Yangtze River fishing-out fisherman re-employment investigation method based on Internet of Things big data

Abstract

The invention discloses a Yangtze River fishing-out fisherman re-employment investigation method based on Internet of Things big data, and belongs to the technical field of Internet of Things technology and big data processing. Real-time and automatic collection of data is realized through the Internet of Things technology, the limitation of a traditional survey method is overcome, the data collection efficiency and accuracy are greatly improved, the survey cost is reduced, the actual situation of re-employment of fishermen who quit fishing can be more accurately grasped through the big data processing technology and fusion and analysis of multi-source data, and the method is suitable for popularization and application of the fishermen who quit fishing and the fishermen who quit fishing and the fishermen who quit fishing and the fishermen who quit fishing. According to the method, rapid updating and dynamic feedback of survey results are achieved, data analysis is more deep and comprehensive, powerful support is provided for government policy making and assisting measures, accurate survey, a dynamic monitoring mechanism and continuous optimization of assisting strategies are achieved, and the method has great significance in improving the re-employment success rate and life quality of fishing-out fishermen. And the method has important value in promoting long-term and stable development of re-employment work of fishing-quitting fishermen.

Description

Technical Field

[0001] The present invention belongs to the field of Internet of Things technology and big data processing technology, and specifically provides a reemployment survey method for retired fishermen in the Yangtze River based on Internet of Things big data. Background Art

[0002] With the in-depth implementation of the ten-year fishing ban policy in the Yangtze River, a large number of retired fishermen are facing the challenges of career transformation and re-employment. Traditional survey methods often have problems such as incomplete data collection, delayed analysis, and low efficiency, which are difficult to meet the needs of policy formulation and employment services. For this reason, the present invention proposes a survey method based on Internet of Things big data. Summary of the invention

[0003] The purpose of the present invention is to provide a re-employment survey method for retired fishermen in the Yangtze River based on Internet of Things big data to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data, comprising the following steps:

[0005] S1. Establish a real-name dynamic database of retired fishermen, and use Internet of Things technology to collect basic information, re-employment intentions, skill levels and other data of retired fishermen;

[0006] S2, transmitting the collected data to the data center for storage via the wireless communication network;

[0007] S3. Use big data analysis technology to process and analyze data;

[0008] S4. Develop a questionnaire survey based on the data analysis results and then put forward targeted policy recommendations;

[0009] S5. Establish a long-term monitoring mechanism to continuously track the employment and living conditions of retired fishermen and ensure the flexibility and adjustability of policies;

[0010] As a further preferred embodiment of the present technical solution: the real-name dynamic database of retired fishermen in step S1 includes fishermen's identity verification information, fishermen's historical fishing experience and skills, fishermen's willingness and expectations for re-employment, and collects data such as basic information, re-employment intention, and skill level of retired fishermen based on the dynamic database;

[0011] As a further preferred embodiment of the present technical solution: a data transmission network and data storage and management are established in S2, wherein the data transmission network uses wireless communication technology to transmit the data collected by the IoT devices to a data center or a cloud storage platform in real time, and the data storage and management adopts a distributed storage architecture and a database management system to store and manage massive amounts of data efficiently, securely and orderly;

[0012] As a further preferred embodiment of the present technical solution: the big data analysis technology in step S3 includes data cleaning and preprocessing technology, cluster analysis and association rule mining technology, and prediction model construction technology. The data cleaning and preprocessing technology is used to improve data quality, the cluster analysis and association rule mining technology is used to identify the common characteristics and employment preferences of fishermen, and the prediction model construction technology can predict the re-employment potential and possible difficulties of fishermen;

[0013] As a further preferred embodiment of the present technical solution: the questionnaire survey in S4 is intended to understand the cultural level and understanding ability of fishermen, the employment market demand in the areas where fishermen are located, and the re-employment assistance policies and resources provided by the government and all sectors of society, and then put forward targeted policy recommendations;

