Cloud-based education recruitment resource integration system and method, and storage medium

Abstract

The invention discloses a cloud-based education recruitment resource integration system and method and a storage medium, and relates to the technical field of data integration. The system comprises a storage cloud, a data receiving module, a directory creating module, a qualification calling module and a data sharing module. The method disclosed by the invention is suitable for the system, and the disclosed electronic equipment is matched with the method. According to the invention, the educational recruitment resource data is subjected to data arrangement, so that the occupational employment information corresponding to the educational recruitment resource data is clarified and then stored in the storage cloud, and data sharing is carried out in a secure network environment, so that resource integration of the educational recruitment resource data is realized, and the utilization rate of the educational recruitment resource data is improved. Clear and accurate job seeker qualification data are provided for recruiters, and an accurate data basis is provided for post matching and personnel demands.

Description

Technical Field

[0001] This application relates to the field of data integration technology, specifically a cloud-based education recruitment resource integration system, method, and storage medium. Background Technology

[0002] As Chinese enterprises develop, they will continue to place greater emphasis on human resource management, creating a huge potential market for human resource services. Saving costs in recruitment is crucial for businesses and promotes their growth. The online recruitment market has already penetrated multiple links in the human resource service industry chain, maximizing resource utilization and service efficiency.

[0003] Online recruitment, through the use of technology, helps corporate HR managers complete the recruitment process. Traditional recruitment websites, whose main revenue comes from corporate payments, often blindly submit large numbers of resumes because job seekers' qualifications cannot be assessed solely from their resumes. The so-called system matching function is practically useless, with corporate HR inboxes overflowing with completely mismatched resumes, reducing work efficiency. These factors undoubtedly create a paradoxical predicament where companies struggle to recruit and job seekers struggle to find jobs, and educational institutions struggle to accurately identify suitable talent. Summary of the Invention

[0004] The purpose of this application is to provide a cloud-based education recruitment resource integration system, method, and storage medium to solve the technical problems mentioned in the background.

[0005] To achieve the above objectives, this application discloses the following technical solutions:

[0006] Firstly, this application provides a cloud-based education recruitment resource integration system, including cloud storage, a data receiving module, a directory creation module, a qualification retrieval module, and a data sharing module;

[0007] The cloud storage is configured to store educational recruitment resource data, which includes job applicant resumes and recommendation letters.

[0008] The data receiving module is configured to upload educational recruitment data, analyze the received educational recruitment data, and match it in the storage cloud. When the personnel information corresponding to the educational recruitment data is an existing personnel, the educational recruitment data is stored in the storage block corresponding to the existing personnel in the storage cloud; otherwise, the educational recruitment data is transmitted to the directory creation module.

[0009] The directory creation module is configured to: create corresponding storage blocks based on the personnel information corresponding to the received education recruitment data, split the education recruitment data according to preset storage entries, classify and store the information segments in the education recruitment data in different storage entries, and upload the created storage blocks to the storage cloud for storage.

[0010] The qualification retrieval module is configured to: verify the received education recruitment data based on big data, and update the corresponding education recruitment data based on the verified data; and collect the education qualifications corresponding to the personnel information corresponding to the education recruitment data based on big data, and update the corresponding education recruitment data based on the collected data.

[0011] The data sharing module is configured to share data stored in the cloud under a secure network environment.

[0012] Preferably, the process of splitting the education recruitment data according to preset storage entries specifically includes:

[0013] Pre-defined storage entries correspond to several entry data characteristics;

[0014] The data features of the aforementioned entries are traversed and matched within the education recruitment data.

[0015] Extract the successfully matched data segments from the education recruitment data;

[0016] When at least two types of storage entries' data features are matched in any extracted data segment, each matched storage entry is defined as a target entry. The correlation coefficient ρ of the data segment relative to each target entry is calculated, and the data segment is classified under the storage entry corresponding to the larger correlation coefficient ρ. The correlation coefficient ρ is calculated using the following formula:

[0017]

[0018] Among them, G e G represents the number of data features of the entries matched in a single target entry. all Kpi1 is the total number of all data features extracted from the data segment, Kpi1 is the preset first performance index coefficient, and G is the total number of data features extracted from the data segment. E Kpi2 represents the number of data features in a single target entry, and Kpi2 is a preset second performance indicator coefficient.

