Intelligent ship business dynamic assembly methods, systems, storage media and electronic devices

Abstract

This invention discloses a method, system, storage medium, and electronic device for dynamically assembling intelligent ship business processes. The method includes the following steps: classifying ship data into APIs based on the type and / or correlation and / or accuracy of the ship data; identifying the rule engine for each API category based on the ship business requirements corresponding to each API category; identifying the associated API categories of the business to be assembled based on the goal of the business to be assembled; constructing the rule engine for the business to be assembled based on the correlation and / or conflict of the rule engines corresponding to different associated API categories; and constructing the program workflow for the business to be assembled based on the rule engine and the goal of the business to be assembled, and encapsulating it to obtain a new business. This invention solves the problems of poor scalability, long development cycle, and lack of industry-specificity in existing intelligent ship technologies, which lead to poor flexibility and high difficulty in business development.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent ships, and particularly relates to an intelligent ship service dynamic assembly method, system, storage medium and electronic device. BACKGROUND

[0002] With the digital development of the shipping industry, customer demand is increasingly diversified, such as real-time monitoring of ship status, intelligent scheduling, energy efficiency optimization, supply chain collaboration, etc. Traditional systems are difficult to quickly adapt to new business scenarios. Therefore, a ship management system that can support multiple services is applied.

[0003] The current ship service management system has the following obvious defects: (1) usually only supports basic functions (such as route management, cargo tracking), and new service modules need to be redeveloped, which is long in cycle and high in cost, and cannot meet the rapidly changing needs of customers; (2) ship operation data (such as AIS data, fuel consumption data, equipment status data) are stored in scattered storage, lack of unified data warehouse and standardized interface (API), and are difficult to share and combine across services; (3) business logic is usually implemented in a hard-coded manner, and code needs to be modified for rule changes, lacking a flexible rule engine to support dynamic adjustment, resulting in slow system response and high maintenance costs; (4) the relevance of different business scenarios (such as ship early warning, port collaboration) is not abstracted, and each development needs to start from scratch, which is inefficient.

[0004] Some ship enterprises try to solve the above problems by customizing development or introducing a general low-code platform, which can achieve specific needs, but the code reuse rate is low, and subsequent expansion still requires repeated development. Moreover, it lacks ship industry specificity and cannot directly adapt to ship data characteristics (such as spatiotemporal relevance, device heterogeneity), and the adaptability of the rule engine and the business template is poor.

[0005] In order to solve the problems of poor expansion of ship services, long development cycle, lack of ship industry specificity, poor flexibility of ship services, and high difficulty of business development, an intelligent ship service dynamic assembly method, system, storage medium and electronic device are proposed. SUMMARY

[0006] The present application proposes an intelligent ship service dynamic assembly method, system, storage medium and electronic device to at least solve the problems of poor expansion of ship services, long development cycle, lack of ship industry specificity, poor flexibility of ship services, and high difficulty of business development in existing intelligent ship technology.

[0007] According to an embodiment of the present application, an intelligent ship service dynamic assembly method is provided, comprising:

[0008] API classifying ship data according to the type of ship data and / or the relevance of ship data and / or the accuracy of ship data;

[0009] identifying a rule engine for each API category according to the corresponding ship service demand of each API category;

[0010] identifying the associated API category of the to-be-assembled service according to the target of the to-be-assembled service;

[0011] constructing a rule engine for the to-be-assembled service according to the relevance of the rule engines corresponding to different associated API categories and / or the conflict of the rule engines corresponding to different associated API categories;

[0012] constructing a program workflow for the to-be-assembled service according to the rule engine for the to-be-assembled service and the target of the to-be-assembled service, and encapsulating the new service by the program workflow, i.e. realizing dynamic assembly of services.

[0013] In a preferred embodiment, the API classification includes a first API category and a second API category, the first API category includes any one or more combinations of a basic data category, a channel data category, a pre-warning data category, a verification data category, and a comprehensive analysis category; the second API category is a subdivision type divided in the first API category, wherein the basic data category includes any one or more sub-categories of a navigation data category and a communication data category, the channel data category includes any one or more sub-categories of a climate environment category and a regional data category, the pre-warning data category includes any one or more sub-categories of a collision avoidance pre-warning category, a pirate pre-warning category, a navigation anomaly pre-warning category, and a personnel anomaly pre-warning category, the comprehensive analysis category includes any one or more sub-categories of a navigation anomaly analysis category, a personnel anomaly analysis category, a navigation route planning category, and an energy saving analysis category; each second API category contains one or more data type sets.

[0014] In a preferred embodiment, the API classification of the ship data according to the type of the ship data and / or the relevance of the ship data and / or the accuracy of the ship data includes the following steps:

[0015] calculating a data type relevance indication value according to the sensor type and / or the data format and / or the data source corresponding to the ship data;

[0016] calculating a data relevance indication value according to the time continuity of the ship data and / or the change relevance of the data;

[0017] calculating a data accuracy indication value according to the real-time nature of the ship data and / or the precision of the ship data;

[0018] calculating a data-in-category indication value according to the data type relevance indication value and / or the data relevance indication value and / or the data accuracy indication value;

[0019] API grouping each data according to the data-in-category indication value of each ship data and the indication value range corresponding to the preset API classification.

[0020] In a preferred embodiment, the rule engine of each API category according to the corresponding ship business demand of each API category comprises the steps of:

[0021] Building a trigger rule according to the input data of the corresponding ship business demand of each API category;

[0022] Building a data execution rule according to the data usage of the corresponding ship business demand of each API category;

[0023] Building a notification rule according to the notification requirement of the corresponding ship business demand of each API category;

[0024] Building a task rule according to the time delay requirement of the corresponding ship business demand of each API category;

[0025] Building a verification rule according to the accuracy requirement of the corresponding ship business demand of each API category;

[0026] Identifying the rule engine of each API category according to the trigger rule and / or data execution rule and / or notification rule and / or task rule and / or verification rule.

[0027] In a preferred embodiment, the associated API category of the to-be-assembled business is identified according to the target of the to-be-assembled business, comprising:

[0028] Identifying the first API category to which the to-be-assembled business belongs;

[0029] Calculating the API association value according to the degree of association between the function corresponding to the program target and the second API category;

[0030] Selecting one or more API categories with an API association value greater than a preset threshold as the associated API category of the to-be-assembled business.

