Apparatus, method, device and medium for identifying regional industry talent skill gap

Abstract

The present disclosure provides a regional industry talent skill gap identification device, method, apparatus and medium, obtaining industry information corresponding to at least one industry of a target region, the industry information including occupation demand information and training supply information; performing word segmentation processing on the occupation demand information and the training supply information respectively, and screening the word segmentation results to obtain effective supply and demand vocabulary, the effective supply and demand vocabulary including effective demand vocabulary and effective supply vocabulary; performing clustering processing on the effective supply and demand vocabulary to obtain a supply and demand skill vocabulary composed of skill word groups corresponding to each cluster; determining demand skill words and supply skill words from the supply and demand skill vocabulary based on the effective demand vocabulary and the effective supply vocabulary; and generating a target identification result of a talent skill gap for at least one industry of the target region by comparing the demand skill words and the supply skill words, thereby ensuring comprehensiveness and accuracy of identification from two dimensions of demand and supply.

Description

Technical Field

[0001] This disclosure relates to the field of skills identification technology, and more specifically, to a device, method, apparatus, and medium for identifying regional industrial talent skills gaps. Background Technology

[0002] In the process of urban development, industry is the main economic pillar. Understanding the internal industrial structure of a city and promoting the formation and strengthening of industrial clusters and chains are effective ways to improve the economic efficiency of urban development. For industrial development, accurately identifying the skills of industrial talent is particularly important. Industrial talent skills are the driving force behind industrial innovation and upgrading, helping to understand the core competitiveness of industrial development and accurately pinpoint bottlenecks and breakthroughs in industrial development.

[0003] When identifying the skills of industry talents, the dimensions considered are mostly relatively singular, resulting in many limitations in the identification results, making it difficult to meet the requirements of comprehensiveness and accuracy, and thus failing to support the actual needs of industrial development. Summary of the Invention

[0004] This disclosure provides at least one device, method, apparatus, and medium for identifying regional industrial talent skills gaps.

[0005] This disclosure provides a device for identifying regional industrial talent skills gaps, including a processor, a memory, and a display. The processor, memory, and display are connected via a bus. The processor stores computer instructions, and the display shows the processing results of the processor, including target identification results generated by the processor. The processor reads the computer instructions stored in the memory to perform the following operations:

[0006] Obtain industry information corresponding to at least one industry in the target region, wherein the industry information includes occupational demand information and training supply information;

[0007] The occupational demand information and the training supply information are segmented into words respectively, and the segmentation results are filtered to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary.

[0008] The effective supply and demand terms are clustered to obtain a supply and demand skill term library composed of the skill terms corresponding to each cluster.

[0009] Based on the effective vocabulary of demand and the effective vocabulary of supply, demand skill words and supply skill words are determined from the supply and demand skill vocabulary library;

[0010] By comparing the demand skill terms and the supply skill terms, a target identification result for the talent skills gap in at least one industry in the target region is generated.

[0011] In one optional implementation, the step of segmenting the occupational demand information and the training supply information into words, and then filtering the segmentation results to obtain effective supply and demand vocabulary, includes:

[0012] The occupational demand information and the training supply information are respectively processed by word segmentation to obtain multiple occupational demand word segments and multiple training supply word segments;

[0013] The target neural network is used to process each of the occupational demand words and each of the training supply words to obtain the demand word vectors corresponding to each of the occupational demand words and the supply word vectors corresponding to each of the training supply words.

[0014] Based on preset skill word vectors, effective words for demand are selected from the multiple occupational demand word segments, and effective words for supply are selected from the multiple training supply word segments; the preset skill word vectors are obtained by processing preset skill words through the target neural network;

[0015] The effective demand terms and effective supply terms are summarized and deduplicated to obtain effective supply and demand terms.

[0016] In one optional implementation, the step of selecting effective demand words from the plurality of occupational demand word segments and selecting effective supply words from the plurality of training supply word segments based on preset skill word vectors includes:

[0017] Determine the similarity between the preset skill word vector and each of the demand word vectors, and the similarity between the preset skill word vector and each of the supply word vectors;

[0018] The occupational demand word segments corresponding to the demand word vectors with similarity greater than or equal to the target threshold are determined as the effective demand words, and the training supply word segments corresponding to the supply word vectors with similarity greater than or equal to the target threshold are determined as the effective supply words.

[0019] In one optional implementation, the clustering of the effective supply and demand vocabulary to obtain a supply and demand skill vocabulary library composed of skill words corresponding to each cluster includes:

[0020] Clustering is performed on the effective supply and demand terms to obtain at least one cluster;

[0021] For each cluster, skill words matching the cluster are generated based on the effective supply and demand words included in the cluster.

[0022] A supply and demand skill terminology database is formed by combining the various skill terms.

[0023] In one optional implementation, determining demand skill terms and supply skill terms from the supply and demand skill terminology database based on the effective demand vocabulary and the effective supply vocabulary includes:

[0024] For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term.

[0025] In one optional implementation, the step of generating a target identification result for the talent skills gap in at least one industry in the target region by comparing the demand skill terms and the supply skill terms includes:

[0026] The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word.

[0027] Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

[0028] In one alternative implementation, the processor is further configured to perform:

[0029] Based on the target identification results, a development strategy for the at least one industry in the target area is generated. The development strategy is used to indicate adjustment information for various skills belonging to the at least one industry. The development strategy is displayed through the display.