[0019] As a preferred option, the system also includes: a resource monitoring module;

[0020] The resource monitoring module is configured to: evaluate and update the educational qualifications of the educational recruitment data in each storage block based on data surveys.

[0021] Preferably, the resource supervision module includes a teacher qualification traceability unit, a list generation unit, a feedback sending and receiving unit, an information analysis unit, and a teacher qualification evaluation unit;

[0022] The teacher qualification traceability unit is configured to: based on the personnel information corresponding to the education recruitment data, confirm the educational experience and employment information of the corresponding education personnel; the employment information includes past employers and / or current employers, and teaching career information;

[0023] The list generation unit is configured to generate a professional competence survey form based on the educational background and employment information.

[0024] The feedback sending and receiving unit is configured to: send a survey cooperation request to relevant personnel who work with the educator based on the school corresponding to the educational experience and the previous and / or current employers corresponding to the employment information; and after the relevant personnel accept the survey cooperation request, send the professional ability survey form to the relevant personnel and collect the professional ability survey form returned by the relevant personnel.

[0025] The information analysis unit is configured to perform teacher qualification data analysis on the returned vocational competence survey forms.

[0026] The teacher qualification assessment unit is configured to: assess the educational qualifications of the educator based on the analysis results of the teacher qualification data analysis, and update the assessment results to the corresponding storage block; wherein, the teacher qualification data analysis specifically includes:

[0027] Extract the content of each survey item in the professional ability survey form and its corresponding survey results;

[0028] Define the project content corresponding to all survey results presented in scores as rating types, and classify the research project content and corresponding survey results corresponding to the rating types.

[0029] The project content corresponding to the survey results that are not presented in scores is defined as the text description type. The research project content corresponding to the text description type and its corresponding survey results are subjected to big language analysis, and the corresponding teacher qualification data features are generated based on the big language analysis results.

[0030] Preferably, the educational qualification assessment specifically includes:

[0031] Calculate the sum of the scores for all item content across all rating types, and obtain the first score ∑Fi. score ;

[0032] The teacher qualification data features are matched with a preset teacher qualification evaluation database to obtain the score value corresponding to each teacher qualification data feature. The sum of the score values ​​corresponding to all teacher qualification data features is then calculated to obtain the second score ∑Se. score ;

[0033] Calculate the teacher qualification assessment value SC, SC = ∑Fi score +∑Se score ;

[0034] Calculate the Teacher Qualification Assessment (CoE) results. Where ∑score represents the total score when each item is scored out of full marks.

[0035] Secondly, this application provides a cloud-based method for integrating educational recruitment resources, which includes the following steps:

[0036] Users upload educational recruitment data;

[0037] The received educational recruitment data is analyzed, and personnel information is matched in the cloud storage.

[0038] When the personnel information corresponding to the education recruitment data is an existing personnel in the storage cloud, the education recruitment data is stored in the storage block corresponding to the existing personnel in the storage cloud. Otherwise, a corresponding storage block is created for the personnel information corresponding to the education recruitment data, and the education recruitment data is split according to the preset storage entries. The information segments in the education recruitment data are classified and stored in different storage entries, and the created storage block is uploaded to the storage cloud for storage.

[0039] The system uses big data to verify the received education recruitment data and updates the corresponding education recruitment data based on the verified data; it also collects the educational qualifications of the personnel based on the personnel information corresponding to the education recruitment data and updates the corresponding education recruitment data based on the collected data.

[0040] In a secure network environment, the data stored in the cloud can be shared.

[0041] Preferably, the process of splitting the education recruitment data according to preset storage entries specifically includes:

[0042] Pre-defined storage entries correspond to several entry data characteristics;

[0043] The data features of the aforementioned entries are traversed and matched within the education recruitment data.

[0044] Extract the successfully matched data segments from the education recruitment data;

[0045] When at least two types of storage entries' data features are matched in any extracted data segment, each matched storage entry is defined as a target entry. The correlation coefficient ρ of the data segment relative to each target entry is calculated, and the data segment is classified under the storage entry corresponding to the larger correlation coefficient ρ. The correlation coefficient ρ is calculated using the following formula:

[0046]

[0047] Among them, G e G represents the number of data features of the entries matched in a single target entry. all Kpi1 is the total number of all data features extracted from the data segment, Kpi1 is the preset first performance index coefficient, and G is the total number of data features extracted from the data segment. E Kpi2 represents the number of data features in a single target entry, and Kpi2 is a preset second performance indicator coefficient.