[0031] In a preferred embodiment, the rule engine of the to-be-assembled business is constructed according to the association of the rule engines corresponding to different associated API categories and / or the conflict of the rule engines corresponding to different associated API categories, comprising the steps of:

[0032] Calculating a rule engine association index according to the association between the rule engines corresponding to different associated API categories;

[0033] Calculating a rule engine conflict index according to the conflict between the rule engines corresponding to different associated API categories;

[0034] Constructing the rule engine of the to-be-assembled business according to the rule engine association index and / or the rule engine conflict index and a preset fusion rule.

[0035] In a preferred embodiment, the program workflow of the to-be-assembled business is constructed according to the rule engine of the to-be-assembled business and the target of the to-be-assembled business, comprising the steps of:

[0036] The basic business workflow is constructed according to the trigger rule and / or the data execution rule and / or the notification rule and / or the task rule and / or the verification rule in the rule engine of the to-be-assembled business;

[0037] The basic business workflow is adjusted according to the target and requirements of the to-be-assembled business to obtain the program workflow of the to-be-assembled business.

[0038] According to another embodiment of the present application, an intelligent ship business dynamic assembly system is provided, comprising:

[0039] An application program interface classification module is configured to classify the ship data according to the type of the ship data and / or the relevance of the ship data and / or the accuracy of the ship data;

[0040] An API and rule engine association module is configured to identify the rule engine of each API category according to the ship business requirement corresponding to each API category;

[0041] An API relevance identification module is configured to identify the relevant API category of the to-be-assembled business according to the target of the to-be-assembled business;

[0042] A rule engine construction module is configured to construct the rule engine of the to-be-assembled business according to the relevance of the rule engine corresponding to different relevant API categories and / or the conflict of the rule engine corresponding to different relevant API categories;

[0043] A business dynamic assembly module is configured to construct the program workflow of the to-be-assembled business according to the rule engine of the to-be-assembled business and the target of the to-be-assembled business, and encapsulate the new business to achieve the dynamic assembly of the business.

[0044] According to another embodiment of the present application, a computer readable storage medium is provided, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the above-mentioned business dynamic assembly method.

[0045] According to another embodiment of the present application, an electronic device is provided, comprising:

[0046] At least one processor;

[0047] and a memory in communication connection with the at least one processor;

[0048] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the business dynamic assembly method.

[0049] The intelligent ship business dynamic assembly method, system, storage medium and electronic equipment of the present application have the following advantages:

[0050] (1) The API is divided into a first category and a second category, and the ship data is classified according to the type of the ship data and / or the relevance of the ship data and / or the accuracy of the ship data. Compared with the traditional ship business management technical solution, the massive and chaotic ship data can be effectively classified and associated according to different dimensions, which not only facilitates quick identification of the API category corresponding to the ship business and improves the subsequent business assembly efficiency, but also effectively establishes the relevance between different ship businesses, breaks down the barriers between ship businesses, and realizes the sharing of ship businesses.

[0051] (2) According to the triggering rule and / or data execution rule and / or notification rule and / or task rule and / or verification rule of each API category corresponding to the ship business demand, the rule engine of each API category is identified. Compared with the traditional ship business management technical solution, the rule matching each API category can be effectively identified, thereby effectively improving the speed of subsequent API calling and new ship business assembly, and reducing the ship business development cycle.

[0052] (3) According to the target of the to-be-assembled business, the associated API category of the to-be-assembled business is identified, and the rule engine of the to-be-assembled business is constructed according to the relevance and conflict of the rule engines corresponding to different associated API categories. Compared with the traditional business assembly technical solution, the ship business generation speed can be effectively improved, thereby reducing the ship business development cycle and development difficulty, and the scalability and flexibility of the ship business are improved through the association and conflict fusion of different rules, which facilitates subsequent ship business changes or business upgrades. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a flowchart of the intelligent ship business dynamic assembly method of the embodiment of the present application;

[0054] Figure 2 is a flowchart of step S01 of the embodiment of the present application;

[0055] Figure 3 is a flowchart of step S02 of the embodiment of the present application;

[0056] Figure 4 is a flowchart of step S03 of the embodiment of the present application;

[0057] Figure 5is a flow chart of step S04 of the embodiment of the present application;

[0058] Figure 6 is a flow chart of step S05 of the embodiment of the present application;

[0059] Figure 7 is an architecture diagram of the intelligent ship service dynamic assembly system according to an embodiment of the present application;

[0060] Figure 8 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] The present application will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present application. These are within the scope of protection of the present application.

[0062] According to an embodiment of the present application, an intelligent ship service dynamic assembly method is provided, and a flow chart is shown as Figure 1 , which comprises:

[0063] Step S01, API classification of ship data according to the type of ship data and / or the relevance of ship data and / or the accuracy of ship data;

[0064] Step S02, identifying the rule engine of each API category according to the ship service demand corresponding to each API category;

[0065] Step S03, identifying the associated API category of the to-be-assembled service according to the target of the to-be-assembled service;

[0066] Step S04, constructing the rule engine of the to-be-assembled service according to the relevance of the rule engines corresponding to different associated API categories and / or the conflict of the rule engines corresponding to different associated API categories;

[0067] Step S05, constructing the program workflow of the to-be-assembled service according to the rule engine of the to-be-assembled service and the target of the to-be-assembled service, and encapsulating the new service to achieve service dynamic assembly.

[0068] In a preferred embodiment, the API classification includes a first API category and a second API category, the first API category includes any one or more combinations of a basic data class, a channel data class, a pre-warning data class, a verification data class, a comprehensive analysis class; the second API category is a subdivision type divided in the first API category, wherein the basic data class includes any one or more sub-classes of a navigation data class, a communication data class, the channel data class includes any one or more sub-classes of a climate environment class, a regional data class, the pre-warning data class includes any one or more sub-classes of a collision avoidance pre-warning class, a pirate pre-warning class, a navigation anomaly pre-warning class, a personnel anomaly pre-warning class, the comprehensive analysis class includes any one or more sub-classes of a navigation anomaly analysis class, a personnel anomaly analysis class, a navigation route planning class, an energy saving analysis class; each second API category contains one or more data type sets. In this embodiment, the API is classified according to the use scene and ship configuration of different ships, wherein the first API category includes but is not limited to a basic data class, a channel data class, a pre-warning data class, a verification data class, and a comprehensive analysis class.

