Intelligent ship service dynamic assembly method and system, storage medium and electronic equipment

Abstract

The invention discloses an intelligent ship business dynamic assembly method and system, a storage medium and electronic equipment. The method comprises the steps of performing API classification on 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; identifying a rule engine of each API category according to the ship business requirement corresponding to each API category; identifying an associated API category of the to-be-assembled service according to the target of the to-be-assembled service; constructing a rule engine of the to-be-assembled business according to the relevance of the rule engines corresponding to the different associated API categories and / or the conflict of the rule engines corresponding to the different associated API categories; and according to the rule engine of the to-be-assembled service and the target of the to-be-assembled service, constructing a program workflow of the to-be-assembled service, and packaging the program workflow to obtain a new service. According to the invention, the problems of poor ship service flexibility and high service development difficulty caused by poor ship service expansibility, long development period and lack of ship industry pertinence in the existing intelligent ship technology are solved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent ship technology, and in particular relates to intelligent ship business dynamic assembly methods, systems, storage media and electronic devices. Background Technology

[0002] With the digital transformation of the shipping industry, customer needs are becoming increasingly diversified, such as real-time ship status monitoring, intelligent scheduling, energy efficiency optimization, and supply chain collaboration. Traditional systems struggle to quickly adapt to new business scenarios. Therefore, ship management systems that can support multiple business functions are being adopted.

[0003] The current ship business management system has the following obvious defects: (1) It usually only supports basic functions (such as route management and cargo tracking). New business modules need to be redeveloped, which is time-consuming and costly, and cannot meet the rapidly changing needs of customers; (2) Ship operation data (such as AIS data, fuel consumption data, and equipment status data) are stored in a scattered manner, lacking a unified data warehouse and standardized interface (API), making it difficult to share and combine applications across businesses; (3) Business logic is usually implemented in a hard-coded manner, and code needs to be modified when rules are changed. There is a lack of flexible rule engine to support dynamic adjustment, resulting in slow system response speed and high maintenance costs; (4) The correlation between different business scenarios (such as ship early warning and port collaboration) has not been abstracted, and each development needs to start from scratch, which is inefficient.

[0004] Some shipbuilding companies have attempted to address these issues through customized development or the introduction of general-purpose low-code platforms. While these solutions can meet specific needs, they suffer from low code reusability, requiring repeated development for subsequent expansions. Furthermore, they lack industry-specific characteristics and cannot directly adapt to ship data features (such as spatiotemporal correlation and equipment heterogeneity), and the rule engines have poor compatibility with business templates.

[0005] To address the issues of poor scalability, long development cycles, and lack of industry-specific focus in shipbuilding business, which result in low business flexibility and high development difficulty, a method, system, storage medium, and electronic equipment for the dynamic assembly of intelligent shipbuilding business are proposed. Summary of the Invention

[0006] This invention proposes a method, system, storage medium, and electronic device for dynamic assembly of intelligent ship business, in order to at least solve the problems of poor scalability of ship business, long development cycle, lack of ship industry specificity, resulting in poor flexibility of ship business and high difficulty in business development in existing intelligent ship technologies.

[0007] According to an embodiment of the present invention, a method for dynamic assembly of intelligent ship operations is provided, comprising:

[0008] Ship data is classified into APIs based on the type of ship data and / or the relevance and / or accuracy of the ship data.

[0009] The rule engine identifies each API category based on the corresponding ship business requirements;

[0010] Identify the associated API categories of the business to be assembled based on its objectives;

[0011] 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.

[0012] Based on the rule engine and the goal of the business to be assembled, the program workflow of the business to be assembled is constructed and encapsulated to obtain the new business, that is, to realize the dynamic assembly of business.

[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 basic data, channel data, early warning data, verification data, and comprehensive analysis. The second API category is a sub-category of the first API category. The basic data category includes any one or more subcategories of navigation data and communication data. The channel data category includes any one or more subcategories of climate and environment and regional data. The early warning data category includes any one or more subcategories of collision avoidance warning, piracy warning, navigation anomaly warning, and personnel anomaly warning. The comprehensive analysis category includes any one or more subcategories of navigation anomaly analysis, personnel anomaly analysis, navigation route planning, and energy saving analysis. Each second API category contains one or more sets of data types.

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

[0015] Calculate the data type correlation indicator value based on the sensor type and / or data format and / or data source corresponding to the ship data;

[0016] Calculate the data correlation indicator value based on the temporal continuity of ship data and / or the correlation of data changes;

[0017] Calculate the data accuracy indication value based on the real-time nature and / or accuracy of the ship data;

[0018] Calculate the in-class indicator value based on the data type relevance indicator value and / or data association indicator value and / or data accuracy indicator value;

[0019] The data is grouped by API based on the data category indication value of each ship and the indication value range corresponding to the preset API category.