[0014] As a further preferred embodiment of the present technical solution: the monitoring mechanism in S5 is based on the situation of fishermen's participation in re-employment training and the evaluation of the training effect, the progress of fishermen's entrepreneurial projects and the operating benefits, and the degree of fishermen's adaptation to and satisfaction with the new positions, and then builds a re-employment service platform, integrates employment information, training resources, and entrepreneurship support policies, etc., and realizes the rapid transmission and accurate push of information through the Internet of Things technology, provides one-stop re-employment services for retired fishermen, provides suggestions for optimizing the re-employment policy for retired fishermen to relevant government departments, and cooperates with government departments to promote the implementation of policy suggestions, and regularly evaluates the policy effects for timely adjustment and optimization;

[0015] As a further preferred embodiment of this technical solution: the industries of the re-employment intention survey are based on three major industries:

[0016] Among them, the primary industry: 57%, the survey of re-employed personnel tends to be more inclined towards the primary industry: planting (28%), forestry (14%), animal husbandry (18%);

[0017] Secondary industry: 25%. The survey of re-employed workers tends to be more inclined towards the following secondary industries: agricultural and sideline food processing industry (7%), furniture manufacturing industry (11%), and textile industry (8%);

[0018] The tertiary industry: 18%. The survey of re-employed people tends to be more inclined towards the tertiary industry: tourism (4%), retail (5%), and catering (8%);

[0019] As a further preferred embodiment of the present technical solution: the skill level is first investigated based on academic qualifications, which include high school and below and college and above.

[0020] Among them, those with high school education or below: 55%, those with high school education or below tend to be employed in welding (10%), maintenance (20%), and transportation (10%).

[0021] College graduates and above: 45%. Re-employment of college graduates and above tends to be in education (25%), after-sales service (10%), and further study (5%).

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes real-time and automatic data collection through the Internet of Things technology, overcomes the limitations of traditional survey methods, greatly improves data collection efficiency and accuracy, and reduces survey costs. Through big data processing technology, the fusion and analysis of multi-source data can more accurately grasp the actual situation of reemployment of retired fishermen, realize rapid updating and dynamic feedback of survey results, make data analysis more in-depth and comprehensive, and provide strong support for government policy formulation and assistance measures. The present invention realizes accurate investigation, uses a dynamic monitoring mechanism, and continuously optimizes the assistance strategy, which is of great value to improving the reemployment success rate and quality of life of retired fishermen and promoting the long-term and stable development of reemployment of retired fishermen. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a framework diagram of the re-employment survey method for retired fishermen in the Yangtze River based on the big data of the Internet of Things of the present invention;

[0024] Figure 2 It is a survey chart of re-employment intention of the re-employment survey method of retired fishermen in the Yangtze River based on the big data of the Internet of Things of the present invention;

[0025] Figure 3 This is a skill level chart of the re-employment survey method for retired fishermen in the Yangtze River based on the big data of the Internet of Things. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Example

[0028] See also Figure 1 As shown, the present invention provides a technical solution: a re-employment survey method for retired fishermen in the Yangtze River based on the big data of the Internet of Things, comprising the following steps:

[0029] The re-employment survey method for retired fishermen in the Yangtze River based on the big data of the Internet of Things is characterized by comprising the following steps:

[0030] S1. Establish a real-name dynamic database of retired fishermen, and use Internet of Things technology to collect basic information, re-employment intentions, skill levels and other data of retired fishermen;

[0031] S2, transmitting the collected data to the data center for storage via the wireless communication network;

[0032] S3. Use big data analysis technology to process and analyze data;

[0033] S4. Based on the data analysis results, formulate a questionnaire survey and then put forward targeted policy recommendations;

[0034] S5. Establish a long-term monitoring mechanism to continuously track the employment and living conditions of retired fishermen and ensure the flexibility and adjustability of policies;

[0035] In this embodiment, specifically: the real-name dynamic database of retired fishermen in step S1 includes identity authentication information of citizens, historical fishing experience and skills of fishermen, and willingness and expectation of fishermen for re-employment, and collects basic information, re-employment intention, skill level and other data of retired fishermen according to the dynamic database;

[0036] In this embodiment, specifically: a data transmission network and data storage and management are established in S2. The data transmission network uses wireless communication technology to transmit the data collected by the IoT devices to the data center or cloud storage platform in real time. The data storage and management adopts a distributed storage architecture and a database management system to store and manage massive data efficiently, securely and orderly.