[0069] The second API category is a subdivision type divided in the first API category, wherein the first API category: the basic data class includes but is not limited to the second API category of a navigation data class, a communication data class, etc., the first API category: the channel data class includes but is not limited to the second API category of a climate environment class, a regional data class, etc., the first API category: the pre-warning data class includes but is not limited to the second API category of a collision avoidance pre-warning class, a pirate pre-warning class, a navigation anomaly pre-warning class, a personnel anomaly pre-warning class, etc., the first API category: the comprehensive analysis class includes but is not limited to the second API category of a navigation anomaly analysis class, a personnel anomaly analysis class, a navigation route planning class, an energy saving analysis class, etc.

[0070] Each second API category contains one or more data type sets, for example, the navigation data category includes position data, speed data, heading data, and other data sets, the communication data category includes communication link data, communication duration, signal strength, packet loss rate, and other data sets, the climate environment category includes wind, air pressure, temperature, humidity, and other data sets, the region data category includes regional piracy information, regional driving restriction data, regional supply data, and other data sets, the collision avoidance warning category includes obstacle data, collision prediction data, collision warning data, and other data sets, the piracy warning category includes piracy distribution data, piracy prediction data, piracy warning data, and other data sets, the navigation anomaly warning category includes navigation mutation data, navigation anomaly prediction data, navigation anomaly warning, and other data sets, the personnel anomaly warning category includes personnel behavior analysis data, personnel anomaly prediction data, personnel anomaly warning data, and other data sets, the navigation anomaly analysis category includes navigation anomaly cause analysis, navigation anomaly impact analysis, navigation anomaly correction data, and other data sets, the personnel anomaly analysis category includes personnel anomaly cause analysis, personnel anomaly impact analysis, personnel anomaly correction data, and other data sets, the navigation route planning category includes starting point and destination data, navigation route planning data, navigation route optimization data, and other data sets, and the energy saving analysis category includes fuel consumption data, navigation route and energy saving optimization data, oil supply data, and other data sets.

[0071] In a preferred embodiment, the step S01, the ship data is API classified according to the type of ship data and / or the relevance of ship data and / or the accuracy of ship data, the flow chart is as shown in Figure 2 The steps include:

[0072] Step S011, calculate the data type relevance indication value according to the sensor type corresponding to the ship data and / or the data format and / or the source of the data;

[0073] Step S012, calculate the data relevance indication value according to the time continuity of the ship data and / or the change relevance of the data;

[0074] Step S013, calculate the data accuracy indication value according to the real-time of the ship data and / or the precision of the ship data;

[0075] Step S014, calculate the data class indication value according to the data type relevance indication value and / or the data relevance indication value and / or the data accuracy indication value;

[0076] Step S015, according to the data class indication value of each ship data and the indication value range corresponding to the preset API classification, the data is API grouped.

[0077] In this embodiment, the data type relevance indication value is calculated according to the positive correlation between the consistency of the sensor type corresponding to the ship data and the data type relevance indication value, the positive correlation between the consistency of the data format and the data type relevance indication value, the positive correlation between the consistency of the source of the ship data (the source consistency is calculated according to the consistency of the data acquisition position and / or the consistency of the sensor equipment corresponding to the acquired data) and the data type relevance indication value, the positive correlation between the consistency of the sensor type corresponding to the ship data and the consistency of the data format and the data type relevance indication value, the positive correlation between the consistency of the sensor type corresponding to the ship data and the consistency of the source of the ship data and the data type relevance indication value, the positive correlation between the consistency of the data format and the consistency of the source of the ship data and the data type relevance indication value, the positive correlation between the consistency of the sensor type corresponding to the ship data, the consistency of the data format and the consistency of the source of the ship data and the data type relevance indication value, any one of which is represented by variable x.

[0078] The data relevance indication value is calculated according to the positive correlation between the time continuity of the ship data (the time continuity is calculated according to the consistency of the time or the interval relationship of the time before and after, and the shorter the time interval, the greater the time continuity) and the data relevance indication value, the positive correlation between the change relevance of the data (the change relevance is calculated according to the relevant change amount caused by the unit change amount of the data, and the greater the relevant change amount caused by the unit time, the greater the change relevance) and the data relevance indication value, the positive correlation between the time continuity of the ship data and the change relevance of the data and the data relevance indication value, any one of which is represented by variable y.

[0079] The calculation of the data accuracy indication value based on the real-time performance and / or precision of the ship data can be performed using any one of the following methods: calculating the data accuracy indication value based on the positive correlation between the real-time consistency of the ship data (calculated based on the difference in data delay, where a larger difference in data delay indicates lower real-time consistency); calculating the data accuracy indication value based on the positive correlation between the precision consistency of the ship data (calculated based on the difference in ship data error, where a smaller difference in error indicates higher precision consistency); or calculating the data accuracy indication value based on the positive correlation between the real-time consistency of the ship data and the precision consistency of the ship data and the data accuracy indication value. The data accuracy indication value is represented by the variable z.

[0080] The calculation of the intra-class indicator value based on the data type relevance indicator value and / or data correlation indicator value and / or data accuracy indicator value is based on the positive correlation between the intra-class indicator value and the data type relevance indicator value and / or data correlation indicator value and / or data accuracy indicator value. The intra-class indicator value is represented by the variable p.

[0081] Examples A1 to A7 illustrate different implementation methods for calculating the indicator value within a data class, as follows:

[0082] Example A1: Calculate the data class indicative value based on the positive correlation between the data type correlation indicative value and the data class indicative value.

[0083] Specifically, a data type correlation indicator value x is calculated based on the sensor type and / or data format and / or data source corresponding to the ship data; an intra-data-class indicator value p is calculated based on the positive correlation between the data type correlation indicator value x and the intra-data-class indicator value. In a preferred embodiment, the intra-data-class indicator value p is calculated as w1·x. w2 +w3, where w1 (w1>0), w2 (w2>0), and w3 are pre-trained calculation coefficients. In this embodiment, the video and image data collected by the ship's CCTV both originate from CCTV, and the data source consistency is calculated as 1 (normalized). Based on the positive correlation between the data source and the data type correlation indicator value, the data type correlation indicator value x=1 (the pre-trained calculation coefficient is 1). The pre-trained calculation coefficients w1=1, w2=1, and w3=0, then the in-class indicator value p=w1·x is calculated. w2 +w3=1×1+0=1.

[0084] Example A2: Calculate the data class indicator value based on the positive correlation between the data correlation indicator value and the data class indicator value.