[0048] Preferably, the method further includes:

[0049] Based on the personnel information corresponding to the education recruitment data, the educational background and employment information of the corresponding education personnel are confirmed; the employment information includes past employers and / or current employers, and teaching occupation information.

[0050] A career competency survey form is generated based on the educational background and employment information.

[0051] Based on the school attended corresponding to the educational experience and the previous and / or current employers corresponding to the employment information, a survey cooperation request is sent to relevant personnel who have worked with the educator. After the relevant personnel accept the survey cooperation request, the professional ability survey form is sent to the relevant personnel, and the professional ability survey forms returned by the relevant personnel are collected.

[0052] The returned professional competence survey forms are analyzed using teacher qualification data. Based on the analysis results, the educator's educational qualifications are assessed, and the assessment results are updated in the corresponding storage block. Specifically, the teacher qualification data analysis includes:

[0053] Extract the content of each survey item in the professional ability survey form and its corresponding survey results;

[0054] Define the project content corresponding to all survey results presented in scores as rating types, and classify the research project content and corresponding survey results corresponding to the rating types.

[0055] The project content corresponding to the survey results that are not presented in scores is defined as the text description type. The research project content corresponding to the text description type and its corresponding survey results are subjected to big language analysis, and the corresponding teacher qualification data features are generated based on the big language analysis results.

[0056] Preferably, the educational qualification assessment specifically includes:

[0057] Calculate the sum of the scores for all item content across all rating types, and obtain the first score ∑Fi. score ;

[0058] The teacher qualification data features are matched with a preset teacher qualification evaluation database to obtain the score value corresponding to each teacher qualification data feature. The sum of the score values ​​corresponding to all teacher qualification data features is then calculated to obtain the second score ∑Se. score ;

[0059] Calculate the teacher qualification assessment value SC, SC = ∑Fi score +∑Se score ;

[0060] Calculate the Teacher Qualification Assessment (CoE) results. Where ∑score represents the total score when each item is scored out of full marks.

[0061] Thirdly, this application provides an electronic device including at least one processor and at least one memory, the memory being signal-connected to the processor, the memory storing a computer program executable by the processor, and when the computer program is executed by the processor, implementing the cloud-based education recruitment resource integration method described above.

[0062] Beneficial effects: This application organizes educational recruitment resource data, thereby clarifying the corresponding occupational employment information, and then stores it in the cloud for data sharing in a secure network environment. This achieves resource integration of educational recruitment resource data and provides recruiters with clear and accurate job seeker qualification data, providing a precise data foundation for job matching and personnel needs. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1This is a structural block diagram of a cloud-based education recruitment resource integration system provided in an embodiment of this application. Detailed Implementation

[0065] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0066] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0067] This embodiment discloses, in a first aspect, a method as follows: Figure 1 The cloud-based education recruitment resource integration system shown includes cloud storage, a data receiving module, a directory creation module, a qualification retrieval module, and a data sharing module.

[0068] Specifically, the cloud storage is configured to store educational recruitment resource data, which includes job applicants' resumes and letters of recommendation.

[0069] Specifically, the data receiving module is configured to upload educational recruitment data, analyze the received educational recruitment data, and match it in the cloud storage. When the personnel information corresponding to the educational recruitment data is an existing personnel, the educational recruitment data is stored in the storage block in the cloud storage corresponding to the existing personnel; otherwise, the educational recruitment data is transmitted to the directory creation module.

[0070] Specifically, the directory creation module is configured to: create corresponding storage blocks based on the personnel information corresponding to the received education recruitment data, split the education recruitment data according to preset storage entries, classify and store the information segments in the education recruitment data in different storage entries, and upload the created storage blocks to the storage cloud for storage.

[0071] Specifically, the qualification retrieval module is configured to: verify the received education recruitment data based on big data, and update the corresponding education recruitment data based on the verified data; and collect the corresponding education qualifications of the personnel information based on big data, and update the corresponding education recruitment data based on the collected data.