[0037] In this embodiment, specifically: the big data analysis technology in step S3 includes data cleaning and preprocessing technology, cluster analysis and association rule mining technology, and prediction model construction technology. The data cleaning and preprocessing technology is used to improve data quality, the cluster analysis and association rule mining technology is used to identify the common characteristics and employment preferences of fishermen, and the prediction model construction technology can predict the re-employment potential and possible difficulties of fishermen;

[0038] In this embodiment, specifically: the questionnaire survey in S4 is intended to understand the cultural level and understanding ability of fishermen, the employment market demand in the fishermen's area, and the re-employment assistance policies and resources provided by the government and all sectors of society, and then put forward targeted policy recommendations;

[0039] In this embodiment, specifically: the monitoring mechanism in S5 builds a re-employment service platform based on the fishermen's participation in re-employment training and training effect evaluation, the progress of fishermen's entrepreneurial projects and operating benefits, and the fishermen's adaptability to new positions and satisfaction surveys, integrates employment information, training resources, entrepreneurship support policies, etc., and realizes rapid transmission and accurate push of information through the Internet of Things technology, provides one-stop re-employment services for retired fishermen, provides suggestions for optimizing re-employment policies for retired fishermen to relevant government departments, and cooperates with government departments to promote the implementation of policy suggestions, and regularly evaluates policy effects for timely adjustment and optimization;

[0040] In this embodiment, specifically: the industries for the re-employment intention survey are based on three major industries:

[0041] Among them, the primary industry: 57%, the survey of re-employed personnel tends to be more inclined towards the primary industry: planting (28%), forestry (14%), animal husbandry (18%);

[0042] Secondary industry: 25%. The survey of re-employed workers tends to be more inclined towards the following secondary industries: agricultural and sideline food processing industry (7%), furniture manufacturing industry (11%), and textile industry (8%);

[0043] The tertiary industry: 18%. The survey of re-employed people tends to be more inclined towards the tertiary industry: tourism (4%), retail (5%), and catering (8%);

[0044] In this embodiment, specifically: skill level, first investigate the educational background, the educational background investigation includes high school and below and college and above,

[0045] Among them, those with high school education or below: 55%, those with high school education or below tend to be employed in welding (10%), maintenance (20%), and transportation (10%).

[0046] College graduates and above: 45%. Re-employment of college graduates and above tends to be in education (25%), after-sales service (10%), and further study (5%).

[0047] Working principle or structural principle: The technical solution of the re-employment survey method of retired fishermen in the Yangtze River based on the big data of the Internet of Things includes the following steps;

[0048] Step 1: First, establish a real-name dynamic database of retired fishermen, use the Internet of Things technology to collect basic information, re-employment intentions, skill levels and other data of retired fishermen, and connect with the retired fishing boat management information system in key waters of the Yangtze River Basin and the real-name dynamic assistance information system for resettlement and protection of retired fishermen to ensure real-time data update and sharing, and collect activity data of retired fishermen in real time;

[0049] Step 2: Use big data analysis technology to deeply mine and analyze the data of retired fishermen, identify fishermen with re-employment potential, as well as their re-employment intentions and job types. Based on the analysis results, formulate a questionnaire survey and push detailed information such as specific re-employment needs, expected salary, and work location preferences to target fishermen through IoT devices (such as smartphones, tablets, etc.);

[0050] Step 3: Use data analysis algorithms (such as cluster analysis, association rules, image comparison tools, etc.) to improve data quality and accuracy; analyze feature data to explore the reemployment patterns of retired fishermen and key indicators in the reemployment process of retired fishermen, such as job matching, training participation, entrepreneurial success rate, etc., to evaluate the effectiveness of reemployment assistance measures in real time, and dynamically adjust assistance strategies based on feedback;

[0051] Step 4: Based on the results of data analysis, provide the government with suggestions for optimizing the reemployment policy for retired fishermen. For example, the government can absorb retired fishermen with working ability by setting up public welfare positions, such as finless porpoise patrol officers, road protectors, and water managers. This not only provides employment opportunities for fishermen, but also helps to protect the ecological environment. At the same time, in response to the problems existing in the reemployment process of retired fishermen, propose measures to improve employment services, including providing diversified employment channels, strengthening skills training, optimizing social security policies, and providing personalized employment recommendation services based on the personal characteristics and employment needs of retired fishermen, so as to improve the efficiency and quality of reemployment services for retired fishermen.

