People and post matching method, device and equipment for basic labor service industry and medium

Abstract

The invention discloses a man-post matching method, device and equipment for a basic labor service industry and a medium. The method comprises the following steps: determining the filled ability of an applicant according to a resume delivered by the applicant; according to the filled ability, the construction log of the project in which the applicant participates, the labor service industry knowledge graph and the interview video of the field operation construction tool of the applicant, mining the non-filled ability of the applicant; and determining a target post matched with the applicant according to the filled ability and the non-filled ability. According to the embodiment of the invention, accurate recruitment of related posts in the basic labor service industry can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent recruitment, and particularly to a method, device, equipment and medium for matching people with jobs in the basic labor service industry. Background Art

[0002] With the development of society, more and more recruitment work has become standardized, and the resumes provided by applicants have become an important reference factor for matching people with jobs. For industries with relatively high average educational levels, applicants can usually repeat their abilities in their resumes, and the reference value of the resumes is relatively high.

[0003] However, for industries with relatively average educational levels, such as relatively basic labor service industries like engineering construction, decoration and logistics. The expression ability of applicants is average, and many of their actual abilities are not directly filled in the resume. For example, during the construction process, an applicant has a good habit of wearing safety protection equipment, but the applicant is not aware that this is a manifestation of their ability of "having excellent safety protection awareness" and does not fill this ability in the resume. This results in a relatively low reference value of the resume, seriously affecting the subsequent matching of people with jobs based on the abilities of applicants. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for matching people with jobs in the basic labor service industry to achieve precise recruitment for relevant positions in the basic labor service industry.

[0005] According to one aspect of the present invention, a method for matching people with jobs in the basic labor service industry is provided, including:

[0006] Determining the filled-in abilities of an applicant according to the resume submitted by the applicant;

[0007] Mining the unfilled-in abilities of the applicant according to the filled-in abilities, the construction logs of the projects the applicant has participated in, the knowledge graph of the labor service industry, and the interview video of the applicant operating construction tools on site;

[0008] Determining the target positions matching the applicant according to the filled-in abilities and the unfilled-in abilities.

[0009] According to another aspect of the present invention, a device for matching people with jobs in the basic labor service industry is provided, including:

[0010] A determining module, configured to determine the filled-in abilities of an applicant according to the resume submitted by the applicant;

[0011] A mining module, configured to mine the unfilled-in abilities of the applicant according to the filled-in abilities, the construction logs of the projects the applicant has participated in, the knowledge graph of the labor service industry, and the interview video of the applicant operating construction tools on site;

[0012] A matching module, configured to determine a target position that matches the applicant according to the filled capabilities and the unfilled capabilities.

[0013] According to another aspect of the present invention, there is provided a computer program product, including a computer program, which when executed by a processor, implements the person-position matching method for the basic labor service industry according to any embodiment of the present invention.

[0014] According to another aspect of the present invention, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the person-position matching method for the basic labor service industry according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for implementing the person-position matching method for the basic labor service industry according to any embodiment of the present invention when executed by a processor.

[0016] By actively mining the unfilled capabilities of the applicant, the embodiments of the present invention comprehensively obtain the capabilities of the applicant, breaking the limitation of only relying on the applicant to fill in the capabilities by himself in the past. In some industries, especially the basic labor service industry, this can significantly improve the accuracy of person-position matching and the accurate recruitment of relevant positions.

[0017] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 is a flowchart of a person-position matching method for the basic labor service industry according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of a person-position matching method for the basic labor service industry according to another embodiment of the present invention;

[0021] Figure 3 It is a schematic structural diagram of a person-job matching device in the basic labor service industry provided according to another embodiment of the present invention;

[0022] Figure 4 It is a schematic structural diagram of an electronic device implementing the embodiment of the present invention. Detailed implementation manners

[0023] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] Figure 1 It is a flowchart of a person-job matching method in the basic labor service industry provided by an embodiment of the present invention. This embodiment is applicable to the situation where after receiving the resume submitted by an applicant in the basic labor service industry, the applicant's capabilities are first mined and then job matching is performed. This method can be executed by a person-job matching device in the basic labor service industry. The device can be implemented in the form of hardware and / or software, and the device can be configured in an electronic device with corresponding data processing capabilities, such as a recruitment system. As Figure 1 shown, the method includes:

[0026] S110. Determine the filled capabilities of the applicant according to the resume submitted by the applicant.

[0027] S120. Mine the unfilled capabilities of the applicant according to the filled capabilities, the construction logs of the projects the applicant has participated in, the labor industry knowledge graph, and the interview video of the applicant operating construction tools on site.