[0072] Specifically, the data sharing module is configured to share data stored in the cloud within a secure network environment. It should be understood that a secure network environment can be any type of authorized network access environment; therefore, appropriate network security protection hardware and software, such as firewalls, need to be configured. This technology is existing and will not be elaborated upon here.

[0073] In this embodiment, the process of splitting the education recruitment data according to preset storage entries specifically includes:

[0074] Pre-defined storage entries correspond to several entry data characteristics;

[0075] The data features of the aforementioned entries are traversed and matched within the education recruitment data.

[0076] Extract the successfully matched data segments from the education recruitment data;

[0077] When at least two types of storage entries' data features are matched in any extracted data segment, each matched storage entry is defined as a target entry. The correlation coefficient ρ of the data segment relative to each target entry is calculated, and the data segment is classified under the storage entry corresponding to the larger correlation coefficient ρ. The correlation coefficient ρ is calculated using the following formula:

[0078]

[0079] Among them, G e G represents the number of data features of the entries matched in a single target entry. all Kpi1 is the total number of all data features extracted from the data segment, Kpi1 is the preset first performance index coefficient, and G is the total number of data features extracted from the data segment. E Kpi2 represents the number of data features in a single target entry, and Kpi2 is a preset second performance indicator coefficient.

[0080] In a preferred embodiment of this invention, the system further includes a resource monitoring module; the resource monitoring module is configured to perform educational qualification assessment and updates on educational recruitment data within each storage block based on data surveys. The resource monitoring module includes a teacher qualification traceability unit, a list generation unit, a feedback sending and receiving unit, an information analysis unit, and a teacher qualification assessment unit.

[0081] The teacher qualification traceability unit is configured to: based on the personnel information corresponding to the education recruitment data, confirm the educational experience and employment information of the corresponding education personnel; the employment information includes past employers and / or current employers, and teaching career information;

[0082] The list generation unit is configured to generate a professional competence survey form based on the educational background and employment information.

[0083] The feedback sending and receiving unit is configured to: send a survey cooperation request to relevant personnel who work with the educator based on the school corresponding to the educational experience and the previous and / or current employers corresponding to the employment information; and after the relevant personnel accept the survey cooperation request, send the professional ability survey form to the relevant personnel and collect the professional ability survey form returned by the relevant personnel.

[0084] The information analysis unit is configured to perform teacher qualification data analysis on the returned vocational competence survey forms.

[0085] The teacher qualification assessment unit is configured to: assess the educational qualifications of the educator based on the analysis results of the teacher qualification data analysis, and update the assessment results to the corresponding storage block; wherein, the teacher qualification data analysis specifically includes:

[0086] Extract the content of each survey item in the professional ability survey form and its corresponding survey results;

[0087] Define the project content corresponding to all survey results presented in scores as rating types, and classify the research project content and corresponding survey results corresponding to the rating types.

[0088] The project content corresponding to the survey results that are not presented in scores is defined as the text description type. The research project content corresponding to the text description type and its corresponding survey results are subjected to big language analysis, and the corresponding teacher qualification data features are generated based on the big language analysis results.

[0089] Furthermore, the aforementioned educational qualification assessment specifically includes:

[0090] Calculate the sum of the scores for all item content across all rating types, and obtain the first score ∑Fi. score ;

[0091] The teacher qualification data features are matched with a preset teacher qualification evaluation database to obtain the score value corresponding to each teacher qualification data feature. The sum of the score values ​​corresponding to all teacher qualification data features is then calculated to obtain the second score ∑Se. score ;

[0092] Calculate the teacher qualification assessment value SC, SC = ∑Fi score+∑Se score ;

[0093] Calculate the Teacher Qualification Assessment (CoE) results. Where ∑score represents the total score when each item is scored out of full marks.

[0094] In a second aspect, this embodiment discloses a cloud-based method for integrating educational recruitment resources. This method is applicable to the aforementioned cloud-based educational recruitment resource integration system. Specifically, the method includes the following steps:

[0095] Users upload educational recruitment data;

[0096] The received educational recruitment data is analyzed, and personnel information is matched in the cloud storage.