[0085] Specifically, a data correlation indicator value y is calculated based on the temporal continuity and / or the correlation of changes in the ship data; an intra-class indicator value p is calculated based on the positive correlation between the data correlation indicator value y and the intra-class indicator value. In a preferred embodiment, the intra-class indicator value p is calculated as w4·y. w5 +w6, where w4 (w4>0), w5 (w5>0), and w6 are pre-trained calculation coefficients. In this embodiment, the ship's CCTV collects both video and image data. The image data is collected simultaneously with the video data at the sampling frequency. The temporal continuity between the image and video data is calculated to be 0.9 (normalized according to a preset temporal continuity threshold). Based on the positive correlation between the image and the data correlation indicator value, the data correlation indicator value y = 0.9 (pre-trained calculation coefficient is 1). The pre-trained calculation coefficients w4 = 1, w5 = 1, and w6 = 0. Therefore, the in-class indicator value p = w4·y is calculated. w5 +w6=1×0.9+0=0.9.

[0086] Example A3: Calculate the data class indicator value based on the positive correlation between the data accuracy indicator value and the data class indicator value.

[0087] Specifically, a data accuracy indicator value z is calculated based on the real-time performance and / or accuracy of the ship data; an intra-class indicator value p is calculated based on the positive correlation between the data accuracy indicator value z and the intra-class indicator value. In a preferred embodiment, the intra-class indicator value p is calculated as p = w7·z. w8 +w9, where w7 (w7>0), w8 (w8>0), and w9 are pre-trained calculation coefficients. In this embodiment, the transmission delay of video data is greater than that of image data due to the limited bandwidth of ship satellite communication. The delay difference between video data and image data collected in a certain instance is calculated to be 0.2 seconds. Based on this, the real-time consistency is calculated to be 0.6 (normalized according to a preset delay difference threshold). According to the positive correlation between real-time consistency and data accuracy indicator, the data accuracy indicator value z=0.6 (the pre-trained calculation coefficient is 1). The pre-trained calculation coefficients w7=1, w8=1, and w9=0. Then, the in-class indicator value p=w7·z is calculated. w8 +w9=1×0.8+0=0.8.

[0088] Example A4: Calculate the data class indicator value based on the positive correlation between the data type relevance indicator value, the data association indicator value, and the data class intra-indicator value.

[0089] Specifically, a data type correlation indicator value x is calculated based on the sensor type and / or data format and / or data source corresponding to the ship data; a data correlation indicator value y is calculated based on the temporal continuity and / or the correlation of data changes in the ship data; and a data class intra-indicator value p is calculated based on the positive correlation between the data type correlation indicator value x, the data correlation indicator value y, and the data class intra-indicator value. In a preferred embodiment, the data class intra-indicator value p is calculated as p = w10·x. w11 +w12·y w13 +w14, where w10 (w10>0), w11 (w11>0), w12 (w12>0), w13 (w13>0), and w14 are computational coefficients obtained through prior training. In this embodiment, both video and image data collected by the ship's CCTV originate from CCTV, and the data source consistency is calculated as 1 (normalized). Based on the positive correlation between the image data and the data type correlation indicator, the data type correlation indicator value x = 1 (the pre-trained calculation coefficient is 1). The image data is collected simultaneously with the video data at the same sampling frequency, and its temporal continuity with the video data is calculated as 0.9 (normalized according to a preset temporal continuity threshold). Based on the positive correlation between the image data and the data correlation indicator, the data correlation indicator value y = 0.9 (the pre-trained calculation coefficient is 1). The pre-trained calculation coefficients w10 = 0.6, w11 = 1, w12 = 0.4, w13 = 1, and w14 = 0. Therefore, the data class indicative value p = w10·x is calculated. w11 +w12·y w13 +w14 = 0.6 × 1 + 0.4 × 0.9 + 0 = 0.96. In another preferred embodiment, the in-class indicator value p = w15·x is calculated. w16 ·y w17+w18, where w15 (w15>0), w16 (w16>0), w17 (w17>0), and w18 are pre-trained calculation coefficients. In this embodiment, the video and image data collected by the ship's CCTV both originate from CCTV, and the data source consistency is calculated as 1 (normalized). Based on the positive correlation between the image data and the data type correlation indicator, the data type correlation indicator value x=1 (pre-trained calculation coefficient is 1). The video and image data collected by the ship's CCTV are also included. The image data is collected simultaneously with the video data at the sampling frequency, and its temporal continuity with the video data is calculated as 0.9 (normalized according to a preset temporal continuity threshold). Based on the positive correlation between the image data and the data correlation indicator, the data correlation indicator value y=0.9 (pre-trained calculation coefficient is 1). The pre-trained calculation coefficients w15=1.06, w16=1, w17=1, and w18=0. Therefore, the data class-specific indicator value p=w15·x is calculated. w16 ·y w17 +w18=1.06×1×0.9+0=0.954.

[0090] Example A5: Calculate the data class indicator value based on the positive correlation between the data type relevance indicator value, the data accuracy indicator value, and the data class indicator value.

[0091] Specifically, a data type correlation indicator value x is calculated based on the sensor type and / or data format and / or data source corresponding to the ship data; a data accuracy indicator value z is calculated based on the real-time performance and / or accuracy of the ship data; and a data class-specific indicator value p is calculated based on the positive correlation between the data type correlation indicator value x, the data accuracy indicator value z, and the data class-specific indicator value. In a preferred embodiment, the data class-specific indicator value p is calculated as p = w19·x. w20 +w21·z w22+w23, where w19 (w19>0), w20 (w20>0), w21 (w21>0), w22 (w22>0), and w23 are calculated coefficients obtained through prior training. In this embodiment, both video and image data collected by the ship's CCTV originate from CCTV. The data source consistency is calculated as 1 (normalized). Based on the positive correlation between the data source and the data type correlation indicator, the data type correlation indicator x = 1 (the pre-trained coefficient is 1). Given the limited bandwidth of the ship's satellite communication, the transmission delay of video data is greater than that of image data. The delay difference between a given video and image data collection is calculated to be 0.2 seconds. Based on this, the real-time consistency is calculated as 0.6 (normalized according to a preset delay difference threshold). Based on the positive correlation between real-time consistency and the data accuracy indicator, the data accuracy indicator z = 0.6 (the pre-trained coefficient is 1). The pre-trained coefficients w19 = 0.7, w20 = 1, w21 = 0.3, w22 = 1, and w23 = 0. Therefore, the data class indicative value p = w19·x is calculated. w20 +w21·z w22 +w23=0.7×1+0.3×0.6+0=0.88. In another preferred embodiment, the in-class indicator value p=w24·x is calculated. w25 ·z w26 +w27, where w24 (w24>0), w25 (w25>0), w26 (w26>0), and w27 are calculated coefficients obtained through prior training. In this embodiment, both video and image data collected by the ship's CCTV originate from CCTV, and the data source consistency is calculated as 1 (normalized). Based on the positive correlation between the data source and the data type correlation indicator, the data type correlation indicator value x = 1 (the pre-trained calculation coefficient is 1). Given the limited bandwidth of the ship's satellite communication, the transmission delay of video data is greater than that of image data. The delay difference between the video and image data collected in a given instance is calculated to be 0.2 seconds, and the real-time consistency is calculated as 0.6 (normalized according to a preset delay difference threshold). Based on the positive correlation between real-time consistency and the data accuracy indicator, the data accuracy indicator value z = 0.6 (the pre-trained calculation coefficient is 1). The pre-trained calculation coefficients w24 = 1.5, w25 = 1, w26 = 1, and w27 = 0. Therefore, the data class indicative value p = w24·x is calculated. w25 ·z w26 +w27=1.5×1×0.6+0=0.9.