[0052] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A survey method for re-employment of retired fishermen in the Yangtze River based on IoT big data, characterized by: The following steps are involved: S1. Establish a real-name dynamic database of retired fishermen, and use Internet of Things technology to collect basic information, re-employment intentions, skill levels and other data of retired fishermen; S2, transmitting the collected data to the data center for storage via the wireless communication network; S3. Use big data analysis technology to process and analyze data; S4. Develop a questionnaire survey based on the data analysis results and then put forward targeted policy recommendations; S5. Establish a long-term monitoring mechanism to continuously track the employment and living conditions of retired fishermen to ensure the flexibility and adjustability of policies.

2. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 1 is characterized by: The real-name dynamic database of retired fishermen in step S1 includes fishermen's identity verification information, fishermen's historical fishing experience and skills, fishermen's willingness and expectations for re-employment, and collects basic information, re-employment intention, skill level and other data of retired fishermen based on the dynamic database.

3. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 1 is characterized by: In S2, a data transmission network and data storage and management are established. The data transmission network uses wireless communication technology to transmit the data collected by IoT devices to a data center or a cloud storage platform in real time. The data storage and management adopts a distributed storage architecture and a database management system to store and manage massive data efficiently, securely and orderly.

4. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 1 is characterized by: The big data analysis technology in step S3 includes data cleaning and preprocessing technology, cluster analysis and association rule mining technology, and prediction model building technology. The data cleaning and preprocessing technology is used to improve data quality, the cluster analysis and association rule mining technology is used to identify the common characteristics and employment preferences of fishermen groups, and the prediction model building technology can predict the re-employment potential of fishermen and the difficulties they may encounter.

5. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 1 is characterized by: The questionnaire survey in S4 is intended to understand the cultural level and comprehension ability of fishermen, the employment market demand in the areas where fishermen are located, and the re-employment assistance policies and resources provided by the government and all sectors of society, and then put forward targeted policy recommendations.

6. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 1 is characterized by: The monitoring mechanism in S5 is based on the fishermen's participation in re-employment training and training effect evaluation, the progress of fishermen's entrepreneurial projects and operating benefits, and the fishermen's adaptability to new positions and satisfaction surveys, and then builds a re-employment service platform to integrate employment information, training resources, and entrepreneurship support policies. Through the Internet of Things technology, information can be quickly transmitted and accurately pushed, providing one-stop re-employment services for retired fishermen, providing government departments with suggestions on optimizing re-employment policies for retired fishermen, and cooperating with government departments to promote the implementation of policy suggestions and regularly evaluate policy effects in order to make timely adjustments and optimizations.

7. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 2 is characterized by: The industries of the re-employment intention survey are based on three major industries: Among them, the primary industry: 57%, the survey of re-employed personnel tends to be more inclined towards the primary industry: planting (28%), forestry (14%), animal husbandry (18%); Secondary industry: 25%. The survey of re-employed workers tends to be more inclined towards the following secondary industries: agricultural and sideline food processing industry (7%), furniture manufacturing industry (11%), and textile industry (8%); The tertiary industry: 18%. The survey of re-employed persons tends to be more inclined towards the tertiary industry: tourism (4%), retail (5%), and catering (8%).

8. The re-employment survey method for retired fishermen in the Yangtze River based on the Internet of Things big data according to claim 1 is characterized by: The skill level mentioned above is first investigated based on academic qualifications, which include high school and below and college and above. Among them, those with high school education or below: 55%, those with high school education or below tend to be employed in welding (10%), maintenance (20%), and transportation (10%). College graduates and above: 45%. Re-employment of college graduates and above tends to be in education (25%), after-sales service (10%), and further study (5%).

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