[0097] When the personnel information corresponding to the education recruitment data is an existing personnel in the storage cloud, the education recruitment data is stored in the storage block corresponding to the existing personnel in the storage cloud. Otherwise, a corresponding storage block is created for the personnel information corresponding to the education recruitment data, and the education recruitment data is split according to the preset storage entries. The information segments in the education recruitment data are classified and stored in different storage entries, and the created storage block is uploaded to the storage cloud for storage.

[0098] The system uses big data to verify the received education recruitment data and updates the corresponding education recruitment data based on the verified data; it also collects the educational qualifications of the personnel based on the personnel information corresponding to the education recruitment data and updates the corresponding education recruitment data based on the collected data.

[0099] In a secure network environment, the data stored in the cloud can be shared.

[0100] In this embodiment, the process of splitting the education recruitment data according to preset storage entries specifically includes:

[0101] Pre-defined storage entries correspond to several entry data characteristics;

[0102] The data features of the aforementioned entries are traversed and matched within the education recruitment data.

[0103] Extract the successfully matched data segments from the education recruitment data;

[0104] When at least two types of storage entries' data features are matched in any extracted data segment, each matched storage entry is defined as a target entry. The correlation coefficient ρ of the data segment relative to each target entry is calculated, and the data segment is classified under the storage entry corresponding to the larger correlation coefficient ρ. The correlation coefficient ρ is calculated using the following formula:

[0105]

[0106] Among them, G e G represents the number of data features of the entries matched in a single target entry. all Kpi1 is the total number of all data features extracted from the data segment, Kpi1 is the preset first performance index coefficient, and G is the total number of data features extracted from the data segment. E Kpi2 represents the number of data features in a single target entry, and Kpi2 is a preset second performance indicator coefficient.

[0107] In a preferred embodiment of this method, the method further includes:

[0108] Based on the personnel information corresponding to the education recruitment data, the educational background and employment information of the corresponding education personnel are confirmed; the employment information includes past employers and / or current employers, and teaching occupation information.

[0109] A career competency survey form is generated based on the educational background and employment information.

[0110] Based on the school attended corresponding to the educational experience and the previous and / or current employers corresponding to the employment information, a survey cooperation request is sent to relevant personnel who have worked with the educator. After the relevant personnel accept the survey cooperation request, the professional ability survey form is sent to the relevant personnel, and the professional ability survey forms returned by the relevant personnel are collected.

[0111] The returned professional competence survey forms are analyzed using teacher qualification data. Based on the analysis results, the educator's educational qualifications are assessed, and the assessment results are updated in the corresponding storage block. Specifically, the teacher qualification data analysis includes:

[0112] Extract the content of each survey item in the professional ability survey form and its corresponding survey results;

[0113] Define the project content corresponding to all survey results presented in scores as rating types, and classify the research project content and corresponding survey results corresponding to the rating types.

[0114] The project content corresponding to the survey results that are not presented in scores is defined as the text description type. The research project content corresponding to the text description type and its corresponding survey results are subjected to big language analysis, and the corresponding teacher qualification data features are generated based on the big language analysis results.

[0115] Furthermore, the aforementioned educational qualification assessment specifically includes:

[0116] Calculate the sum of the scores for all item content across all rating types, and obtain the first score ∑Fi. score ;

[0117] The teacher qualification data features are matched with a preset teacher qualification evaluation database to obtain the score value corresponding to each teacher qualification data feature. The sum of the score values ​​corresponding to all teacher qualification data features is then calculated to obtain the second score ∑Se. score ;

[0118] Calculate the teacher qualification assessment value SC, SC = ∑Fi score +∑Se score ;

[0119] Calculate the Teacher Qualification Assessment (CoE) results. Where ∑score represents the total score when each item is scored out of full marks.

[0120] It should be noted that since the method proposed in this text is applicable to this system, other technical solutions corresponding to each component of the system can be referred to the description in the technical content of the method, and will not be elaborated in this text.

[0121] Thirdly, this application provides an electronic device including at least one processor and at least one memory, the memory being signal-connected to the processor, the memory storing a computer program executable by the processor, and when the computer program is executed by the processor, implementing the cloud-based education recruitment resource integration method described above.