[0092] Example A6: Calculate the data class indicator value based on the positive correlation between the data correlation indicator value, the data accuracy indicator value, and the data class indicator value.

[0093] Specifically, a data correlation indicator value y is calculated based on the temporal continuity and / or the correlation of changes in the ship data; a data accuracy indicator value z is calculated based on the real-time performance and / or the precision of the ship data; and a data class-specific indicator value p is calculated based on the positive correlation between the data correlation indicator value y, the data accuracy indicator value z, and the data class-specific indicator value. In a preferred embodiment, the data class-specific indicator value p is calculated as p = w²⁸·y. w29 +w30·z w31 +w32, where w28 (w28>0), w29 (w29>0), w30 (w30>0), w31 (w31>0), and w32 are computational coefficients obtained through prior training. In this embodiment, the ship's CCTV collects both video and image data. The image data is collected simultaneously with the video data at the same sampling frequency. The temporal continuity between the image and video data is calculated to be 0.9 (normalized according to a preset temporal continuity threshold). Based on the positive correlation between the image and video data, the data correlation indicator value y = 0.9 (calculation coefficient 1 obtained through pre-training) is calculated. Given the limited bandwidth of the ship's satellite communication, the transmission delay of the video data is greater than that of the image data. The delay difference between the video and image data collected in a given instance is calculated to be 0.2 seconds. Based on this, the real-time consistency is calculated to be 0.6 (normalized according to a preset delay difference threshold). Based on the positive correlation between real-time consistency and the data accuracy indicator value, the data accuracy indicator value z = 0.6 (calculation coefficient 1 obtained through pre-training) is calculated. The pre-trained calculation coefficients w28 = 0.7, w29 = 1, w30 = 0.3, w31 = 1, and w32 = 0.1 are used to calculate the data class-specific indicator value p = w28·y. w29 +w30·z w31 +w32=0.7×0.9+0.3×0.6+0.1=0.81. In another preferred embodiment, the in-class indicator value p=w33·y is calculated. w34 ·z w35+w36, where w33 (w33>0), w34 (w34>0), w35 (w35>0), and w36 are calculated coefficients obtained through prior training. In this embodiment, the ship's CCTV collects both video and image data. Image data is collected simultaneously with video data at the same sampling frequency. The temporal continuity between image and video data is calculated to be 0.9 (normalized according to a preset temporal continuity threshold). Based on the positive correlation between image and video data, the data correlation indicator value y = 0.9 (calculation coefficient 1 obtained through pre-training) is calculated. Given the limited bandwidth of the ship's satellite communication, the transmission delay of video data is greater than that of image data. The delay difference between video and image data in a given collection is calculated to be 0.2 seconds. Based on this, the real-time consistency is calculated to be 0.6 (normalized according to a preset delay difference threshold). Based on the positive correlation between real-time consistency and data accuracy indicator value, the data accuracy indicator value z = 0.6 (calculation coefficient 1 obtained through pre-training) is calculated. The pre-trained calculation coefficients w33 = 1.5, w34 = 1, w35 = 1, and w36 = 0. Therefore, the data class-specific indicator value p = w33·y is calculated. w34 ·z w35 +w36=1.5×0.9×0.6+0=0.81.

[0094] Example A7: Calculate the data class indicator value based on the positive correlation between the data type relevance indicator value, the data association indicator value, the data accuracy indicator value, and the data class indicator value.

[0095] Specifically, a data type correlation indicator value x is calculated based on the sensor type and / or data format and / or data source corresponding to the ship data; a data correlation indicator value y is calculated based on the temporal continuity and / or the correlation of data changes in the ship data; a data accuracy indicator value z is calculated based on the real-time performance and / or the precision of the ship data; and a data class intra-indicator value p is calculated based on the positive correlation between the data type correlation indicator value x, the data correlation indicator value y, the data accuracy indicator value z, and the data class intra-indicator value. In a preferred embodiment, the data class intra-indicator value p is calculated as p = w37·x w38 +w39·y w40 +w41·z w42+w43, where w37 (w37>0), w38 (w38>0), w39 (w39>0), w40 (w40>0), w41 (w41>0), w42 (w42>0), and w43 are pre-trained calculation coefficients. In this embodiment, the video and image data collected by the ship's CCTV both originate from CCTV, and the data source consistency is calculated as 1 (normalized). Based on its positive correlation with the data type correlation indicator value, the data type correlation indicator value x=1 (the pre-trained calculation coefficient is 1). The image data is collected simultaneously with the video data according to the sampling frequency, and its temporal continuity with the video data is calculated as 0.9 (normalized according to a preset temporal continuity threshold). Based on its positive correlation with the data correlation indicator value, the data correlation indicator value y=0.9 (the pre-trained calculation coefficient is 1). On the ship... Given the limited bandwidth of satellite communication, the transmission delay of video data is greater than that of image data. The delay difference between a given acquisition of video and image data is calculated to be 0.2 seconds. Based on this, the real-time consistency is calculated to be 0.6 (normalized according to a preset delay difference threshold). According to the positive correlation between real-time consistency and data accuracy indicator, the data accuracy indicator z = 0.6 (with a pre-trained coefficient of 1). The pre-trained coefficients w37 = 0.5, w38 = 1, w39 = 0.3, w40 = 1, w41 = 0.2, w42 = 1, and w43 = 0. The in-class indicator p = w37·x is then calculated. w38 +w39·y w40 +w41·z w42 +w43 = 0.5 × 1 + 0.3 × 0.9 + 0.2 × 0.6 + 0 = 0.89. In another preferred embodiment, the in-class indicator value p = w44·x is calculated. w45 ·y w46 ·z w47+w48, where w44 (w44>0), w45 (w45>0), w46 (w46>0), w47 (w47>0), and w48 are pre-trained calculation coefficients. In this embodiment, the video and image data collected by the ship's CCTV both originate from CCTV, and the data source consistency is calculated as 1 (normalized). Based on the positive correlation between the image data and the data type correlation indicator, the data type correlation indicator value x=1 (pre-trained calculation coefficient is 1). The image data is collected simultaneously with the video data according to the sampling frequency, and its temporal continuity with the video data is calculated as 0.9 (normalized according to a preset temporal continuity threshold). Based on the positive correlation between the image data and the data correlation indicator value, the data correlation indicator value y=0.9 (pre-trained calculation coefficient is 1). (The number is 1). Under the condition of limited satellite communication bandwidth on ships, the transmission delay of video data is greater than that of image data. The delay difference between video data and image data acquired in a certain period is calculated to be 0.2 seconds. Based on this, the real-time consistency is calculated to be 0.6 (normalized according to the preset delay difference threshold). According to the positive correlation between real-time consistency and data accuracy indicator, the data accuracy indicator z=0.6 (the calculation coefficient obtained in the pre-training is 1). The calculation coefficients obtained in the pre-training are w44=1.67, w45=1, w46=1, w47=1, w48=0. The data class indicator p=w44·x is calculated. w45 ·y w46 ·z w47 +w48=1.67×1×0.9×0.6+0=0.9.