[0030] Receive the editing results of the development strategy being displayed, and publish the editing results.

[0031] This disclosure also provides a method for identifying regional industrial talent skills gaps, the method comprising:

[0032] Obtain industry information corresponding to at least one industry in the target region, wherein the industry information includes occupational demand information and training supply information;

[0033] The occupational demand information and the training supply information are segmented into words respectively, and the segmentation results are filtered to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary.

[0034] The effective supply and demand terms are clustered to obtain a supply and demand skill term library composed of the skill terms corresponding to each cluster.

[0035] Based on the effective vocabulary of demand and the effective vocabulary of supply, demand skill words and supply skill words are determined from the supply and demand skill vocabulary library;

[0036] By comparing the demand skill terms and the supply skill terms, a target identification result for the talent skills gap in at least one industry in the target region is generated.

[0037] This disclosure also provides a device for identifying regional industrial talent skills gaps, the device comprising:

[0038] The data acquisition module is used to acquire industry information corresponding to at least one industry in the target area, the industry information including occupational demand information and training supply information;

[0039] The filtering module is used to perform word segmentation on the occupational demand information and the training supply information respectively, and to filter the word segmentation results to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary.

[0040] The clustering module is used to cluster the effective supply and demand vocabulary to obtain a supply and demand skill vocabulary library composed of the skill words corresponding to each cluster.

[0041] The classification module is used to determine demand skill terms and supply skill terms from the supply and demand skill terminology library based on the effective demand terms and the effective supply terms.

[0042] The comparison module is used to generate a target identification result for the talent skills gap in at least one industry in the target region by comparing the demand skills terms and the supply skills terms.

[0043] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method for identifying regional industry talent skills gaps.

[0044] The present disclosure provides an equipment, method, apparatus, and medium for identifying regional industrial talent skills gaps. It acquires industry information corresponding to at least one industry in a target region, including occupational demand information and training supply information. The occupational demand information and the training supply information are segmented into words, and the segmentation results are filtered to obtain effective supply and demand vocabulary, including effective demand vocabulary and effective supply vocabulary. The effective supply and demand vocabulary is clustered to obtain a supply and demand skill vocabulary library composed of skill words corresponding to each cluster. Based on the effective demand vocabulary and the effective supply vocabulary, demand skill words and supply skill words are determined from the supply and demand skill vocabulary library. By comparing the demand skill words and the supply skill words, a target identification result for the talent skills gap in at least one industry in the target region is generated.

[0045] In this way, when identifying regional industrial skills gaps, the occupational demand information and training supply information of the target region's industries are used. This allows for a comprehensive and detailed analysis of the current state of industrial talent from both demand and supply perspectives. It considers both the skills that industries actually expect talent to possess and the skills that the talent training system can provide, thus avoiding the limitations caused by considering only one dimension. This ensures the comprehensiveness and accuracy of skills gap identification and meets the actual needs of industrial development. Furthermore, by segmenting, filtering, and clustering occupational demand and training supply information to obtain a supply and demand skills vocabulary, scattered corpus information is transformed into corresponding skills terms. This achieves effective integration of complex industrial information, presents the distribution of industrial skills more systematically, and lays a solid foundation for locating gaps.

[0046] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this disclosure.

[0047] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0049] Figure 1 A schematic diagram of a regional industrial talent skills gap identification device provided in an embodiment of this disclosure is shown;

[0050] Figure 2 This illustration shows a process for determining a supply and demand skills vocabulary provided by an embodiment of the present disclosure;

[0051] Figure 3 This illustration shows a schematic diagram of a comparison process between demand skill terms and supply skill terms provided by an embodiment of the present disclosure;

[0052] Figure 4 A flowchart illustrating a method for identifying regional industrial talent skills gaps according to an embodiment of this disclosure is shown;

[0053] Figure 5 A schematic diagram of a device for identifying regional industrial talent skills gaps provided in an embodiment of this disclosure is shown. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0056] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0057] Research has found that when identifying the skills of industry talents, the dimensions considered are mostly relatively singular. For example, analysis and identification are conducted only from the perspective of demand or only from the perspective of supply. This results in many limitations in the identification results, making it difficult to meet the requirements of comprehensiveness and accuracy. It may even lead to the identification results lagging behind the actual needs of industrial development, thus making it difficult to adapt to the diverse needs of industrial development.

[0058] Based on the above research, this disclosure provides a device for identifying regional industrial talent skills gaps. When identifying regional industrial talent skills gaps, it uses occupational demand information and training supply information of the target region's industries. This allows for a comprehensive and detailed analysis of the current state of industrial talent from both demand and supply perspectives. It considers both the skills that industries actually expect talent to possess and the skills that the talent training system can provide, thus avoiding the limitations caused by considering only one dimension. This ensures the comprehensiveness and accuracy of talent skills gap identification and meets the actual needs of industrial development.

[0059] The following description, in conjunction with the accompanying drawings, illustrates an embodiment of a device for identifying regional industrial talent skills gaps provided in this disclosure.

[0060] See Figure 1 The diagram shown is a schematic representation of a regional industrial skills gap identification device 100 provided in an embodiment of this disclosure, comprising:

[0061] The system includes a processor 110, a memory 120, and a display 130, which are connected via a bus 140. The memory 120 stores computer instructions, and the display shows the processing results of the processor, including target recognition results generated by the processor.