[0122] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0123] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A cloud-based education recruitment resource integration system, characterized in that, It includes cloud storage, data receiving module, directory creation module, qualification retrieval module, and data sharing module; The cloud storage is configured to store educational recruitment resource data, which includes job applicant resumes and recommendation letters. The data receiving module is configured to upload educational recruitment data, analyze the received educational recruitment data, and match it in the storage cloud. When the personnel information corresponding to the educational recruitment data is an existing personnel, the educational recruitment data is stored in the storage block corresponding to the existing personnel in the storage cloud; otherwise, the educational recruitment data is transmitted to the directory creation module. The directory creation module is configured to: create corresponding storage blocks based on the personnel information corresponding to the received education recruitment data, split the education recruitment data according to preset storage entries, classify and store the information segments in the education recruitment data in different storage entries, and upload the created storage blocks to the storage cloud for storage. The qualification retrieval module is configured to: verify the received education recruitment data based on big data, and update the corresponding education recruitment data based on the verified data; and collect the education qualifications corresponding to the personnel information corresponding to the education recruitment data based on big data, and update the corresponding education recruitment data based on the collected data. The data sharing module is configured to share data stored in the cloud under a secure network environment.

2. The cloud-based education recruitment resource integration system according to claim 1, characterized in that, The process of splitting the education recruitment data according to preset storage entries specifically includes: Pre-defined storage entries correspond to several entry data characteristics; The data features of the aforementioned entries are traversed and matched within the education recruitment data. Extract the successfully matched data segments from the education recruitment data; When at least two types of storage entries' data features are matched in any extracted data segment, each matched storage entry is defined as a target entry. The correlation coefficient ρ of the data segment relative to each target entry is calculated, and the data segment is classified under the storage entry corresponding to the larger correlation coefficient ρ. The correlation coefficient ρ is calculated using the following formula: Among them, G e G represents the number of data features of the entries matched in a single target entry. all Kpi1 is the total number of all data features extracted from the data segment, Kpi1 is the preset first performance index coefficient, and G is the total number of data features extracted from the data segment. E Kpi2 represents the number of data features in a single target entry, and Kpi2 is a preset second performance indicator coefficient.

3. The cloud-based education recruitment resource integration system according to claim 1, characterized in that, The system also includes: a resource monitoring module; The resource monitoring module is configured to: evaluate and update the educational qualifications of the educational recruitment data in each storage block based on data surveys.

4. The cloud-based education recruitment resource integration system according to claim 3, characterized in that, The resource supervision module includes a teacher qualification traceability unit, a list generation unit, a feedback sending and receiving unit, an information analysis unit, and a teacher qualification evaluation unit; The teacher qualification traceability unit is configured to: based on the personnel information corresponding to the education recruitment data, confirm the educational experience and employment information of the corresponding education personnel; the employment information includes past employers and / or current employers, and teaching career information; The list generation unit is configured to generate a professional competence survey form based on the educational background and employment information. The feedback sending and receiving unit is configured to: send a survey cooperation request to relevant personnel who work with the educator based on the school corresponding to the educational experience and the previous and / or current employers corresponding to the employment information; and after the relevant personnel accept the survey cooperation request, send the professional ability survey form to the relevant personnel and collect the professional ability survey form returned by the relevant personnel. The information analysis unit is configured to perform teacher qualification data analysis on the returned vocational competence survey forms. The teacher qualification assessment unit is configured to: assess the educational qualifications of the educator based on the analysis results of the teacher qualification data analysis, and update the assessment results to the corresponding storage block; wherein, the teacher qualification data analysis specifically includes: Extract the content of each survey item in the professional ability survey form and its corresponding survey results; Define the project content corresponding to all survey results presented in scores as rating types, and classify the research project content and corresponding survey results corresponding to the rating types. The project content corresponding to the survey results that are not presented in scores is defined as the text description type. The research project content corresponding to the text description type and its corresponding survey results are subjected to big language analysis, and the corresponding teacher qualification data features are generated based on the big language analysis results.

5. The cloud-based education recruitment resource integration system according to claim 4, characterized in that, The aforementioned educational qualification assessment specifically includes: Calculate the sum of the scores for all item content across all rating types, and obtain the first score ∑Fi. score ; The teacher qualification data features are matched with a preset teacher qualification evaluation database to obtain the score value corresponding to each teacher qualification data feature. The sum of the score values ​​corresponding to all teacher qualification data features is then calculated to obtain the second score ∑Se. score ; Calculate the teacher qualification assessment value SC, SC = ∑Fi score +∑Se score ; Calculate the Teacher Qualification Assessment (CoE) results. Where ∑score represents the total score when each item is scored out of full marks.