[0028] S130: Determine a target position that matches the applicant based on the filled-in capabilities and the unfilled capabilities.

[0029] Among them, filled-in abilities are abilities that the applicant has actively filled in in the resume, and unfilled abilities are abilities that the applicant has not actively filled in in the resume but objectively possesses. The target positions are positions in the basic labor industry, such as plumbers and carpenters.

[0030] Specifically, a three-layer data collection system consisting of structured data, semi-structured data, and unstructured data is established in advance.

[0031] Structured data: This includes basic data such as the applicant's job type, age, gender, and salary expectations, stored in a clear and concise table format for direct reading and analysis.

[0032] Semi-structured data: such as skill certificates and project experience, can be stored in a specific format (such as XML). Certificate information includes fields such as certificate name, issuing authority, and acquisition time; project experience includes fields such as project name, project cycle, role, and project results.

[0033] Unstructured data: Construction log text of the candidate’s previous projects, recorded in natural language; interview videos, stored in multimedia format.

[0034] Resumes submitted by applicants are obtained and the text and image information within them are identified and analyzed to identify their listed competencies. During the interview, applicants are asked to demonstrate the operation of on-site equipment and construction tools, and the demonstration is recorded to create a video interview. Combined with the listed competencies, construction logs, the labor industry knowledge graph, and interview videos of applicants operating on-site construction tools are analyzed one by one to uncover the applicant's unlisted competencies.

[0035] According to the filled-in and unfilled abilities, the ability matrix is constructed. The matrix in the ability matrix is divided into two categories. One category is basic abilities, including filled-in abilities and unfilled abilities, such as job type, years of experience, certificate level, average daily steps (smart bracelet data), high-altitude operation time, total working hours, etc. The other category is derived abilities, which are obtained by calculating the derived dimensions. Taking the high-risk operation adaptation index as an example, its calculation formula is:

[0036]

[0037] Based on the capability matrix, a three-level filtering system consisting of collaborative filtering + content recommendation + expert rules is used to determine the target position that matches the applicant, giving full play to the advantages of different recommendation algorithms and improving matching accuracy.

[0038] In the embodiments of the present invention, by actively mining the abilities not filled in by the applicant, the abilities of the applicant are comprehensively obtained, breaking the limitation of only relying on the applicant to fill in the abilities by himself / herself in the past. In some industries, especially the basic labor service industry, this can significantly improve the accuracy of the person-job matching and the accurate recruitment of relevant positions.

[0039] Figure 2 FIG. is a flowchart of a person-job matching method for the basic labor service industry provided by another embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. As Figure 2 shown, the method includes:

[0040] S210. Determine the filled-in abilities of the applicant according to the resume submitted by the applicant.

[0041] S220. Determine the professional terms related to the applicant in the construction logs of the projects participated in by the applicant, and determine the first unfilled ability of the applicant according to the occurrence frequency of the professional terms in the construction logs.

[0042] S230. Determine the second unfilled ability related to the filled-in ability according to the industry knowledge graph; determine the third unfilled ability of the applicant according to the proficiency and safety awareness of the applicant in operating the construction tools in the interview video of the applicant operating the construction tools on site.

[0043] Specifically, a large number of labeled construction logs are used as training data and input into the text processing (BERT) model. The text processing model classifies each word in the construction log by learning the context information in the construction log to determine whether it is a professional term such as "concrete pouring" or "working at height". After multiple rounds of training, the text processing model can accurately identify the professional terms in the construction log and output them. The construction logs of the projects participated in by the applicant are input into the text processing model, and the text processing model outputs all the professional terms existing in the construction log. On this basis, the target professional terms related to the applicant in the construction Japanese are determined among these professional terms. The occurrence frequency of each target professional term in the construction log is counted, and the target professional terms with an occurrence frequency greater than the preset number are determined as the first unfilled abilities actually possessed by the applicant.

[0044] The industry knowledge graph is constructed through technologies such as knowledge extraction and knowledge fusion. For example, entities (such as "bricklayer", "scaffolding erection", etc.) and relationships between entities (such as "bricklayer" and "scaffolding erection" have an operational association relationship) are extracted from data sources such as technical specification documents, professional books, and industry reports in the construction industry. When it is necessary to explore the abilities of applicants, determine whether there are entities associated with the filled-in abilities in the industry knowledge graph, and determine the professional terms corresponding to these entities as the second unfilled ability. For example, if the filled-in abilities include "having experience as a bricklayer", the knowledge graph can find its association with "having experience in scaffolding erection", and "having experience in scaffolding erection" is determined as the second unfilled ability.