[0062] The memory 120 includes a main memory 121 and an external memory 122. The main memory 121, also known as internal memory, is used to temporarily store the computational data in the processor 110 and the data exchanged with the external memory 122, such as the hard disk. The processor 110 exchanges data with the external memory 122 through the main memory 121.

[0063] The memory 120 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0064] Processor 110 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0065] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the regional industry talent skills gap identification device 100. In other embodiments of this application, the regional industry talent skills gap identification device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0066] In this embodiment, the memory 120 is specifically used to store computer instructions for executing the scheme of this application, and the processor 110 controls the execution. That is, when the regional industrial talent skills gap identification device 100 is running, the processor 110 communicates with the memory 120 through the bus 140, so that the processor 110 reads the computer instructions stored in the memory 120 to perform the following operations:

[0067] Obtain industry information corresponding to at least one industry in the target region, wherein the industry information includes occupational demand information and training supply information;

[0068] The occupational demand information and the training supply information are segmented into words respectively, and the segmentation results are filtered to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary.

[0069] The effective supply and demand terms are clustered to obtain a supply and demand skill term library composed of the skill terms corresponding to each cluster.

[0070] Based on the effective vocabulary of demand and the effective vocabulary of supply, demand skill words and supply skill words are determined from the supply and demand skill vocabulary library;

[0071] By comparing the demand skill terms and the supply skill terms, a target identification result for the talent skills gap in at least one industry in the target region is generated.

[0072] The above scheme will now be described in conjunction with specific implementation methods.

[0073] Here, the target area can be at the city level, national level, etc.

[0074] The occupational demand information can be obtained from the demand side associated with the at least one industry, and the training supply information can be obtained from the supply side associated with the at least one industry.

[0075] It is understood that the demand side and the supply side have a supply-demand relationship. For example, the demand side may be companies providing jobs, and the supply side may be schools training talent. Alternatively, the demand side may be departments planning industries, and the supply side may be departments promoting industrial development.

[0076] Specifically, taking companies that provide job openings as an example, when obtaining job demand information, they can crawl corpus information related to the demand side from recruitment websites, and filter the crawled corpus information by combining it with target keywords to obtain job requirements information under various industry attributes, which can be used as job demand information.

[0077] Optionally, you can select a filter category on the recruitment website that is relevant to the target region and industry, such as company industry, job type, etc., and then search for recruitment information in all occupational categories under the filter category. Perform multiple filters according to the hierarchy of target region - company industry and job type - job name - job posting - job description - job requirements to obtain corpus information.

[0078] Once the job requirements information is obtained, considering that it covers a variety of industries and positions, the job requirements information can be simplified and the result of the simplified process can be used as the job demand information.

[0079] Specifically, taking schools that cultivate talent as the supply side as an example, when obtaining training supply information, they can collect corpus information related to the professional talent training programs of various colleges related to the industry in universities within the target region, including professional introductions and training objectives, as training supply information.

[0080] If a comprehensive industrial analysis and identification of the target area is required in the future, the industrial information of all industries in the target area can be obtained here. If a targeted analysis and identification of one or several industries in the target area is required in the future, the industrial information of the corresponding industries in the target area can be obtained here.

[0081] In some possible implementations, the process of segmenting the occupational demand information and the training supply information into words, and then filtering the segmentation results to obtain effective supply and demand vocabulary, includes:

[0082] The occupational demand information and the training supply information are respectively processed by word segmentation to obtain multiple occupational demand word segments and multiple training supply word segments;

[0083] The target neural network is used to process each of the occupational demand words and each of the training supply words to obtain the demand word vectors corresponding to each of the occupational demand words and the supply word vectors corresponding to each of the training supply words.

[0084] Based on preset skill word vectors, effective words for demand are selected from the multiple occupational demand word segments, and effective words for supply are selected from the multiple training supply word segments; the preset skill word vectors are obtained by processing preset skill words through the target neural network;

[0085] The effective demand terms and effective supply terms are summarized and deduplicated to obtain effective supply and demand terms.

[0086] In the above steps, when performing word segmentation on the occupational demand information and the training supply information, word segmentation algorithms can be used, such as the jieba word segmentation algorithm, to obtain multiple occupational demand words and multiple training supply words.

[0087] The target neural network can perform word embedding on the input word segments and output word vectors, which represent the position of the corresponding word segments in the semantic space. It can be understood that if two word vectors are close in distance, the semantics corresponding to those two word vectors are generally similar.

[0088] Optionally, the target neural network can also filter out English words and symbols in the input word segmentation to obtain Chinese output results.

[0089] Here, multiple preset skill words are pre-set. These preset skill words are labor skills words with existing research foundations. The target neural network processes each preset skill word to obtain a preset skill word vector corresponding to each preset skill word.

[0090] The target neural network can be a pre-trained BERT model.

[0091] Using preset skill word vectors as the filtering criteria, effective words for demand are selected from the multiple occupational demand word segments, and effective words for supply are selected from the multiple training supply word segments.

[0092] In practical applications, there are often overlaps between the effective demand terms and the effective supply terms. By summarizing and deduplicating the effective demand terms and the effective supply terms, the effective supply and demand terms can be obtained.

