Enterprise culture data processing method and system based on time sequence and blockchain

Abstract

This invention provides a method and system for processing corporate culture data based on time series and blockchain. It offers a method and system for evaluating the effectiveness of corporate culture activities that can dynamically analyze data trends, intelligently optimize, and ensure the authenticity and reliability of data, thereby improving the accuracy and reliability of the evaluation and providing more valuable references for corporate decision-making. Through time series analysis, dynamic indicator selection and optimization are achieved, quickly adapting to changes in corporate culture activities at different stages and under different environments. This invention does not rely on manually setting complex rules, effectively uncovering potential information behind the data and discovering influencing factors that are difficult to detect using traditional methods. The introduction of blockchain technology for data storage and verification fundamentally solves the problem of data tampering, ensuring the authenticity and integrity of the evaluation data and providing a reliable basis for corporate decision-making. Through effectiveness prediction and feedback optimization, it helps enterprises continuously optimize cultural activities and improve the level of corporate culture construction.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for processing corporate culture data based on time series and blockchain. Background Technology

[0002] Corporate culture, accumulated through long-term production and operation practices, encompasses core elements such as corporate vision, mission, values, behavioral norms, and team atmosphere. It serves as a unique spiritual symbol and driving force for a company's development. It permeates every aspect of corporate operations, subtly influencing employees' mindsets and work attitudes. It unites team strength, standardizes business practices, and shapes brand image, ultimately creating a core advantage that is difficult to replicate in market competition. In the context of continuously expanding corporate scale and increasingly complex internal and external environments, clearly understanding, effectively disseminating, and continuously optimizing corporate culture has become crucial for improving management efficiency and ensuring the implementation of strategies. By transforming abstract corporate culture into a perceptible and analyzable data format, companies can more accurately grasp the direction of culture building, ensuring that culture aligns with business strategy.

[0003] Existing methods for processing corporate culture data mainly rely on manual collection and statistical analysis, supplemented by basic information technology tools. This approach suffers from problems such as low efficiency, strong subjectivity, and narrow data coverage, making it difficult to comprehensively and objectively reflect the true state of corporate culture and providing accurate data support for optimizing corporate culture development. Summary of the Invention

[0004] This invention provides a method and system for processing corporate culture data based on time series and blockchain.

[0005] A first aspect of this invention provides a method for processing corporate culture data based on time series and blockchain, comprising:

[0006] The system retrieves enterprise business information from the database at preset time intervals and extracts corresponding corporate culture indicators from the enterprise business information according to preset dimensions.

[0007] The index of each corporate culture indicator is obtained by multi-dimensional analysis of the information form, information level and information volume of corporate culture indicators in the database.

[0008] The extracted corporate culture indicators are compared with historical corporate culture data to identify the same first indicator and the different second indicator, and a differentiated indicator set is generated based on the first and second indicators.

[0009] The large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data.

[0010] Optionally, in one possible implementation of the first aspect, enterprise operating information in the database is acquired at preset time intervals, and corresponding enterprise culture indicators are extracted from the enterprise operating information according to preset dimensions, including:

[0011] Collect multi-source data related to corporate culture activities, including but not limited to employee behavior data, business operation data, and social media data;

[0012] By preprocessing the collected heterogeneous data through data cleaning and standardization, and transforming it into a unified format, corporate culture indicators are obtained.

[0013] Optionally, in one possible implementation of the first aspect, the original data corresponding to the collected enterprise operation information and the key data in the evaluation process are stored using blockchain technology.

[0014] By leveraging the distributed ledger, hash encryption, and consensus mechanisms of blockchain, the immutability and traceability of data can be ensured.

[0015] During the evaluation result verification phase, the authenticity of the data is verified through a blockchain network to prevent malicious data tampering and improve the credibility of the evaluation results.

[0016] Optionally, in one possible implementation of the first aspect, enterprise operating information in the database is retrieved at preset time intervals, and corresponding enterprise culture indicators are extracted from the enterprise operating information according to preset dimensions, including:

[0017] In response to any sending end transmitting information to any receiving end, the transmission information corresponding to the information transmission is uploaded to the database;

[0018] The transmitted information is identified, and the identification result indicates that the information format of the transmitted information is a document or a shortcut. Based on the semantic recognition model, the transmitted information is semantically compared with a preset business semantic database, wherein the preset business semantic database includes various preset dimensions.

[0019] If the response comparison result shows that the transmitted information matches any preset dimension, the transmitted information is identified as enterprise business information.

[0020] The system retrieves enterprise business information from the database at preset time intervals and extracts corresponding corporate culture indicators from the enterprise business information according to preset dimensions.

[0021] Optionally, in one possible implementation of the first aspect, the method further includes:

[0022] The response recognition result is that the corresponding transmitted information includes any image information in the form of an image, and image recognition is performed on the image information based on the image recognition model;

[0023] If the response identification result indicates the existence of an image semantic corresponding to the image information, the image information is identified as image information, and the information format of the current state of the transmission information corresponding to the image information is identified as a shortcut format.

