipment;

[0070] If the position coordinates in the laser code do not match the position coordinates in the digital tag, or the installation time stamp conflicts with the service life of the ship equipment in the factory parameters, an audible and light alarm is triggered and the operation permission of the ship equipment is frozen, and if the position coordinates are consistent and the installation time stamp does not conflict, the laser code is transmitted to the edge perception module and the data hub module.

[0071] The edge perception module receives the laser code that passes the verification, collects basic data of different ship equipment through the sensor group, inserts a digital tag to generate tagged data, and the digital tag is provided by the data hub module; according to the basic data in the tagged data, and in combination with the historical maintenance records, it is identified whether the real-time working condition of the ship equipment is abnormal, and the tagged data is input into the data hub module;

[0072] The sensor group collects the original data of the real-time working condition of the ship equipment in real time;

[0073] In some embodiments, the real-time working condition includes vibration spectrum, temperature change and oil metal particle concentration, and the basic data is extracted from the original data of the real-time working condition;

[0074] In some embodiments, the basic data includes the main vibration frequency in the vibration frequency amplitude, the temperature change rate per hour, the metal particle concentration growth rate, and the ratio of the cumulative operating hours of the ship equipment;

[0075] The edge perception module simultaneously identifies the real-time working condition of the ship equipment in real time, and triggers a local alarm when the real-time working condition is identified as abnormal;

[0076] Each basic data is provided with an abnormal threshold, and when the corresponding abnormal threshold is exceeded, it is determined that the real-time working condition is abnormal;

[0077] When it is determined that the real-time working condition is not abnormal, the sensor group continues to collect and generate basic data, and saves the original data of the real-time working condition in the last 24 hours to the local database of the sensor group;

[0078] When it is determined that the real-time working condition is abnormal, an audible and visual alarm is immediately triggered, and based on the digital tag, the position coordinates of the ship equipment where the real-time working condition is abnormal are locked.

[0079] The data hub module receives the laser code and converts the laser code into a digital tag; a built-in blockchain database stores tagged data and historical maintenance records of the ship equipment; the health status of the ship equipment is evaluated, and the basic data in the tagged data is associated with the historical maintenance records to generate associated data of the ship equipment; in combination with the associated data and the health status evaluation result, the optimal maintenance scheme is screened based on the maintenance scheme priority score; the optimal maintenance scheme is transmitted to the verification execution module; the Figure 2 and Figure 3 ;

[0080] When the ship equipment is replaced, the old digital tag is abolished and a new digital tag is generated, and the intelligent identification system is triggered to scan the laser code again, and the installation position coordinates and installation time stamp of the last section of the laser code are double-verified;

[0081] The tagged data is written into the blockchain database. When writing, it is checked whether it carries the digital tag. The basic data without the digital tag is returned to the edge perception module, and the digital tag bound to the current basic data is inserted again to ensure that only tagged data can be written into the device gene chain of the equipment;

[0082] The blockchain database is composed of the device gene chain of each ship equipment;

[0083] In some embodiments, the device gene chain is composed of tagged data and historical maintenance records;

[0084] In some embodiments, the historical maintenance records include health state evaluation results and optimal maintenance schemes;

[0085] The generation process of the health state evaluation result is as follows:

[0086] The data hub module receives tagged data and extracts historical maintenance records from the blockchain database. The tagged data is combined with the historical maintenance records to evaluate the health state of the ship equipment, generate health state evaluation results, and use the health state evaluation index to measure the health state evaluation results, whose formula is:

[0087]

[0088] Wherein, H is the health state evaluation index of the ship equipment, M is the matching degree of the historical maintenance record, t is the time from the last similar failure, is the weight coefficient, W i is the weight of the ith basic data, n is the number of basic data, V i is the ith basic data processed by the edge perception module; V d,i is the design threshold value corresponding to the basic data, which is stored in the ship equipment factory parameter;

[0089] The health state evaluation result is uploaded to the next step, and the health state evaluation result is returned to the blockchain database;

[0090] The selection process of the optimal maintenance scheme is as follows:

[0091] Combined with the health state evaluation result and the associated data, a maintenance scheme is generated and the priority score of the maintenance scheme is calculated, whose formula is:

[0092]

[0093] Wherein, P is the priority score, H is the health state evaluation index of the ship equipment, is the position coordinate parameter, S is the historical maintenance success rate of similar failures, and W is the maintenance experience credibility weight, is the historical experience attenuation coefficient, t is time, R d is the current fault standard maintenance difficulty, R a is the mechanical maintenance equipment capacity value, is the balance coefficient;

[0094] wherein, is used to calculate the health emergency degree, the lower H is, the worse the health state of the ship equipment is, the lower the position is, the closer the ship equipment is, and the higher the health emergency degree is;

[0095] is used to calculate the historical experience correction degree, the higher S is, and the higher the W score is, the higher the historical experience correction degree is, but it is weakened with the passage of t;

[0096] is used to calculate the maintenance difficulty ratio, R d the stronger or the lower R a is, the higher the maintenance difficulty ratio is;

[0097] The health emergency degree, the historical experience correction degree and the maintenance difficulty ratio are added to obtain a priority score;

[0098] According to the priority score, the optimal maintenance scheme is screened out, and the optimal maintenance scheme is written into the blockchain database;

[0099] The optimal maintenance scheme with the mark is received and optimized, and the optimal maintenance scheme is regenerated, and the latest optimal maintenance scheme is re-uploaded to the verification execution module.

[0100] The verification execution module receives the optimal maintenance scheme, constructs a three-dimensional simulation model based on the factory parameters and the position coordinates in the digital tag, performs feasibility verification on the optimal maintenance scheme based on the three-dimensional simulation model, further performs simulation operation after the verification is passed, generates operation instructions after the simulation is passed, and performs maintenance on the ship equipment according to the operation instructions, records this maintenance as the latest historical maintenance record, and uploads the latest historical maintenance record to the blockchain database;

[0101] The digital tag is derived from laser coding, and the laser coding includes a hash value of a ship classification society certification number, factory parameters of the ship equipment, installation position coordinates of the ship equipment and installation time stamps, and a three-dimensional simulation model is constructed based on the factory parameters and the position coordinates in the digital tag;

[0102] The feasibility verification process is as follows:

[0103] The three-dimensional simulation model calls the historical maintenance record in the blockchain database, judges the maintenance success rate of the same type, and calculates the optimal maintenance scheme failure risk value through a mathematical model combined with the real-time working condition of the ship equipment, and the mathematical model formula is:

[0104]

[0105] wherein F is the failure risk value of the optimal maintenance scheme, S is the success rate of the historical maintenance records of the same type, H is the health state evaluation index of the marine equipment, H c is the preset health state critical threshold of the marine equipment, is the temperature change rate, t1-t0 is the predicted implementation time span of the maintenance scheme, is the smoothing constant;

[0106] The numerator in the mathematical model formula is a risk value driving term, the lower H is or the farther H is from H c , the higher F is, and H c -H is calculated only when H c is less than H std , otherwise the absolute value is taken;

[0107] directly reflects the severity of the environment in which the marine equipment is located, the higher F is;

[0108] t1-t0 directly reflects the time required for maintenance, the longer the required time is, the higher F is;

[0109] The denominator in the formula is a risk value inhibiting term, the higher S is, the stronger the inhibiting effect on F is, prevents the formula from failing when there is no historical data (S=0);

[0110] The failure risk value of the optimal maintenance scheme is subjected to risk threshold judgment to infer whether the optimal maintenance scheme is allowed to be executed;

[0111] If the failure risk value of the maintenance scheme is less than the set risk threshold, execution is allowed, the optimal maintenance scheme is simulated, and the optimal maintenance scheme is returned to the blockchain database;

[0112] Otherwise, execution is not allowed, and the data hub module is triggered to re-screen the optimal maintenance scheme;

[0113] The simulation process is as follows:

[0114] In combination with the basic data, the optimal maintenance scheme simulates the maintenance process in the three-dimensional simulation model, and the deviation degree of the optimal maintenance scheme is calculated in the simulation process, and the formula is as follows:

[0115]

[0116] wherein D is the deviation degree of the optimal maintenance scheme, is the average deviation of the parameters of the marine equipment in the simulation process from the factory parameters, X std is the factory parameter, is the rate of temperature change, H is the health state evaluation index of the ship equipment, H c is the preset health state critical threshold of the ship equipment, and T is the total simulation time length;

[0117] The deviation degree formula, for calculating the factory parameter deviation sensitivity, X accounts for std The smaller the proportion, the lower the sensitivity;

[0118] The total value of D is directly amplified, which reflects the influence of the environment of the ship equipment on the maintenance process;

[0119] Directly reflects the health state of the ship equipment, when the health state of the ship equipment deteriorates, the fault tolerance of the maintenance process decreases, D increases linearly, and only when H c is less than H c , otherwise, take zero;

[0120] T directly reflects the total simulation time length, the longer the simulation time of the maintenance scheme, the more the negative influence can be accumulated and amplified, and D increases linearly;

[0121] When the deviation degree meets the standard, it is determined that the optimal maintenance scheme is effective, and operation instructions are directly generated according to the optimal maintenance scheme and issued to the mechanical maintenance equipment terminal:

[0122] When the deviation degree does not meet the standard, it is determined that the optimal maintenance scheme is invalid, a need-to-optimize mark is written in the optimal maintenance scheme, and the marked optimal maintenance scheme is fed back to the data hub module;

[0123] The mechanical maintenance equipment terminal directly receives the operation instructions and maintains the ship equipment;

[0124] After the actual maintenance is completed, the maintenance time is recorded, the actual maintenance record is generated in combination with the maintenance time, the actual maintenance record is taken as the latest historical maintenance record, and is fed back to the blockchain database.

[0125] The ship equipment full life cycle intelligent management method based on the Internet of Things, characterized in that the method comprises the following steps:

[0126] Step 1, identification management, store the factory parameters of the ship equipment; the surface of the ship equipment is engraved with laser code, and the unique physical identity information of the ship equipment is established; the laser code is read and verified in real time through an intelligent identification system, and the laser code is converted into a digital tag; the laser code is transmitted to step 2;

[0127] Step two, edge awareness, receives the laser code and digital label that passes the verification; collects the basic data of different ship equipment through the sensor group, inserts the digital label, and generates labeled data; according to the labeled data, whether the real-time working condition of the ship equipment is abnormal is identified, and the labeled data is input into step three;

[0128] Step three, data hub, built-in blockchain database, stores labeled data and historical maintenance records of ship equipment; health status assessment is performed on the ship equipment, and the basic data in the labeled data is associated with the historical maintenance records to generate associated data of the ship equipment; combined with the associated data and the health status assessment result, the optimal maintenance scheme is screened based on the maintenance scheme priority score; the optimal maintenance scheme is transmitted to step four;

[0129] Step four, verification execution, after receiving the optimal maintenance scheme, a three-dimensional simulation model is constructed based on the factory parameters and location coordinates in the digital label; the feasibility of the optimal maintenance scheme is verified based on the three-dimensional simulation model, and after verification, further simulation operation is performed, and operation instructions are generated after simulation; the ship equipment is maintained according to the operation instructions, and the maintenance record of this time is taken as the latest historical maintenance record and uploaded to the blockchain database.

[0130] The ship equipment full life cycle intelligent management system and method based on the Internet of Things provided by the application, through real-time verification of laser coding and blockchain digital label, ensures the uniqueness of the equipment identity, and prevents replacement and fraud; based on multi-dimensional data collection and equipment gene chain construction, the data barriers of real-time working condition, historical maintenance records and health assessment are broken through; the dynamic health index and three-dimensional simulation model are used to predict the failure risk and simulate the operation deviation of the maintenance scheme, replacing the artificial experience decision, and finally realizing the abnormal identification of the ship equipment, the success rate improvement of the maintenance scheme and the optimization of the full life cycle cost, solving the problems of disconnection between physical identity and digital information of ship equipment, low retrieval efficiency and low success rate of maintenance scheme in traditional management system.