[0093] In this way, by comprehensively utilizing word segmentation technology, neural network algorithms, and vector comparison methods, effective vocabulary for supply and demand is obtained. This fully leverages the advantages of different technologies, complements each other to compensate for the limitations of a single technology, and allows for large-scale data processing, making the entire skills gap identification process more reliable and effectively improving the credibility of the identification results.

[0094] In some possible implementations, the step of selecting effective demand words from the plurality of occupational demand word segments and selecting effective supply words from the plurality of training supply word segments based on preset skill word vectors includes:

[0095] Determine the similarity between the preset skill word vector and each of the demand word vectors, and the similarity between the preset skill word vector and each of the supply word vectors;

[0096] The occupational demand word segments corresponding to the demand word vectors with similarity greater than or equal to the target threshold are determined as the effective demand words, and the training supply word segments corresponding to the supply word vectors with similarity greater than or equal to the target threshold are determined as the effective supply words.

[0097] Here, when determining similarity, we can specifically determine the cosine triangular similarity. The closer the semantics of two word vectors, the closer their positions in the semantic space are generally. The closer the semantics of two word vectors, the greater their cosine triangular similarity. Therefore, we can use the cosine triangular similarity to estimate the semantic similarity between two words, ensuring the accuracy of word vector matching.

[0098] The specific value of the target threshold depends on the actual screening needs and is not specifically limited here.

[0099] In this embodiment, the same threshold is used when screening effective words for demand and effective words for supply, which is the target threshold. This ensures that demand-side and supply-side word screening are carried out according to a unified standard, thereby improving the reliability of word screening.

[0100] Optionally, when determining the occupational demand words corresponding to the demand word vectors with similarity greater than or equal to the target threshold, these occupational demand words can be considered as occupational demand words containing skill descriptions. Furthermore, the occupational demand words corresponding to the demand word vectors with similarity greater than or equal to the target threshold can be manually screened, and the manually screened occupational demand words are determined as the effective demand vocabulary. Similarly, when determining the training supply words corresponding to the supply word vectors with similarity greater than or equal to the target threshold, these training supply words can be considered as training supply words containing skill descriptions. For example, the word "professional introduction" will not be screened and retained. Furthermore, the training supply words corresponding to the supply word vectors with similarity greater than or equal to the target threshold can be manually screened, and the manually screened training supply words are determined as the effective supply vocabulary. This effectively improves the accuracy of effective demand vocabulary and effective supply vocabulary.

[0101] In this way, by comparing the preset skill word vectors with the demand word vectors and supply word vectors respectively, we can effectively filter out words closely related to skills, avoid interference from words unrelated to skills, improve the accuracy and reliability of word selection, and ensure the effectiveness of subsequent analysis.

[0102] In some possible implementations, the clustering of the effective supply and demand vocabulary to obtain a supply and demand skill vocabulary library composed of skill words corresponding to each cluster includes:

[0103] Clustering is performed on the effective supply and demand terms to obtain at least one cluster;

[0104] For each cluster, skill words matching the cluster are generated based on the effective supply and demand words included in the cluster.

[0105] A supply and demand skill terminology database is formed by combining the various skill terms.

[0106] Here, when performing clustering processing on the effective supply and demand vocabulary, the supply and demand word vectors corresponding to the effective supply and demand vocabulary can be determined, and the supply and demand word vectors can be clustered using a clustering algorithm, such as the k-means algorithm, to obtain at least one cluster.

[0107] In this way, word vectors can better reflect the relationships between words than words, making the clustering results more accurate.

[0108] Similarly, the supply and demand effective vocabulary can be processed by the target neural network to obtain the supply and demand word vectors.

[0109] As can be seen from the foregoing, word vectors are the result of processing by the target neural network. In practical applications, word vectors are often high-dimensional. Taking the target neural network as the BERT model as an example, the word vectors output by the BERT model are PyTorch tensors, which can generally reach 768 dimensions.

[0110] In high-dimensional space, data points are generally sparse and difficult to form dense clusters. Most clustering algorithms rely on dense regions that often fail in high-dimensional space. Therefore, high-dimensional word vectors are often difficult to cluster.

[0111] In some possible implementations, the supply and demand word vectors can be first reduced in dimensionality, for example, to 30 dimensions, and then clustered.

[0112] In some other possible implementations, the supply and demand word vectors can be converted into NumPy arrays, the NumPy arrays can be reduced in dimensionality, and the reduced NumPy arrays can be clustered.

[0113] Here, NumPy arrays come with a built-in Principal Component Analysis (PCA) function. By directly using the PCA function built into NumPy arrays, dimensionality reduction of NumPy arrays can be achieved conveniently and quickly, improving data processing efficiency.

[0114] When performing clustering, you can first set the target range for the number of clusters, and then perform clustering according to that range. Alternatively, you can use the silhouette score method to determine the target range for the number of clusters, for example, between 100 and 500.

[0115] After obtaining the clusters, for each cluster, skill words matching the cluster are generated based on the effective supply and demand vocabulary included in the cluster. For example, the effective supply and demand vocabulary included in the cluster can be imported into a large language model, and the large language model can summarize and name the cluster based on the effective supply and demand vocabulary included in the cluster to generate skill words matching the cluster.

[0116] The set of each of the aforementioned skill terms is used as the supply and demand skill term library.