[0045] Analyze the process of the candidate's on-site operation of construction tools in the interview video to determine whether the user has correctly equipped the corresponding safety protection equipment and whether the operating actions are standard, and obtain the user's safety awareness (high or low) and proficiency (high or low) as the third unfilled ability that the candidate actually possesses.

[0046] Based on the above embodiment, optionally, analyzing the candidate's proficiency and safety awareness in operating construction tools through an interview video of the candidate operating construction tools on site includes:

[0047] Detecting whether the interview video of the candidate operating construction tools on site is equipped with safety protection equipment that matches the construction tools, and determining the candidate's safety awareness in operating construction tools based on the detection results;

[0048] The distance between the actual action sequence of the candidate operating the construction tools and the standard action sequence in the interview video of the candidate operating the construction tools on site is calculated, and the proficiency of the candidate in operating the construction tools is determined based on the calculated distance.

[0049] The test results include the presence of safety protection equipment in the video and the specific type of safety protection equipment. Safety protection equipment refers to equipment that protects the applicant's health during work, such as hard hats, goggles, insulating gloves, and earplugs.

[0050] Specifically, a large number of videos containing safety equipment are annotated to form a training dataset. These videos are then fed into a neural network model, which is trained to detect and classify the safety equipment in the videos, resulting in a detection model. During use, the interview video is fed into the detection model, which then outputs a test result. Based on this result, it is determined whether the user is correctly wearing the appropriate safety equipment. The more complete the safety equipment, the higher the user's safety awareness; the less safety equipment is worn, the lower the user's safety awareness.

[0051] Analyze the actual action sequence of the applicant operating the tool in the video, and use the Dynamic Time Warping (DTW) algorithm to match it with the standard operation action sequence. Let the action sequence of the candidate operating the tool be A = {a1, a2, …, am}, and the standard operation action sequence be B = {b1, b2, …, bn}. The DTW algorithm calculates the optimal alignment path between the two sequences to obtain the matching distance d(A, B). The smaller the matching distance, the higher the proficiency of the user; the larger the matching distance, the lower the proficiency of the user.

[0052] S240. Organize the filled capabilities and the unfilled capabilities to obtain the ability matrix of the applicant; determine the first candidate positions of the in-service employees with abilities similar to those of the applicant according to the ability matrix.

[0053] S250. Determine the job description that matches the ability matrix according to the matching model, and determine the second candidate positions corresponding to the job description; determine the third candidate positions that match the special abilities in the ability matrix.

[0054] S260. Cross-compare the first candidate positions, the second candidate positions, and the third candidate positions to determine the target positions that match the applicant.

[0055] Specifically, on the collaborative filtering branch, for each position, determine the ability matrix of the in-service employees of the position. Calculate the similarity between the ability matrix of the in-service employees and the ability matrix of the applicant. If the similarity is greater than the similarity threshold, then determine that the position is the first candidate position.

[0056] On the content recommendation branch, for each position, after preprocessing the job description and the ability matrix of the position, input them into the matching model to obtain the matching score output by the matching model. If the matching score is greater than the score threshold, then determine that the position is the second candidate position.

[0057] On the expert rule branch, since there are some special positions with special constraint conditions such as "special operation personnel must hold certificates to work". For each special position, determine whether there are special abilities in the ability matrix that match the special constraint conditions of the special position. If so, then determine that the special position is the third candidate position; if not, then do not determine that the special position is the third candidate position.

[0058] Cross-compare the first candidate positions, the second candidate positions, and the third candidate positions, and preferentially determine the positions that belong to all three as the target positions. If there are no positions that belong to all three, then determine the positions that belong to both as the target positions.

[0059] Based on the above embodiments, optionally, the weights of the capabilities in the capability matrix are determined according to the current construction environment and the current construction progress.

[0060] Specifically, the skill requirements for different types of work in a construction project vary in different seasons and at different construction stages, and it is difficult for traditional matching rules to be adjusted in real time. Therefore, when constructing the capability matrix in this application, corresponding weights are set for each capability in the capability matrix. The specific weight values can be determined with reference to the current construction environment and the current construction progress. For example, when the current construction environment is the rainy season and the current construction progress is foundation laying, which requires outdoor operations, in this case, the weight of the "waterproof operation experience" capability can be increased during recruitment. When it is not the rainy season and outdoor operations are not required, the weight of this capability can be restored to its normal value.

[0061] By adjusting the importance of capabilities in real time based on industry dynamic changes, the matching becomes more in line with actual needs.