[0096] The data class indication value of any two sets of ship data can be calculated according to the method described in any one of the embodiments A1 to A7.

[0097] The step of grouping ship data into API categories based on the data category indication values ​​of each ship data and the indication value range corresponding to the preset API category includes: grouping ship data into a first API category based on the data category indication value range corresponding to the preset first API category; grouping ship data into a second API category based on the data category indication value range corresponding to the preset second API category; wherein the data category indication value range corresponding to the second API category is a subset of the data category indication value range corresponding to the first API category.

[0098] In a preferred embodiment, step S02 involves identifying the rule engine for each API category based on the ship business requirements corresponding to each API category, as shown in the flowchart below. Figure 3 As shown, the steps include:

[0099] Step S021: Construct trigger rules based on the input data of the ship business requirements corresponding to each API category;

[0100] Step S022: Construct data execution rules based on the data usage methods of the ship business requirements corresponding to each API category;

[0101] Step S023: Construct notification rules based on the notification requirements of the ship business needs corresponding to each API category;

[0102] Step S024: Construct task rules based on the latency requirements of the ship business needs corresponding to each API category;

[0103] Step S025: Construct verification rules based on the accuracy requirements of the ship business needs corresponding to each API category;

[0104] Step S026: Identify the rule engine for each API category based on triggering rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules.

[0105] In this embodiment, the step of constructing triggering rules based on the input data of the ship business requirements corresponding to each API category is to obtain triggering rules based on the triggering rules of the ship data corresponding to the first API category, or based on the triggering rules of the ship data corresponding to the second API category, or based on the combination of the triggering rules of the ship data corresponding to the first API category and the triggering rules of the ship data corresponding to the second API category.

[0106] The data execution rules are constructed based on the data usage methods of the ship business needs corresponding to each API category. This is done by obtaining data execution rules based on the acquisition and processing methods of ship data corresponding to the first API category, or based on the acquisition and processing methods of ship data corresponding to the second API category, or based on a combination of the acquisition and processing methods of ship data corresponding to the first API category and the acquisition and processing methods of ship data corresponding to the second API category.

[0107] The construction of notification rules based on the notification requirements of ship business needs corresponding to each API category is to obtain notification rules based on the notification requirement rules of ship data corresponding to the first API category, or based on the notification requirement rules of ship data corresponding to the second API category, or based on the combination of the notification requirement rules of ship data corresponding to the first API category and the notification requirement rules of ship data corresponding to the second API category.

[0108] The task rules are constructed based on the latency requirements of the ship business needs corresponding to each API category. This is done by obtaining the task rules based on the overall latency requirements of the ship data corresponding to the first API category, or based on the overall latency requirements of the ship data corresponding to the second API category, or based on the combination of the overall latency requirements of the ship data corresponding to the first API category and the overall latency requirements of the ship data corresponding to the second API category.

[0109] The verification rules are constructed based on the accuracy requirements of the ship business needs corresponding to each API category. This is done by obtaining the verification rules based on the overall data error requirements of the ship data corresponding to the first API category, or based on the overall data error requirements of the ship data corresponding to the second API category, or based on the combination of the overall data error requirements of the ship data corresponding to the first API category and the overall data error requirements of the ship data corresponding to the second API category.

[0110] The rule engine for each API category is derived from the triggering rules and / or data execution rules and / or notification rules and / or task rules and / or validation rules for each API category.

[0111] In a preferred embodiment, step S03 involves identifying the associated API category of the service to be assembled based on its target. The flowchart is as follows: Figure 4 As shown, it includes:

[0112] Step S031: Identify the first API category to which the business to be assembled belongs;

[0113] Step S032: Calculate the API relevance value based on the degree of relevance between the function corresponding to the program target and the second API category;

[0114] Step S033: Select one or more API categories with an API relevance value greater than a preset threshold as the associated API categories for the business to be assembled.

[0115] In this embodiment, the business requirements of the intelligent ship and the new business to be assembled (i.e., intelligent business that has not yet been developed on existing ships) are obtained, and the first API category to which the business to be assembled belongs is identified.

[0116] The step of calculating the API relevance value based on the degree of association between the function corresponding to the program target and the second API category includes: identifying the function corresponding to the target of the business program to be assembled; calculating the degree of association between the function corresponding to the program target and the second API category based on the positive correlation between the consistency of data type and / or data processing and / or data transmission and / or data usage of the function and data corresponding to the program target and the second API category; and calculating the API relevance value based on the positive correlation between the degree of association between the function corresponding to the program target and the second API category and the API relevance value, wherein the API relevance value is represented by the variable m.

[0117] Based on the ship usage scenario and the external environment, a preset correlation threshold M is set, and one or more API categories with an API correlation value greater than the preset threshold M are selected as the associated API categories of the business to be assembled.