[0131] In summary, the application has the advantages that through real-time mutual verification of laser coding and blockchain label, a physical entity and an unforgeable digital identity are constructed, replacement and fraud of spare parts are solved and prevented, based on the equipment gene chain in the blockchain database, real-time working condition, historical maintenance records and health assessment are deeply bound, maintenance decision is upgraded from discrete experience judgment to continuous self-optimizing data-driven mode, a three-dimensional simulation model is constructed, the optimal maintenance scheme is verified-simulated through failure risk prediction and deviation dynamic calibration, the success rate of one-time maintenance operation is improved, the trial-and-error cost of maintenance is reduced, and the whole system realizes the full-link closed loop of abnormal discovery, scheme screening, simulation verification and instruction execution without manual intervention, especially suitable for high reliability demand scenarios of ocean-going ships.

[0132] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An Internet of Things-based intelligent management system for the whole life cycle of ship equipment, characterized in that, The system comprises: An identification management module stores ship equipment factory parameters; a ship equipment surface is engraved with a laser code to establish a unique physical identity information of the ship equipment; the laser code is read and verified in real time through an intelligent identification system; and the laser code is transmitted to an edge perception module and a data hub module; The laser code is a three-section dynamic encryption code, the first section is a hash value of a ship classification society certification number, the middle section records the ship equipment factory parameters, and the last section marks the ship equipment installation position coordinates and installation time stamp; The intelligent identification system verifies the laser code of the ship equipment, initiates a verification request to a blockchain database, and the blockchain database extracts the position coordinates and installation time stamp stored in the digital tag from the labeled data of the corresponding ship equipment; If the position coordinates in the laser code do not match the position coordinates in the digital tag, or the installation time stamp conflicts with the service life of the ship equipment in the factory parameters, an audible and light alarm is triggered and the operation permission of the ship equipment is frozen, if the position coordinates are consistent and the installation time stamp does not conflict, the laser code is transmitted to the edge perception module and the data hub module; An edge perception module receives the laser code that passes the verification; basic data of different ship equipment is collected through a sensor group, and a digital tag is inserted to generate labeled data, the digital tag is provided by the data hub module; according to the basic data in the labeled data, and in combination with the historical maintenance records, whether the real-time working condition of the ship equipment is abnormal is identified, and the labeled data is input into the data hub module; A data hub module receives the laser code and converts the laser code into a digital tag; a blockchain database is built in to store the labeled data and the historical maintenance records of the ship equipment; the health status of the ship equipment is evaluated, and the basic data in the labeled data is associated with the historical maintenance records to generate associated data of the ship equipment; in combination with the associated data and the health status evaluation result, the optimal maintenance scheme is selected based on the maintenance scheme priority score; and the optimal maintenance scheme is transmitted to a verification execution module; The generation process of the health status evaluation result is as follows: The data hub module receives the labeled data and extracts the historical maintenance records from the blockchain database, the labeled data is combined with the historical maintenance records to evaluate the health status of the ship equipment, and a health status evaluation result is generated, the health status evaluation result is measured by a health status evaluation index, and the formula is: wherein H is a health state evaluation index of the ship equipment, M is a matching degree of historical maintenance records, t is a time from a last similar fault, is a weight coefficient, is a weight of the ith basic data, and n is a number of the basic data, is the ith basic data processed by the edge perception module; is a design threshold corresponding to the basic data and is stored in a factory parameter of the ship equipment. The health status evaluation result is returned to the blockchain database; A verification execution module, after receiving the optimal maintenance scheme, constructs a three-dimensional simulation model based on the factory parameters and position coordinates in the digital tag; the optimal maintenance scheme is verified based on the three-dimensional simulation model, and after the verification is passed, further simulation operation is performed, and operation instructions are generated after the simulation is passed; the ship equipment is maintained according to the operation instructions, the maintenance record of this time is taken as the latest historical maintenance record, and is uploaded to the blockchain database. 