[0062] Based on the above embodiments, optionally, the method further includes:

[0063] After the preset time for the applicant to arrive at the post, determine the matching quality of this match according to preset evaluation indicators; the evaluation indicators include the arrival rate, retention rate, and work injury accident rate;

[0064] Feedback-adjust the matching model according to the matching quality.

[0065] Specifically, establish a matching quality evaluation system. The evaluation indicators include the arrival rate, retention rate, and work injury accident rate over a certain period, as shown in the following examples:

[0066]

[0067] In addition, process indicators such as the click-delivery conversion rate of candidates and the job browsing duration (counted by the stay time of the applicant on the details page of the target job, reflecting the candidate's interest in the target job) can also be designed, as shown in the following examples:

[0068]

[0069] The matching model is feedback - adjusted by using reinforcement learning. Let the state space S be the values of various evaluation indicators and / or process indicators, and the action space A be the adjustment operations on the model parameters (such as adjusting the weights of feature dimensions, modifying the hyperparameters of the recommendation algorithm, etc.). The reward function R is set according to the matching accuracy rate, the shortening of the recruitment cycle, etc. For example, when the matching accuracy rate increases, a reward of +0.5 points is given; when the recruitment cycle is shortened, a reward of +0.3 points is given. By continuously trying different actions and optimizing the matching model parameters according to the reward feedback, the matching quality is improved. By establishing a closed - loop optimization logic including safety indicators (such as the work - related injury accident rate), the recruitment quality in the industry is enhanced, not only paying attention to the recruitment efficiency but also the impact of the recruitment results on the actual work.

[0070] In the embodiment of the present invention, a three - level filtering system of collaborative filtering + content recommendation + expert rules is adopted to achieve accurate matching, giving full play to the advantages of different recommendation algorithms and improving the matching accuracy.

[0071] Figure 3 It is a schematic structural diagram of a person - position matching device for the basic labor service industry provided by another embodiment of the present invention. As Figure 3 shown, the device includes:

[0072] A determination module 310, configured to determine the filled - in capabilities of the applicant according to the resume submitted by the applicant;

[0073] A mining module 320, configured to mine the unfilled - in capabilities of the applicant according to the filled - in capabilities, the construction logs of the projects the applicant has participated in, the labor service industry knowledge graph, and the interview video of the applicant operating construction tools on - site;

[0074] A matching module 330, configured to determine the target position matching the applicant according to the filled - in capabilities and the unfilled - in capabilities.

[0075] The person - position matching device for the basic labor service industry provided by the embodiment of the present invention can execute the person - position matching method for the basic labor service industry provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0076] Optionally, the mining module 330 includes:

[0077] A log mining unit, configured to determine the professional terms associated with the applicant in the construction logs of the projects the applicant has participated in, and determine the first unfilled - in capabilities of the applicant according to the occurrence frequency of the professional terms in the construction logs;

[0078] A graph mining unit, configured to determine the second unfilled - in capabilities related to the filled - in capabilities according to the industry knowledge graph;

[0079] A video mining unit, configured to determine a third unfilled ability of the applicant according to the proficiency and safety awareness of the applicant in operating construction tools in the interview video of the applicant operating construction tools on site.

[0080] Optionally, the determination process of the proficiency and safety awareness is as follows:

[0081] Detect whether there is safety protection equipment matching the construction tools in the interview video of the applicant operating construction tools on site, and determine the safety awareness of the applicant operating construction tools according to the detection result;

[0082] Calculate the distance between the actual action sequence and the standard action sequence of the applicant operating construction tools in the interview video of the applicant operating construction tools on site, and determine the proficiency of the applicant operating construction tools according to the calculated distance.

[0083] Optionally, the matching module 330 includes:

[0084] An arrangement unit, configured to arrange the filled abilities and the unfilled abilities to obtain an ability matrix of the applicant;

[0085] A first screening unit, configured to determine a first candidate position where an on-the-job employee with abilities similar to those of the applicant is located according to the ability matrix;

[0086] A second screening unit, configured to determine a job description matching the ability matrix according to a matching model, and determine a second candidate position corresponding to the job description;

[0087] A third screening unit, configured to determine a third candidate position matching the special abilities in the ability matrix;

[0088] A matching unit, configured to perform cross-comparison on the first candidate position, the second candidate position, and the third candidate position to determine a target position matching the applicant.

[0089] Optionally, the weights of the abilities in the ability matrix are determined according to the current construction environment and the current construction progress.

[0090] Optionally, the device further includes:

[0091] A quality analysis module, configured to determine the matching quality of this match according to preset evaluation indicators after a preset time when the applicant arrives at the post; the evaluation indicators include arrival rate, retention rate, and work injury accident rate;

[0092] A feedback adjustment module, configured to perform feedback adjustment on the matching model according to the matching quality.