[0118] Taking ship communication anomaly warning as an example, a certain ship has not yet developed this intelligent application. The business requirement is to send the abnormal information to the shore for alarm when the ship's communication is abnormal.

[0119] The service to be assembled was identified as a ship communication anomaly warning, and its first API category is the basic data category and the warning data category.

[0120] The function corresponding to the target of the business program to be assembled is to identify whether the communication is abnormal and to send the abnormal alarm to the shore.

[0121] The consistency of data type and data processing between the function corresponding to the target of the business program to be assembled and the communication data class in the basic data category is 1 (normalized). The correlation between the function corresponding to the program target and the second API category (communication data class) is calculated to be 1. Based on this, the API correlation value between the function corresponding to the program target and the second API category (communication data class) is calculated to be 1.

[0122] The function corresponding to the target of the business program to be assembled is consistent with the alarm information transmission method in the navigation anomaly warning category of the early warning data category. Its transmission consistency is 1. The correlation between the function corresponding to the program target and the second API category (navigation anomaly warning category) is calculated to be 1. Based on this, the API correlation value between the function corresponding to the program target and the second API category (navigation anomaly warning category) is calculated to be 1.

[0123] Similarly, calculate the degree of association and association value between the function corresponding to the target of the business program to be assembled and other second API categories.

[0124] Based on the ship usage scenario and the external environment, a correlation threshold of M=0.9 is preset. Communication data classes and navigation anomaly warning classes with API correlation values ​​greater than 0.9 are selected as the associated API categories for the services to be assembled.

[0125] In a preferred embodiment, step S04 involves constructing a rule engine for the business to be assembled based on the correlation between rule engines corresponding to different associated API categories and / or the conflict between rule engines corresponding to different associated API categories. The flowchart is as follows: Figure 5 As shown, the steps include:

[0126] Step S041: Calculate the rule engine correlation index based on the correlation between rule engines corresponding to different associated API categories;

[0127] Step S042: Calculate the rule engine conflict index based on the conflict between rule engines corresponding to different associated API categories;

[0128] Step S043: Construct the rule engine for the business to be assembled based on the rule engine correlation index and / or rule engine conflict index and the preset fusion rules.

[0129] In this embodiment, the calculation of the rule engine correlation index based on the correlation between rule engines corresponding to different associated API categories is based on the positive correlation between the consistency of trigger rules and / or similarity of data execution rules and / or relevance of notification rules and / or correlation of task rules and / or availability of verification rules and the rule engine correlation index.

[0130] The calculation of the rule engine conflict index based on the conflict between rule engines corresponding to different associated API categories is based on the positive correlation between the conflict of trigger rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules of the rule engine corresponding to the API category and the rule engine conflict index.

[0131] In a preferred embodiment, the step of constructing the rule engine for the business to be assembled based on the rule engine correlation index and / or rule engine conflict index and the preset fusion rules is as follows: First, a correlation index threshold is preset based on the ship type and / or ship usage scenario and / or external environment. Then, rule engines whose rule engine correlation index values ​​are greater than the preset correlation index thresholds in the associated API categories of all businesses to be assembled are fused to obtain the rule engine for the business to be assembled.

[0132] In another preferred embodiment, the step of constructing the rule engine for the business to be assembled based on the rule engine correlation index and / or rule engine conflict index and preset fusion rules is as follows: First, a conflict index threshold is preset based on the ship type and / or ship usage scenario and / or external environment. Then, rules in the associated API categories of all businesses to be assembled whose rule engine conflict index is greater than the preset conflict index threshold are sorted according to their correlation value with the API and then selected and fused to obtain the rule engine for the business to be assembled.

[0133] In another preferred embodiment, the step of constructing the rule engine for the business to be assembled based on the rule engine correlation index and / or rule engine conflict index and the preset fusion rules is as follows: rule engines whose rule engine correlation index values ​​are greater than the preset correlation index threshold in the associated API categories of all businesses to be assembled are fused, and rules whose rule engine conflict index values ​​are greater than the preset conflict index threshold are deleted, to obtain the final rule engine for the business to be assembled.

[0134] In a preferred embodiment, step S05 involves constructing the workflow for the business to be assembled based on the rule engine and the target of the business to be assembled, as shown in the flowchart below. Figure 6 As shown, the steps include:

[0135] Step S051: Construct a basic business workflow based on the triggering rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules in the business rule engine to be assembled;

[0136] Step S052: Adjust the basic business workflow according to the goals and requirements of the business to be assembled to obtain the program workflow of the business to be assembled.

[0137] In this embodiment, a basic business workflow is constructed based on the triggering rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules in the business rule engine to be assembled. The basic business workflow is then adjusted to ensure that the business objectives are met, thereby obtaining the program workflow of the business to be assembled.

[0138] According to another embodiment of the present invention, an intelligent ship business dynamic assembly system is provided, the system architecture diagram of which is shown below. Figure 7 As shown, it includes:

[0139] The application programming interface (API) classification module is used to classify ship data according to the type of ship data and / or the relevance and / or the accuracy of ship data.

[0140] The API and rule engine association module is used to identify the rule engine for each API category based on the corresponding ship business needs.

[0141] The API association identification module is used to identify the associated API categories of the business to be assembled based on the target of the business to be assembled.

[0142] The rule engine building module is used to build the rule engine for the business to be assembled based on the correlation between the rule engines corresponding to different related API categories and / or the conflict between the rule engines corresponding to different related API categories.

[0143] The business dynamic assembly module is used to construct the program workflow of the business to be assembled based on the rule engine and the goal of the business to be assembled, and encapsulate it to obtain a new business, that is, to realize business dynamic assembly.

[0144] According to another embodiment of the present invention, a computer-readable storage medium is provided that stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the above-described dynamic business assembly method.

[0145] According to another embodiment of the present invention, an electronic device is provided, the structural schematic diagram of which is shown below. Figure 8 As shown, it includes:

[0146] At least one processor;

[0147] and a memory communicatively connected to the at least one processor;

[0148] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the above-described dynamic business assembly method.

[0149] Of course, those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Any changes or modifications to the above embodiments that are within the scope of the present invention will fall within the protection scope of the present invention.