[0117] In this way, effective supply and demand terms are clustered and integrated into multiple clusters, with each cluster corresponding to a skill term, forming a structured supply and demand skill term library. This systematic integration method allows the originally scattered terms to be presented in a centralized manner, enabling the generation of skill terms that match demand and supply more accurately.

[0118] To more clearly illustrate the process of determining the supply and demand skills vocabulary, please refer to [link / reference]. Figure 2 This is a schematic diagram illustrating a process for determining a supply and demand skills vocabulary database according to an embodiment of this disclosure. Figure 2 As shown, the occupational demand information and the training supply information are segmented into multiple occupational demand words and multiple training supply words, respectively. Corresponding word vectors are obtained through target neural network processing. Based on preset word vectors, effective demand words and effective supply words are selected. These effective demand words and effective supply words are then summarized and deduplicated to obtain effective supply and demand words. These effective supply and demand words are then clustered to obtain at least one cluster. Each cluster is summarized and named to generate skill words, forming a supply and demand skill word library. The specific steps are described in the aforementioned embodiment and will not be repeated here.

[0119] In some possible implementations, determining demand skill terms and supply skill terms from the supply and demand skill terminology database based on the effective demand terminology and the effective supply terminology includes:

[0120] For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term.

[0121] Thus, if the cluster corresponding to the skill term includes effective supply and demand terms that match the effective demand terms but not the effective supply terms, the skill term is determined to be a demand skill term; if the cluster corresponding to the skill term includes effective supply and demand terms that match the effective demand terms but not the effective supply terms, the skill term is determined to be a supply skill term; if the cluster corresponding to the skill term includes effective supply and demand terms that match both the effective demand terms and the effective supply terms, the skill term is determined to be both a demand skill term and a supply skill term.

[0122] In this way, by matching the effective supply and demand terms included in the cluster with the previously obtained effective demand terms and effective supply terms, the type of each skill term can be determined, which helps to accurately match demand skills and supply skills, improve the accuracy and efficiency of talent skill matching, and provide a more accurate basis for the subsequent identification of talent skill gaps.

[0123] In some possible implementations, the step of generating a target identification result for the talent skills gap in at least one industry in the target region by comparing the demand skills terms and the supply skills terms includes:

[0124] The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word.

[0125] Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

[0126] For a better understanding of this embodiment, please refer to the following: Figure 3 This is a schematic diagram illustrating a comparison process between demand skill terms and supply skill terms provided in an embodiment of this disclosure. Figure 3 As shown in the diagram, circle A represents the demand skill term, and circle B represents the supply skill term. Statistical software is used to compare demand skill term A and supply skill term B. For example, Stata software is used to compare demand skill term A and supply skill term B to determine the intersection of demand skill term A and supply skill term B as the first skill term a, the difference between demand skill term A and supply skill term B as the second skill term b, and the difference between supply skill term B and demand skill term A as the third skill term c.

[0127] Combination Figure 3It can be understood that the first skill term 'a' is the same part of the demand skill term 'A' and the supply skill term 'B', and is regarded as the skill in which industrial development has achieved supply and demand balance at the labor level; the second skill term 'b' is the part included by the demand skill term 'A' but not by the supply skill term 'B', that is, the skill that is in short supply, belonging to the skill that is needed on the demand side but not supplied on the supply side; the third skill term 'c' is the part included by the supply skill term 'B' but not by the demand skill term 'A', that is, the skill that is in oversupply, belonging to the skill that is supplied on the supply side but not needed on the demand side.

[0128] Thus, based on the first skill term, the second skill term, and the third skill term, a target identification result for the talent skills gap in at least one industry in the target region is generated. The target identification result is used to indicate that the first skill term represents the skills for which industrial development has achieved supply and demand balance at the labor level, the second skill term represents the skills that are needed on the demand side but not supplied on the supply side, and the third skill term represents the skills that are supplied on the supply side but not needed on the demand side.

[0129] In this way, by comparing demand and supply skill terms, different parts such as intersection and difference are determined, effectively identifying the matching and differences between demand and supply of talent skills, comprehensively presenting the skill supply and demand situation from both the supply and demand sides, and generating clear identification results, providing a strong basis for regional industrial talent development.

[0130] In some possible implementations, the processor is also used to perform:

[0131] Based on the target identification results, a development strategy for the at least one industry in the target area is generated. The development strategy is used to indicate adjustment information for various skills belonging to the at least one industry. The development strategy is displayed through the display.

[0132] Receive the editing results of the development strategy being displayed, and publish the editing results.

[0133] Specifically, the development strategy is used to indicate the maintenance of industrial development and talent cultivation corresponding to the first skill term, the addition of talent cultivation corresponding to the second skill term, and the reduction, cancellation, or conversion of talent cultivation corresponding to the third skill term.

[0134] After the processor generates the development strategy, the display shows the development strategy. Users can edit the displayed development strategy according to actual development needs. The processor can also receive the editing results of the displayed development strategy and publish the editing results.

[0135] In this way, by generating and displaying development strategies based on the results of target identification, clear guidance is provided for the adjustment of the direction of subsequent skill-related industrial development and talent training. At the same time, the development strategies can be edited, so that the demand or supply side can flexibly optimize the strategies according to its own needs and environmental changes, thereby enhancing the pertinence and practicality of the development strategies and thus better promoting the matching and connection between industrial development and talent training.