2.The Internet of Things based intelligent management system for ship equipment full life cycle according to claim 1, characterized in that, The sensor group collects real-time data of the real-time working condition of the ship equipment in real time; The real-time working condition includes vibration spectrum, temperature change and oil metal particle concentration, and the basic data is extracted from the real-time data of the real-time working condition; The basic data includes the ratio of vibration frequency to vibration amplitude, the hourly temperature change rate, the metal particle concentration growth rate, and the cumulative operating hours of the ship equipment; The edge perception module simultaneously identifies the real-time working condition of the ship equipment in real time. When an abnormal real-time working condition is identified, a local alarm is triggered; Each type of basic data has an abnormal threshold. When the corresponding abnormal threshold is exceeded, it is determined that the real-time working condition is abnormal; When it is not determined that the real-time working condition is abnormal, the sensor group continues to collect and generate basic data; When it is determined that the real-time working condition is abnormal, an audible and visual alarm is immediately triggered, and the location coordinates of the ship equipment with an abnormal real-time working condition are locked based on the digital tag. 3.The Internet of Things based intelligent management system for ship equipment full life cycle according to claim 1, characterized in that, When the ship equipment is replaced, the old digital tag is discarded and a new digital tag is generated, and the intelligent identification system is triggered to scan the laser code again. The installation location coordinates and installation time stamp of the ship equipment at the end of the laser code are double-checked. The tagged data is written into the blockchain database. When writing, it is checked whether the digital tag is carried. Basic data without a digital tag is returned to the edge perception module, and the digital tag bound to the current basic data is reinserted to ensure that only tagged data can be written into the equipment gene chain. 4.The Internet of Things based intelligent management system for ship equipment full life cycle according to claim 3, characterized in that, The blockchain database is composed of the equipment gene chain of each ship equipment; The equipment gene chain is composed of tagged data and historical maintenance records; The health status evaluation results and the optimal maintenance scheme are included in the historical maintenance records. 5.The Internet-of-Things-based intelligent management system for ship equipment throughout life cycle according to claim 1, characterized in that, The selection process of the optimal maintenance scheme is as follows: Based on the health status evaluation results and the associated data, a maintenance scheme is generated and a priority score of the maintenance scheme is calculated, with the formula being: wherein P is a priority score, H is a health status evaluation index of the ship equipment, is a position coordinate parameter, S is a historical maintenance success rate of the same type of failure, and W is a maintenance experience credibility weight, is a historical experience attenuation coefficient, and t is time, is a current failure standard maintenance difficulty, is a mechanical maintenance equipment capacity value, is a balance coefficient; According to the priority score, the optimal maintenance scheme is selected, and the optimal maintenance scheme is written into the blockchain database; The tagged optimal maintenance scheme is received and optimized, and the optimal maintenance scheme is regenerated. The latest optimal maintenance scheme is uploaded to the verification execution module. 6.The Internet of Things based intelligent management system for ship equipment full life cycle according to claim 5, characterized in that, The feasibility verification process is as follows: The three-dimensional simulation model calls the historical maintenance records in the blockchain database, judges the success rate of the same type of maintenance, and calculates the failure risk value of the optimal maintenance scheme through a mathematical model based on the real-time working condition of the ship equipment, with the formula being: wherein F is the optimal maintenance scheme failure risk value, S is the success rate of historical maintenance records of the same type, H is the health state evaluation index of the ship equipment, is the preset health state threshold of the ship equipment, is the temperature change rate, is the maintenance scheme predicted implementation time span, is the smoothing constant; The failure risk value of the optimal maintenance scheme is judged by a risk threshold to determine whether the optimal maintenance scheme is allowed to be executed; If the failure risk value of the maintenance scheme is less than the set risk threshold, execution is allowed, the optimal maintenance scheme is simulated, and the optimal maintenance scheme is returned to the blockchain database; Otherwise, execution is not allowed, and the data hub module reselects the optimal maintenance scheme. 7.The Internet of Things based intelligent management system for ship equipment full life cycle according to claim 6, characterized in that, The simulation process is as follows: Based on the basic data, the optimal maintenance scheme simulates the maintenance process in the three-dimensional simulation model. The deviation degree of the optimal maintenance scheme is calculated during the simulation process, with the formula being: wherein D is the deviation degree of the optimal maintenance scheme, is the average deviation of the ship equipment parameters in the simulation process from the factory parameters, is the factory parameter, is the temperature change rate, H is the health state evaluation index of the ship equipment, is the preset health state critical threshold of the ship equipment, and T is the total simulation time. When the deviation degree meets the standard, it is determined that the optimal maintenance scheme is valid, and operation instructions are directly generated based on the optimal maintenance scheme and sent to the mechanical maintenance equipment terminal: When the deviation degree does not meet the standard, it is determined that the optimal maintenance scheme is invalid, a mark is written in the optimal maintenance scheme, and the tagged optimal maintenance scheme is fed back to the data hub module. The mechanical maintenance equipment terminal directly receives operation instructions and performs maintenance on the ship equipment; After the actual maintenance is completed, the maintenance time is recorded, and the actual maintenance record is generated in combination with the maintenance time, the actual maintenance record is taken as the latest historical maintenance record, and the latest historical maintenance record is returned to the blockchain database.