[0020] In a preferred embodiment, the rule engine for identifying each API category based on the corresponding ship business needs includes the following steps:

[0021] Trigger rules are constructed based on the input data of the ship business requirements corresponding to each API category;

[0022] Data execution rules are constructed based on the data usage methods corresponding to the ship business needs of each API category;

[0023] Develop notification rules based on the informative requirements of the ship business needs corresponding to each API category;

[0024] Task rules are constructed based on the latency requirements of the ship business needs corresponding to each API category;

[0025] Validation rules are constructed based on the accuracy requirements of the ship business needs corresponding to each API category;

[0026] The rule engine identifies each API category based on triggering rules and / or data execution rules and / or notification rules and / or task rules and / or validation rules.

[0027] In a preferred embodiment, identifying the associated API category of the service to be assembled based on its target includes:

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

[0029] The API relevance value is calculated based on the degree of relevance between the function corresponding to the program objective and the second API category;

[0030] Select one or more API categories whose API relevance values ​​are greater than a preset threshold as the associated API categories for the business to be assembled.

[0031] In a preferred embodiment, the step of constructing the 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 includes the following steps:

[0032] Calculate the rule engine correlation index based on the correlation between rule engines corresponding to different associated API categories;

[0033] Calculate the rule engine conflict index based on the conflict between rule engines corresponding to different associated API categories;

[0034] The rule engine for the business to be assembled is constructed based on the rule engine's correlation indicators and / or conflict indicators and preset fusion rules.

[0035] In a preferred embodiment, the step of constructing the workflow for assembling the business based on the rule engine and the target of the business to be assembled includes the following steps:

[0036] 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;

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

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

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

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

[0046] At least one processor;

[0047] and a memory communicatively connected to the at least one processor;

[0048] 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.

[0049] The advantages of the intelligent ship business dynamic assembly method, system, storage medium, and electronic device of the present invention are:

[0050] (1) Dividing APIs into first and second categories and classifying ship data according to the type of ship data and / or the correlation and / or the accuracy of ship data, compared with the traditional technical solution for ship business management, can effectively classify massive and messy ship data according to different dimensions. This not only facilitates the rapid identification of the API category corresponding to the ship business and improves the efficiency of subsequent business assembly, but also effectively establishes the correlation between different ship businesses, breaks down the barriers between ship businesses, and realizes the sharing of ship businesses.

[0051] (2) The rule engine for each API category is identified based on the triggering rules and / or data execution rules and / or notification rules and / or task rules and / or verification rules corresponding to the ship business requirements of each API category. Compared with the traditional technical solution for ship business management, it can effectively identify the rules that match each API category, thereby effectively improving the speed of subsequent API calls and new ship business assembly and reducing the ship business development cycle.

[0052] (3) Identify the associated API categories of the business to be assembled based on the target of the business to be assembled, and construct the rule engine of the business to be assembled based on the association and conflict of the rule engines corresponding to different associated API categories. Compared with the traditional business assembly technology solution, it can not only effectively improve the speed of ship business generation, thereby reducing the development cycle and development difficulty of ship business, but also improve the scalability and flexibility of ship business through the association and conflict fusion of different rules, which is convenient for subsequent ship business changes or business upgrades. Attached Figure Description

[0053] Figure 1 This is a flowchart of the intelligent ship business dynamic assembly method according to an embodiment of the present invention;

[0054] Figure 2 This is a flowchart of step S01 in an embodiment of the present invention;

[0055] Figure 3 This is a flowchart of step S02 in an embodiment of the present invention;

[0056] Figure 4 This is a flowchart of step S03 in an embodiment of the present invention;

[0057] Figure 5This is a flowchart of step S04 in an embodiment of the present invention;

[0058] Figure 6 This is a flowchart of step S05 in an embodiment of the present invention;

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

[0060] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

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

[0062] According to an embodiment of the present invention, a method for dynamic assembly of intelligent ship operations is provided, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0063] Step S01: Classify the ship data according to the type of ship data and / or the relevance and / or the accuracy of the ship data using API.

[0064] Step S02: Identify the rule engine for each API category based on the corresponding ship business requirements;

[0065] Step S03: Identify the associated API categories of the business to be assembled based on the target of the business to be assembled;

[0066] Step S04: Construct 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.

[0067] Step S05: Based on the rule engine of the business to be assembled and the goal of the business to be assembled, construct the program workflow of the business to be assembled and encapsulate it to obtain the new business, that is, realize the dynamic assembly of business.