[0136] In some possible implementations, the processor is also used to perform:

[0137] For the target region, based on the target identification results of the talent skills gap in various industries in the target region, the talent matching situation of each industry is determined, so that industries with poor talent training matching can be vigorously developed; it can also determine the industry demand for each skill and give priority to training skills with high industry demand but low supply.

[0138] In some other possible implementations, the processor is also used to perform:

[0139] For multiple target regions, based on the first skill term, the second skill term, and the third skill term of each target region, the talent skill gap ratio of each target region is determined, and the development strategy of each target region is determined according to the talent skill gap ratio of each target region.

[0140] In some other possible implementations, the processor is also used to perform:

[0141] For the target region, based on the second skill words of the target region and the third skill words of other target regions, a matching value between the target region and the other target regions is determined. If the matching value is greater than or equal to the matching threshold, talent can be introduced from the other target regions to the target region according to the second skill words of the target region. If the matching value is less than the matching threshold, the target region can carry out talent training on its own.

[0142] In some other possible implementations, the processor is also configured to perform:

[0143] Obtain the first, second, and third skill terms of the target region in its historical development stages. Based on the first, second, and third skill terms of the historical development stages and the first, second, and third skill terms of the current development stage, determine the supply and demand development change index of the target region. Based on the supply and demand development change index of each target region, determine the development strategy for each target region.

[0144] The regional industry talent skills gap identification device provided in this embodiment utilizes occupational demand information and training supply information of the target region's industries when identifying regional industry talent skills gaps. This allows for a comprehensive and detailed analysis of the current state of industry talent from both demand and supply perspectives. It considers both the skills that industries actually expect talent to possess and the skills that the talent training system can provide, thus avoiding the limitations of considering only one dimension. This ensures the comprehensiveness and accuracy of talent skills gap identification and meets the actual needs of industrial development. Furthermore, by segmenting, filtering, and clustering occupational demand information and training supply information to obtain a supply and demand skills lexicon, it transforms scattered corpus information into corresponding skills terms, effectively integrating complex industry information and more systematically presenting the distribution of industry skills, laying a solid foundation for locating gaps.

[0145] This disclosure also provides a method for identifying regional industrial talent skills gaps. The method described below will be detailed. The execution entity of this method is generally the aforementioned regional industrial talent skills gap identification device. This device can be a terminal device or other processing device. The terminal device can be a mobile device, terminal, or computing device, etc. Other processing devices can include devices with processors and memory, and are not limited thereto.

[0146] See Figure 4 The diagram shown is a flowchart of a method for identifying regional industrial talent skills gaps according to an embodiment of this disclosure. Figure 4 As shown in the figure, the method for identifying regional industrial talent skills gaps provided in this embodiment of the disclosure includes steps S401 to S405, wherein:

[0147] S401: Obtain industry information corresponding to at least one industry in the target area, wherein the industry information includes occupational demand information and training supply information.

[0148] S402: The occupational demand information and the training supply information are processed by word segmentation, and the word segmentation results are filtered to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary.

[0149] In some possible implementations, the process of segmenting the occupational demand information and the training supply information into words, and then filtering the segmentation results to obtain effective supply and demand vocabulary, includes:

[0150] The occupational demand information and the training supply information are respectively processed by word segmentation to obtain multiple occupational demand word segments and multiple training supply word segments;

[0151] The target neural network is used to process each of the occupational demand words and each of the training supply words to obtain the demand word vectors corresponding to each of the occupational demand words and the supply word vectors corresponding to each of the training supply words.

[0152] Based on preset skill word vectors, effective words for demand are selected from the multiple occupational demand word segments, and effective words for supply are selected from the multiple training supply word segments; the preset skill word vectors are obtained by processing preset skill words through the target neural network;

[0153] The effective demand terms and effective supply terms are summarized and deduplicated to obtain effective supply and demand terms.

[0154] In some possible implementations, the step of selecting effective demand words from the multiple occupational demand word segments and selecting effective supply words from the multiple training supply word segments based on preset skill word vectors includes:

[0155] Determine the similarity between the preset skill word vector and each of the demand word vectors, and the similarity between the preset skill word vector and each of the supply word vectors;

[0156] The occupational demand word segments corresponding to the demand word vectors with similarity greater than or equal to the target threshold are determined as the effective demand words, and the training supply word segments corresponding to the supply word vectors with similarity greater than or equal to the target threshold are determined as the effective supply words.

[0157] S403: Cluster the effective supply and demand terms to obtain a supply and demand skill term library composed of the skill terms corresponding to each cluster.

[0158] In some possible implementations, the clustering of the effective supply and demand vocabulary to obtain a supply and demand skill vocabulary library composed of skill words corresponding to each cluster includes:

[0159] Clustering is performed on the effective supply and demand terms to obtain at least one cluster;

[0160] For each cluster, skill words matching the cluster are generated based on the effective supply and demand words included in the cluster.

[0161] A supply and demand skill terminology database is formed by combining the various skill terms.

[0162] S404: Based on the effective demand vocabulary and the effective supply vocabulary, determine the demand skill words and supply skill words from the supply and demand skill vocabulary library.

[0163] In some possible implementations, determining demand skill terms and supply skill terms from the supply and demand skill terminology database based on the effective demand vocabulary and the effective supply vocabulary includes:

[0164] For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term.

[0165] S405: By comparing the demand skill terms and the supply skill terms, a target identification result for the talent skills gap in at least one industry in the target region is generated.