8. The method for intelligent management of the whole life cycle of ship equipment based on the Internet of Things, characterized in that, The method comprises the following steps: Step one, identity management, store the ship equipment factory parameters; the surface of the ship equipment is engraved with laser code, the unique physical identity information of the ship equipment is established; the laser code is read and verified in real time through the intelligent identification system, and the laser code is converted into a digital tag; the laser code is transmitted to step two; The laser code is a three-section dynamic encryption code, the first section is the hash value of the classification society certification number, the middle section records the ship equipment factory parameters, and the last section marks the ship equipment installation position coordinates and installation time stamp; The intelligent identification system verifies the laser code of the ship equipment, initiates a verification request to the blockchain database, and the blockchain database extracts the position coordinates and installation time stamp stored in the digital tag from the tagged data corresponding to the ship equipment; If the position coordinates in the laser code do not match the position coordinates in the digital tag, or the installation time stamp conflicts with the service life of the ship equipment in the factory parameters, an audible and light alarm is triggered and the operation permission of the ship equipment is frozen, if the position coordinates are consistent and the installation time stamp does not conflict, the laser code is transmitted to the edge perception module and the data hub module; Step two, edge perception, receive the laser code and digital tag that pass the verification; collect the basic data of different ship equipment through a sensor group, and insert the digital tag to generate tagged data; according to the tagged data, whether the real-time working condition of the ship equipment is abnormal is identified, and the tagged data is input into step three; Step three, data hub, receives the laser code and converts the laser code into a digital tag; a built-in blockchain database stores tagged data and historical maintenance records of the ship equipment; the health status of the ship equipment is evaluated, and the basic data in the tagged data is associated with the historical maintenance records to generate associated data of the ship equipment; in combination with the associated data and the health status evaluation result, the optimal maintenance scheme is screened based on the maintenance scheme priority score; the optimal maintenance scheme is transmitted to step four; The generation process of the health status evaluation result is as follows: The data hub module receives the tagged data and extracts the historical maintenance records from the blockchain database, the tagged data is combined with the historical maintenance records, the health status of the ship equipment is evaluated, and the health status evaluation result is generated, the health status evaluation result is measured by a health status evaluation index, and the formula is: wherein H is the health state evaluation index of the ship equipment, M is the matching degree of historical maintenance records, t is the time from the last similar failure, is a weight coefficient, is the weight of the ith basic data, and n is the number of basic data, is the ith basic data processed by the edge perception module; is the design threshold corresponding to the basic data, which is stored in the factory parameters of the ship equipment. The health status evaluation result is returned to the blockchain database; Step four, verification execution, after receiving the optimal maintenance scheme, a three-dimensional simulation model is constructed based on the factory parameters and position coordinates in the digital tag; the feasibility of the optimal maintenance scheme is verified based on the three-dimensional simulation model, and the simulation operation is further performed after the verification is passed, and the operation instruction is generated after the simulation is passed; the ship equipment is maintained according to the operation instruction, the maintenance record is taken as the latest historical maintenance record, and the latest historical maintenance record is uploaded to the blockchain database.

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