Claims

1. A method for dynamic assembly of intelligent ship business services, characterized by, The application comprises the following steps: API classification of the ship data according to the type of the ship data and / or the relevance of the ship data and / or the accuracy of the ship data; The API classification comprises a first API category and a second API category; the first API category comprises any one or more combinations of a basic data category, a channel data category, a pre-warning data category, a verification data category, and a comprehensive analysis category; The second API category is a subdivision type divided in the first API category; The API classification of the ship data according to the type of the ship data and / or the relevance of the ship data and / or the accuracy of the ship data comprises the following steps: calculating a data type relevance indication value according to the type of the sensor corresponding to the ship data and / or the data format and / or the source of the data; calculating a data relevance indication value according to the time continuity of the ship data and / or the change relevance of the data; calculating a data accuracy indication value according to the real-time nature of the ship data and / or the precision of the ship data; and calculating a data-in-category indication value according to the data type relevance indication value and / or the data relevance indication value and / or the data accuracy indication value; API grouping of each data according to the data-in-category indication value of each ship data and the indication value range corresponding to the preset API classification; Identifying the rule engine of each API category according to the ship business demand corresponding to each API category; Identifying the associated API categories of the to-be-assembled business according to the target of the to-be-assembled business; The step of identifying the associated API categories of the to-be-assembled business according to the target of the to-be-assembled business comprises the following steps: identifying the first API category to which the to-be-assembled business belongs; calculating an API relevance value according to the degree of association between the function corresponding to the program target and the second API category; and selecting one or more API categories with an API relevance value greater than a preset threshold as the associated API categories of the to-be-assembled business; Constructing the rule engine of the to-be-assembled business according to the relevance of the rule engines corresponding to different associated API categories and / or the conflictiveness of the rule engines corresponding to different associated API categories; the step of constructing the rule engine of the to-be-assembled business according to the relevance of the rule engines corresponding to different associated API categories and / or the conflictiveness of the rule engines corresponding to different associated API categories comprises the following steps: calculating a rule engine relevance index according to the relevance between the rule engines corresponding to different associated API categories; calculating a rule engine conflictiveness index according to the conflictiveness between the rule engines corresponding to different associated API categories; and constructing the rule engine of the to-be-assembled business according to the rule engine relevance index and / or the rule engine conflictiveness index and a preset fusion rule; Constructing the program workflow of the to-be-assembled business according to the rule engine of the to-be-assembled business and the target of the to-be-assembled business, and encapsulating the new business obtained in this way, so as to realize dynamic assembly of the business.

2. The intelligent ship business dynamic assembly method according to claim 1, characterized by, The basic data category includes any one or more sub-categories of the navigation data category, the communication data category, the channel data category includes any one or more sub-categories of the climate environment category, the regional data category, the early warning data category includes any one or more sub-categories of the anti-collision early warning category, the pirate early warning category, the navigation anomaly early warning category, the personnel anomaly early warning category, and the comprehensive analysis category includes any one or more sub-categories of the navigation anomaly analysis category, the personnel anomaly analysis category, the navigation route planning category, and the energy saving analysis category; each second API category includes one or more data type sets.

3. The intelligent ship business dynamic assembly method according to claim 1, characterized by, The rule engine of each API category is identified according to the ship business demand corresponding to each API category, and includes the following steps: Trigger rules are constructed according to the input data of the ship business demand corresponding to each API category; Data execution rules are constructed according to the data usage of the ship business demand corresponding to each API category; Notification rules are constructed according to the notification requirement of the ship business demand corresponding to each API category; Task rules are constructed according to the time delay requirement of the ship business demand corresponding to each API category; Verification rules are constructed according to the accuracy requirement of the ship business demand corresponding to each API category; The rule engine of each API category is identified according to the trigger rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules.

4. The intelligent ship business dynamic assembly method according to claim 1, characterized by, The program workflow of the to-be-assembled business is constructed according to the rule engine of the to-be-assembled business and the target of the to-be-assembled business, and includes the following steps: A basic business workflow is constructed according to the trigger rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules in the rule engine of the to-be-assembled business; The program workflow of the to-be-assembled business is obtained by adjusting the basic business workflow according to the target and requirement of the to-be-assembled business.

5. An intelligent ship business dynamic assembly system characterized by, It includes: An application program interface classification module is used to classify ship data according to the type of ship data and / or the relevance of ship data and / or the accuracy of ship data; the API classification includes a first API category and a second API category; the first API category includes any one or more combinations of the basic data category, the channel data category, the early warning data category, the verification data category, and the comprehensive analysis category; The second API category is a subdivided type in the first API category; The ship data is classified according to the type of ship data and / or the relevance of ship data and / or the accuracy of ship data, including the following steps: calculating a data type relevance indication value according to the sensor type and / or data format and / or data source corresponding to the ship data; calculating a data relevance indication value according to the time continuity of the ship data and / or the change relevance of the data; calculating a data accuracy indication value according to the real-time nature of the ship data and / or the accuracy of the ship data; calculating a data intra-category indication value according to the data type relevance indication value and / or the data relevance indication value and / or the data accuracy indication value; The data is API grouped according to the data intra-category indication value of each ship data and the indication value range corresponding to the preset API classification. An API and rule engine association module is configured to identify a rule engine for each API category according to a ship business requirement corresponding to each API category; An API association identification module is configured to identify an associated API category of the to-be-assembled business according to a target of the to-be-assembled business; The API association identification module is configured to identify a first API category to which the to-be-assembled business belongs, calculate an API association value according to a degree of association between a function corresponding to the program target and a second API category, and select one or more API categories with an API association value greater than a preset threshold as the associated API category of the to-be-assembled business; A rule engine construction module is configured to construct a rule engine for the to-be-assembled business according to an association of rule engines corresponding to different associated API categories and / or a conflict of rule engines corresponding to different associated API categories. The rule engine construction module is configured to calculate a rule engine association index according to the association between the rule engines corresponding to the different associated API categories, calculate a rule engine conflict index according to the conflict between the rule engines corresponding to the different associated API categories, and construct the rule engine for the to-be-assembled business according to the rule engine association index and / or the rule engine conflict index and a preset fusion rule. A business dynamic assembly module is configured to construct a program workflow of the to-be-assembled business according to the rule engine of the to-be-assembled business and a target of the to-be-assembled business, and encapsulate the program workflow to obtain a new business, thereby achieving business dynamic assembly.

6. A computer readable storage medium storing a computer program for electronic data interchange, wherein, The computer program enables a computer to execute the method of any one of claims 1-4.

7. An electronic device, comprising: The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. 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The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-4. The computer program enables a computer to execute the method of any one of claims 1-

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