[0166] In some possible implementations, the step of generating a target identification result for the talent skills gap in at least one industry in the target region by comparing the demand skills terms and the supply skills terms includes:

[0167] The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word.

[0168] Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

[0169] In some possible implementations, the method further includes:

[0170] Based on the target identification results, a development strategy for the at least one industry in the target area is generated, and the development strategy is displayed, wherein the development strategy is used to indicate adjustment information for various skills belonging to the at least one industry;

[0171] Receive the editing results of the development strategy being displayed, and publish the editing results.

[0172] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0173] It should be noted that the method provided in this embodiment of the invention can achieve all the steps implemented in the above device embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the device embodiment will not be described in detail here.

[0174] Based on the same inventive concept, this disclosure also provides a device for identifying regional industrial talent skills gaps, which corresponds to the method for identifying regional industrial talent skills gaps. Since the principle of the device for identifying regional industrial talent skills gaps in this disclosure is similar to the method for identifying regional industrial talent skills gaps described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0175] Please see Figure 5 , Figure 5 This is a schematic diagram of a device for identifying regional industrial talent skills gaps, provided as an embodiment of this disclosure. Figure 5 As shown in the figure, the regional industrial talent skills gap identification device 500 provided in this embodiment includes:

[0176] The data acquisition module 510 is used to acquire industry information corresponding to at least one industry in the target area, the industry information including occupational demand information and training supply information;

[0177] The filtering module 520 is used to perform word segmentation on the occupational demand information and the training supply information respectively, and to filter the word segmentation results to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary.

[0178] Clustering module 530 is used to perform clustering processing on the supply and demand effective vocabulary to obtain a supply and demand skill vocabulary library composed of skill words corresponding to each cluster.

[0179] The classification module 540 is used to determine demand skill words and supply skill words from the supply and demand skill word library based on the demand effective words and the supply effective words;

[0180] The comparison module 550 is used to generate a target identification result of talent skills gap for at least one industry in the target region by comparing the demand skills terms and the supply skills terms.

[0181] In one optional implementation, the filtering module 520 is specifically used for:

[0182] The occupational demand information and the training supply information are respectively processed by word segmentation to obtain multiple occupational demand word segments and multiple training supply word segments;

[0183] The target neural network is used to process each of the occupational demand words and each of the training supply words to obtain the demand word vectors corresponding to each of the occupational demand words and the supply word vectors corresponding to each of the training supply words.

[0184] Based on preset skill word vectors, effective words for demand are selected from the multiple occupational demand word segments, and effective words for supply are selected from the multiple training supply word segments; the preset skill word vectors are obtained by processing preset skill words through the target neural network;

[0185] The effective demand terms and effective supply terms are summarized and deduplicated to obtain effective supply and demand terms.

[0186] In an optional implementation, when the filtering module 520 is used to filter effective demand words from the plurality of occupational demand word segments and effective supply words from the plurality of training supply word segments based on preset skill word vectors, it is specifically used for:

[0187] Determine the similarity between the preset skill word vector and each of the demand word vectors, and the similarity between the preset skill word vector and each of the supply word vectors;

[0188] The occupational demand word segments corresponding to the demand word vectors with similarity greater than or equal to the target threshold are determined as the effective demand words, and the training supply word segments corresponding to the supply word vectors with similarity greater than or equal to the target threshold are determined as the effective supply words.

[0189] In one optional implementation, the clustering module 530 is specifically used for:

[0190] Clustering is performed on the effective supply and demand terms to obtain at least one cluster;

[0191] For each cluster, skill words matching the cluster are generated based on the effective supply and demand words included in the cluster.

[0192] A supply and demand skill terminology database is formed by combining the various skill terms.

[0193] In one optional implementation, the classification module 540 is specifically used for:

[0194] For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term.

[0195] In one optional implementation, the comparison module 550 is specifically used for:

[0196] The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word.

[0197] Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

[0198] In an optional implementation, the comparison module 550 is further configured to:

[0199] Based on the target identification results, a development strategy for the at least one industry in the target area is generated, and the development strategy is displayed, wherein the development strategy is used to indicate adjustment information for various skills belonging to the at least one industry;

[0200] Receive the editing results of the development strategy being displayed, and publish the editing results.

[0201] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0202] It should be noted that the apparatus provided in this disclosure can implement all the steps implemented in the above-mentioned device embodiments and can achieve the same technical effects. Therefore, the parts and beneficial effects that are the same as those in the device embodiments and the device embodiments will not be described in detail here.