[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 basic data, channel data, early warning data, verification data, and comprehensive analysis. The second API category is a sub-category within the first API category. The basic data category includes any one or more subcategories of navigation data and communication data; the channel data category includes any one or more subcategories of climate and environment and regional data; the early warning data category includes any one or more subcategories of collision avoidance warning, piracy warning, navigation anomaly warning, and personnel anomaly warning; and the comprehensive analysis category includes any one or more subcategories of navigation anomaly analysis, personnel anomaly analysis, navigation route planning, and energy-saving analysis. Each second API category contains one or more sets of data types. In this embodiment, API classification is performed based on different vessel usage scenarios and vessel configurations. The first API category includes, but is not limited to, basic data, channel data, early warning data, verification data, and comprehensive analysis.

[0069] The second API category is a subcategory within the first API category. The first API category includes: Basic Data (including but not limited to navigation data, communication data, etc.); Channel Data (including but not limited to climate and environment data, regional data, etc.); Early Warning Data (including but not limited to collision avoidance warnings, piracy warnings, navigation anomaly warnings, personnel anomaly warnings, etc.); and Comprehensive Analysis Data (including but not limited to navigation anomaly analysis, personnel anomaly analysis, route planning, energy conservation analysis, etc.).

[0070] Each second API category contains one or more sets of data types. For example, the navigation data category includes sets of data such as position data, speed data, and heading data; the communication data category includes sets of data such as communication link data, communication duration, signal strength, and packet loss rate; the climate and environment category includes sets of data such as wind force, air pressure, temperature, and humidity; the regional data category includes sets of data such as regional pirate information, regional traffic restriction data, and regional resupply data; the collision avoidance warning category includes sets of data such as obstacle data, collision prediction data, and collision alarm data; the pirate warning category includes sets of data such as pirate distribution data, pirate prediction data, and pirate alarm data; and the navigation anomaly warning category includes sets of data such as navigation mutation data. The datasets include: navigation anomaly prediction data, navigation anomaly alarm data, etc.; personnel anomaly early warning data includes personnel behavior analysis data, personnel anomaly prediction data, personnel anomaly alarm data, etc.; navigation anomaly analysis data includes navigation anomaly cause analysis, navigation anomaly impact analysis, navigation anomaly correction data, etc.; personnel anomaly analysis data includes personnel anomaly cause analysis, personnel anomaly impact analysis, personnel anomaly correction data, etc.; navigation route planning data includes origin and destination data, navigation route planning data, navigation route optimization data, etc.; and energy saving analysis data includes fuel consumption data, navigation route and energy saving optimization data, fuel replenishment data, etc.

[0071] In a preferred embodiment, step S01 involves classifying the ship data according to its type and / or its relevance and / or its accuracy using API classification, as shown in the flowchart below. Figure 2 As shown, the steps include:

[0072] Step S011: Calculate the data type correlation indicator value based on the sensor type and / or data format and / or data source corresponding to the ship data;

[0073] Step S012: Calculate the data correlation indicator value based on the temporal continuity of the ship data and / or the correlation of data changes;

[0074] Step S013: Calculate the data accuracy indication value based on the real-time nature and / or accuracy of the ship data;

[0075] Step S014: Calculate the data class indicative value based on the data type relevance indicator value and / or data correlation indicator value and / or data accuracy indicator value;

[0076] Step S015: Group the data into API groups based on the data class indication values ​​of each ship data and the indication value range corresponding to the preset API classification.

[0077] In this embodiment, the step of calculating the data type correlation indicator value based on the sensor type and / or data format and / or data source corresponding to the ship data includes: calculating the data type correlation indicator value based on the positive correlation between the consistency of the sensor type corresponding to the ship data and the data type correlation indicator value; calculating the data type correlation indicator value based on the positive correlation between the consistency of the ship data format and the data type correlation indicator value; calculating the data type correlation indicator value based on the positive correlation between the consistency of the ship data source (source consistency is calculated based on the consistency of the data acquisition location and / or the consistency of the sensor device corresponding to the data acquisition) and the data type correlation indicator value; calculating the data type correlation indicator value based on the positive correlation between the consistency of the sensor type and the consistency of the ship data format and the data type correlation indicator value; calculating the data type correlation indicator value based on the positive correlation between the consistency of the sensor type and the consistency of the ship data source and the data type correlation indicator value; calculating the data type correlation indicator value based on the positive correlation between the consistency of the ship data format and the consistency of the ship data source and the data type correlation indicator value; and calculating the data type correlation indicator value based on the positive correlation between the consistency of the sensor type, the consistency of the ship data format, and the consistency of the ship data source and the data type correlation indicator value. The data type correlation indicator value is represented by the variable x.