6. A cloud-based method for integrating educational recruitment resources, characterized in that, The method includes the following steps: Users upload educational recruitment data; The received educational recruitment data is analyzed, and personnel information is matched in the cloud storage. When the personnel information corresponding to the education recruitment data is an existing personnel in the storage cloud, the education recruitment data is stored in the storage block corresponding to the existing personnel in the storage cloud. Otherwise, a corresponding storage block is created for the personnel information corresponding to the education recruitment data, and the education recruitment data is split according to the preset storage entries. The information segments in the education recruitment data are classified and stored in different storage entries, and the created storage block is uploaded to the storage cloud for storage. The system uses big data to verify the received education recruitment data and updates the corresponding education recruitment data based on the verified data; it also collects the educational qualifications of the personnel based on the personnel information corresponding to the education recruitment data and updates the corresponding education recruitment data based on the collected data. In a secure network environment, the data stored in the cloud can be shared.

7. The cloud-based educational recruitment resource integration method according to claim 6, characterized in that, The process of splitting the education recruitment data according to preset storage entries specifically includes: Pre-defined storage entries correspond to several entry data characteristics; The data features of the aforementioned entries are traversed and matched within the education recruitment data. Extract the successfully matched data segments from the education recruitment data; When at least two types of storage entries' data features are matched in any extracted data segment, each matched storage entry is defined as a target entry. The correlation coefficient ρ of the data segment relative to each target entry is calculated, and the data segment is classified under the storage entry corresponding to the larger correlation coefficient ρ. The correlation coefficient ρ is calculated using the following formula: Among them, G e G represents the number of data features of the entries matched in a single target entry. all Kpi1 is the total number of all data features extracted from the data segment, Kpi1 is the preset first performance index coefficient, and G is the total number of data features extracted from the data segment. E Kpi2 represents the number of data features in a single target entry, and Kpi2 is a preset second performance indicator coefficient.

8. The cloud-based educational recruitment resource integration method according to claim 6, characterized in that, The method also includes: Based on the personnel information corresponding to the education recruitment data, the educational background and employment information of the corresponding education personnel are confirmed; the employment information includes past employers and / or current employers, and teaching occupation information. A career competency survey form is generated based on the educational background and employment information. Based on the school attended corresponding to the educational experience and the previous and / or current employers corresponding to the employment information, a survey cooperation request is sent to relevant personnel who have worked with the educator. After the relevant personnel accept the survey cooperation request, the professional ability survey form is sent to the relevant personnel, and the professional ability survey forms returned by the relevant personnel are collected. The returned professional competence survey forms are analyzed using teacher qualification data. Based on the analysis results, the educator's educational qualifications are assessed, and the assessment results are updated in the corresponding storage block. Specifically, the teacher qualification data analysis includes: Extract the content of each survey item in the professional ability survey form and its corresponding survey results; Define the project content corresponding to all survey results presented in scores as rating types, and classify the research project content and corresponding survey results corresponding to the rating types. The project content corresponding to the survey results that are not presented in scores is defined as the text description type. The research project content corresponding to the text description type and its corresponding survey results are subjected to big language analysis, and the corresponding teacher qualification data features are generated based on the big language analysis results.

9. The cloud-based educational recruitment resource integration method according to claim 8, characterized in that, The aforementioned educational qualification assessment specifically includes: Calculate the sum of the scores for all item content across all rating types, and obtain the first score ∑Fi. score ; The teacher qualification data features are matched with a preset teacher qualification evaluation database to obtain the score value corresponding to each teacher qualification data feature. The sum of the score values ​​corresponding to all teacher qualification data features is then calculated to obtain the second score ΣSe. score ; Calculate the teacher qualification assessment value SC, SC = ΣFi score +∑Se score ; Calculate the Teacher Qualification Assessment (CoE) results. Where ∑score represents the total score when each item is scored out of full marks.

10. An electronic device, characterized in that, It includes at least one processor and at least one memory, the memory being signal-connected to the processor, the memory storing a computer program executable by the processor, and when the computer program is executed by the processor, implementing the cloud-based education recruitment resource integration method as described in any one of claims 6-9.

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