[0203] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the method for identifying regional industry talent skills gaps described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0204] This disclosure also provides a computer program product, which, when invoked by a computer, causes the computer to execute the steps of the method for identifying regional industrial talent skills gaps described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0205] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0206] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and apparatuses described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0207] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0208] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0209] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0210] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A device for identifying regional industrial talent skills gaps, characterized in that, The system includes a processor, a memory, and a display, which are connected via a bus. The processor stores computer instructions, and the display shows the processing results generated by the processor, including target recognition results. The processor reads the computer instructions stored in the memory to perform the following operations: Obtain industry information corresponding to at least one industry in the target region, wherein the industry information includes occupational demand information and training supply information; The occupational demand information and the training supply information are processed by word segmentation, and the word segmentation results are filtered to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary. The effective supply and demand terms are clustered to obtain a supply and demand skill term library composed of the skill terms corresponding to each cluster. Based on the effective vocabulary of demand and the effective vocabulary of supply, demand skill words and supply skill words are determined from the supply and demand skill vocabulary library; By comparing the demand skill terms and the supply skill terms, a target identification result for the talent skill gap in at least one industry in the target region is generated. The step of determining demand skill terms and supply skill terms from the supply and demand skill terminology database based on the effective demand terminology and the effective supply terminology includes: For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term. The process of comparing the demand skill terms and the supply skill terms to generate target identification results for talent skill gaps in at least one industry in the target region includes: The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word. Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

2. The device according to claim 1, characterized in that, The process involves segmenting the occupational demand information and the training supply information into words, and then filtering the segmentation results to obtain effective supply and demand vocabulary, including: The occupational demand information and the training supply information are respectively processed by word segmentation to obtain multiple occupational demand word segments and multiple training supply word segments; The target neural network is used to process each of the occupational demand words and each of the training supply words to obtain the demand word vectors corresponding to each of the occupational demand words and the supply word vectors corresponding to each of the training supply words. Based on preset skill word vectors, effective words for demand are selected from the multiple occupational demand word segments, and effective words for supply are selected from the multiple training supply word segments; the preset skill word vectors are obtained by processing preset skill words through the target neural network; The effective demand terms and effective supply terms are summarized and deduplicated to obtain effective supply and demand terms.

3. The device according to claim 2, characterized in that, The process of selecting effective words for demand from multiple occupational demand word segments and selecting effective words for supply from multiple training supply word segments based on preset skill word vectors includes: Determine the similarity between the preset skill word vector and each of the demand word vectors, and the similarity between the preset skill word vector and each of the supply word vectors; The occupational demand word segments corresponding to the demand word vectors with similarity greater than or equal to the target threshold are determined as the effective demand words, and the training supply word segments corresponding to the supply word vectors with similarity greater than or equal to the target threshold are determined as the effective supply words.

4. The device according to claim 1, characterized in that, The clustering process of the effective supply and demand vocabulary yields a supply and demand skill vocabulary library composed of skill words corresponding to each cluster, including: Clustering is performed on the effective supply and demand terms to obtain at least one cluster; For each cluster, skill words matching the cluster are generated based on the supply and demand effective words included in the cluster. A supply and demand skill terminology database is formed by combining the various skill terms.

5. The device according to claim 1, characterized in that, The processor is also used to perform: Based on the target identification results, a development strategy for the at least one industry in the target area is generated. The development strategy is used to indicate adjustment information for various skills belonging to the at least one industry. The development strategy is displayed through the display. Receive the editing results of the development strategy being displayed, and publish the editing results.

6. A method for identifying regional industrial talent skills gaps, characterized in that, The method includes: Obtain industry information corresponding to at least one industry in the target region, wherein the industry information includes occupational demand information and training supply information; The occupational demand information and the training supply information are processed by word segmentation, and the word segmentation results are filtered to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary. The effective supply and demand terms are clustered to obtain a supply and demand skill term library composed of the skill terms corresponding to each cluster. Based on the effective vocabulary of demand and the effective vocabulary of supply, demand skill words and supply skill words are determined from the supply and demand skill vocabulary library; By comparing the demand skill terms and the supply skill terms, a target identification result for the talent skill gap in at least one industry in the target region is generated. The step of determining demand skill terms and supply skill terms from the supply and demand skill terminology database based on the effective demand terminology and the effective supply terminology includes: For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term. The process of comparing the demand skill terms and the supply skill terms to generate target identification results for talent skill gaps in at least one industry in the target region includes: The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word. Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

7. A device for identifying regional industrial talent skills gaps, characterized in that, The device includes: The data acquisition module is used to acquire industry information corresponding to at least one industry in the target area, the industry information including occupational demand information and training supply information; The filtering module is used to perform word segmentation on the occupational demand information and the training supply information respectively, and to filter the word segmentation results to obtain effective supply and demand vocabulary, which includes effective demand vocabulary and effective supply vocabulary. The clustering module is used to cluster the effective supply and demand vocabulary to obtain a supply and demand skill vocabulary library composed of the skill words corresponding to each cluster. The classification module is used to determine demand skill terms and supply skill terms from the supply and demand skill terminology library based on the effective demand terms and the effective supply terms. The comparison module is used to generate a target identification result of talent skills gap for at least one industry in the target region by comparing the demand skills terms and the supply skills terms. The classification module is specifically used for: For each skill term in the supply and demand skill terminology database, the effective supply and demand terms included in the cluster corresponding to the skill term are matched with the effective demand terms and the effective supply terms, respectively, to determine the type of effective supply and demand terms included in the cluster corresponding to the skill term; the type corresponding to the skill term is a demand skill term and / or a supply skill term. The comparison module is specifically used for: The demand skill words and the supply skill words are compared to determine the first skill word, the second skill word, and the third skill word. The first skill word is the intersection between the demand skill word and the supply skill word, the second skill word is the difference between the demand skill word and the supply skill word, and the third skill word is the difference between the supply skill word and the demand skill word. Based on the first skill term, the second skill term, and the third skill term, a target identification result for talent skills gaps in at least one industry in the target region is generated.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for identifying regional industrial talent skills gaps as described in claim 6.

Images