[0078] The calculation of the data correlation indicator value based on the temporal continuity and / or the correlation of changes in ship data includes: calculating the data correlation indicator value based on the positive correlation between the temporal continuity of ship data (the temporal continuity is calculated based on the consistency of time or the interval between time periods; the shorter the time interval, the greater the temporal continuity) and the data correlation indicator value; calculating the data correlation indicator value based on the positive correlation between the temporal continuity of ship data, the temporal correlation of changes in data (the correlation of changes in data is calculated based on the related changes brought about by a unit change in data; the greater the related changes brought about by a unit time, the greater the correlation of changes in data); and calculating the data correlation indicator value based on the positive correlation between the temporal continuity of ship data, the correlation of changes in data, and the data correlation indicator value. The data correlation indicator value is represented by the 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 calculated 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 trigger 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 dynamically assembling intelligent ship operations, characterized in that, include: Ship data is classified into APIs based on the type of ship data and / or the relevance and / or accuracy of the ship data. The rule engine identifies each API category based on the corresponding ship business requirements; Identify the associated API categories of the business to be assembled based on its objectives; 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. Based on the rule engine and the goal of the business to be assembled, the program workflow of the business to be assembled is constructed and encapsulated to obtain the new business, that is, to realize the dynamic assembly of business.

2. The intelligent ship business dynamic assembly method according to claim 1, characterized in that, The API classification includes a first API category and a second API category. The first API category includes any one or more of the following: basic data, channel data, early warning data, verification data, and comprehensive analysis. The second API category is a subcategory within the first API category. The basic data category includes one or more subcategories of navigation data and communication data; the channel data category includes one or more subcategories of climate and environment data and regional data; the early warning data category includes one or more subcategories of collision avoidance warning, piracy warning, navigation anomaly warning, and personnel anomaly warning; and the comprehensive analysis category includes one or more subcategories of navigation anomaly analysis, personnel anomaly analysis, navigation route planning, and energy conservation analysis. Each second API category contains one or more sets of data types.

3. The intelligent ship business dynamic assembly method according to claim 2, characterized in that, The API classification of ship data based on the type of ship data and / or the relevance and / or accuracy of ship data includes the following steps: Calculate the data type correlation indicator value based on the sensor type and / or data format and / or data source corresponding to the ship data; Calculate the data correlation indicator value based on the temporal continuity of ship data and / or the correlation of data changes; Calculate the data accuracy indication value based on the real-time nature and / or accuracy of the ship data; Calculate the in-class indicator value based on the data type relevance indicator value and / or data association indicator value and / or data accuracy indicator value; The data is grouped by API based on the data category indication value of each ship and the indication value range corresponding to the preset API category.

4. The intelligent ship business dynamic assembly method according to claim 1, characterized in that, The rule engine that identifies each API category based on the corresponding ship business needs includes the following steps: Trigger rules are constructed based on the input data of the ship business requirements corresponding to each API category; Data execution rules are constructed based on the data usage methods corresponding to the ship business needs of each API category; Develop notification rules based on the informative requirements of the ship business needs corresponding to each API category; Task rules are constructed based on the latency requirements of the ship business needs corresponding to each API category; Validation rules are constructed based on the accuracy requirements of the ship business needs corresponding to each API category; The rule engine identifies each API category based on triggering rules and / or data execution rules and / or notification rules and / or task rules and / or validation rules.

5. The intelligent ship business dynamic assembly method according to claim 1, characterized in that, The step of identifying the associated API category of the business to be assembled based on the target of the business to be assembled includes: Identify the first API category to which the business to be assembled belongs; The API relevance value is calculated based on the degree of relevance between the function corresponding to the program objective and the second API category; Select one or more API categories whose API relevance values ​​are greater than a preset threshold as the associated API categories for the business to be assembled.

6. The intelligent ship business dynamic assembly method according to claim 5, characterized in that, The step of constructing the 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 includes the following steps: Calculate the rule engine correlation index based on the correlation between rule engines corresponding to different associated API categories; Calculate the rule engine conflict index based on the conflict between rule engines corresponding to different associated API categories; The rule engine for the business to be assembled is constructed based on the rule engine's correlation indicators and / or conflict indicators and preset fusion rules.

7. The intelligent ship business dynamic assembly method according to claim 1, characterized in that, The process workflow for constructing the business to be assembled based on the rule engine and the target of the business to be assembled includes the following steps: 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; The basic business workflow is adjusted according to the goals and requirements of the business to be assembled to obtain the program workflow of the business to be assembled.

8. A smart ship business dynamic assembly system, characterized in that, include: 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. 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. 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. 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. 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.

9. A computer-readable storage medium storing a computer program for electronic data interchange, wherein, The computer program causes the computer to perform the method as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the business dynamic assembly method as described in any one of claims 1-7.

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