[0093] The person-job matching device for the basic labor service industry described further can also execute the person-job matching method for the basic labor service industry provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0094] Figure 4 FIG. shows a schematic structural diagram of an electronic device 40 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0095] As Figure 4 shown, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. The input / output (I / O) interface 45 is also connected to the bus 44.

[0096] A plurality of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0097] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the person-job matching method in the basic labor service industry.

[0098] In some embodiments, the person-job matching method in the basic labor service industry can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the person-job matching method in the basic labor service industry described above can be executed. Alternatively, in other embodiments, the processor 41 can be configured to execute the person-job matching method in the basic labor service industry by any other suitable means (e.g., by means of firmware).

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a special or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0101] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0104] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0105] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0106] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A person-job matching method for the basic labor service industry, characterized in that, The method includes: Determining the filled capabilities of the applicant based on the resume submitted by the applicant; Mining the unfilled capabilities of the applicant based on the filled capabilities, the construction logs of the projects the applicant has participated in, the labor industry knowledge graph, and the interview video of the applicant operating construction tools on-site; Determining the target position that matches the applicant based on the filled capabilities and the unfilled capabilities.

2. The method according to claim 1, wherein The unfilled capabilities include the first unfilled capability, the second unfilled capability, and the third unfilled capability. The mining of the unfilled capabilities of the applicant based on the filled capabilities, the construction logs of the projects the applicant has participated in, the labor industry knowledge graph, and the interview video of the applicant operating construction tools on-site includes: Determining the professional terms associated with the applicant in the construction logs of the projects the applicant has participated in, and determining the first unfilled capability of the applicant based on the frequency of occurrence of the professional terms in the construction logs; Determining the second unfilled capability related to the filled capabilities according to the industry knowledge graph; Determining the third unfilled capability of the applicant based on the proficiency and safety awareness of the applicant operating construction tools in the interview video of the applicant operating construction tools on-site.

3. The method according to claim 2, characterized in that The determination process of the proficiency and safety awareness is as follows: Detecting whether there is safety protection equipment matching the construction tools in the interview video of the applicant operating construction tools on-site, and determining the safety awareness of the applicant operating construction tools based on the detection result; Calculating the distance between the actual action sequence and the standard action sequence of the applicant operating construction tools in the interview video of the applicant operating construction tools on-site, and determining the proficiency of the applicant operating construction tools based on the calculated distance.

4. The method according to claim 1, wherein The determining the target position that matches the applicant based on the filled capabilities and the unfilled capabilities includes: Sorting out the filled capabilities and the unfilled capabilities to obtain the capability matrix of the applicant; Determining the first candidate positions where the in-service employees with capabilities similar to those of the applicant are located based on the capability matrix; Determining the job description that matches the capability matrix according to the matching model, and determining the second candidate positions corresponding to the job description; Determining the third candidate positions that match the special capabilities in the capability matrix; Cross-comparing the first candidate positions, the second candidate positions, and the third candidate positions to determine the target position that matches the applicant.

5. The method according to claim 4, wherein The weights of the capabilities in the capability matrix are determined according to the current construction environment and the current construction progress.

6. The method according to claim 4, wherein The method further includes: After the applicant arrives at the post for a preset time, determining the matching quality of this match according to preset evaluation indicators; the evaluation indicators include the arrival rate, the retention rate, and the work injury accident rate; Making feedback adjustments to the matching model according to the matching quality.

7. A person-job matching device for the basic labor service industry, characterized in that, The device includes: A determination module, configured to determine the filled capabilities of the applicant based on the resume submitted by the applicant; A mining module, configured to mine the unfilled capabilities of the applicant according to the filled capabilities, the construction logs of the projects the applicant has participated in, the labor industry knowledge graph, and the interview video of the applicant operating construction tools on site; A matching module, configured to determine a target position matching the applicant according to the filled capabilities and the unfilled capabilities; 8. The device according to claim 7, characterized in that, The mining module includes: A log mining unit, configured to determine the professional terms associated with the applicant in the construction logs of the projects the applicant has participated in, and determine the first unfilled capabilities of the applicant according to the occurrence frequency of the professional terms in the construction logs; A graph mining unit, configured to determine the second unfilled capabilities related to the filled capabilities according to the industry knowledge graph; A video mining unit, configured to determine the third unfilled capabilities of the applicant according to the proficiency and safety awareness of the applicant operating construction tools in the interview video of the applicant operating construction tools on site.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the person-job matching method for the basic labor industry according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the person-job matching method for the basic labor industry according to any one of claims 1-6 when executed by a processor.

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