[0024] If the response identification result is that there is no image semantic corresponding to the image information, the transmission time of the transmission information corresponding to the image information is obtained;

[0025] Using the transmission time as the median and a time interval determined based on a preset time threshold, the image information of each enterprise's business information corresponding to the transmission time falling within the time interval is determined as each comparison information.

[0026] Based on the image recognition model, the comparison pixel values ​​of each comparison pixel point that makes up each comparison information and the image pixel values ​​of each image pixel point that makes up the image information are determined;

[0027] Determine the percentage of identical pixels in each comparison pixel value corresponding to the same comparison information and each image pixel value. If any percentage of identical pixels is greater than a preset percentage, determine the image semantics of the comparison information corresponding to the same percentage of pixels as the image semantics of the image information.

[0028] Optionally, in one possible implementation of the first aspect, a multi-dimensional analysis of the information form, information level, and information volume of corporate culture indicators within the database is used to obtain the indicator index for each corporate culture indicator, including:

[0029] Retrieve the form evaluation table, wherein the form evaluation table includes each information form and each preset evaluation value corresponding to each information form;

[0030] Based on the information format of the initial state of the corporate culture indicators in the database, the preset evaluation values ​​of the corresponding corporate culture indicators are determined in the formal evaluation table.

[0031] The preset evaluation values ​​corresponding to the same corporate culture indicator are summed, and the summation result is multiplied by the preset formal coefficient to obtain the formal evaluation value of the corresponding corporate culture indicator.

[0032] Based on the information level and information volume of corporate culture indicators in the database, the corresponding level evaluation value and quantity evaluation value are determined. The formal evaluation value, level evaluation value and quantity evaluation value are then weighted and summed to determine the indicator index for each corporate culture indicator.

[0033] Optionally, in one possible implementation of the first aspect, the corresponding level evaluation value and quantity evaluation value are determined based on the information level and information quantity of corporate culture indicators in the database, including:

[0034] Determine the port level of each sending end and each receiving end corresponding to the same corporate culture indicator, calculate the difference between the maximum port level and the minimum port level, and retrieve the information level corresponding to the obtained level difference.

[0035] The information level is multiplied by the retrieved preset level coefficient to obtain the level evaluation value corresponding to the corporate culture indicator.

[0036] Based on the database, the number of each information corresponding to each information form in the initial state of the same corporate culture indicator is determined, and the number of each information form is multiplied by the preset evaluation value of each information form to obtain the evaluation value of each number of information forms.

[0037] The evaluation values ​​of each frequency corresponding to the same corporate culture indicator are summed, and the summation result is multiplied by a preset quantity coefficient to obtain the quantitative evaluation value of the corresponding corporate culture indicator.

[0038] Optionally, in one possible implementation of the first aspect, the currently extracted corporate culture indicators are compared with historical corporate culture data to identify identical first indicators and differing second indicators. A set of differentiated indicators is then generated based on the first and second indicators, including:

[0039] The extracted corporate culture indicators are compared with historical corporate culture data. If any corporate culture indicator is found to be included in the corporate culture data, the corporate culture display data corresponding to the corporate culture indicator is determined based on a preset comparison strategy.

[0040] Compare the data displayed by each company culture with the corresponding historical sub-data of the company culture data.

[0041] The corporate culture indicator corresponding to the corporate culture display data that is identical to the comparison result data is determined as the first indicator;

[0042] The corporate culture indicators corresponding to the corporate culture display data that differ from the comparison results are identified as the second indicator, and a differentiated indicator set is generated based on the first and second indicators.

[0043] Optionally, in one possible implementation of the first aspect, determining the corporate culture display data corresponding to the corporate culture indicators based on a preset comparison strategy includes:

[0044] The information format corresponding to the corporate culture indicators includes document format, and the transmission time of each enterprise's business information corresponding to the corporate culture indicators in the document format is compared over time.

[0045] The business information of the enterprise corresponding to the maximum transmission time is determined as the enterprise culture display data corresponding to the enterprise culture indicators;

[0046] The information form of responding to the initial state of the corresponding corporate culture indicators includes image form. Based on the image recognition model, the business information of each enterprise corresponding to the corporate culture indicators in the image file form is image recognized. The number of each person and the percentage of each person with open eyes in the business information of each enterprise are weighted and summed to obtain the information evaluation value of each enterprise business information.

[0047] Add the enterprise's business information corresponding to the maximum information evaluation value to the enterprise culture display data corresponding to the enterprise culture indicators.

[0048] Optionally, in one possible implementation of the first aspect, the large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data, including:

[0049] Obtain a panoramic view of each distributed display device, determine each device area based on the panoramic view, and obtain the area of ​​each device area.

[0050] Starting from the center point of the panoramic image of the corresponding device, generate connection lines connecting each device to the center point of each device in the corresponding device area, and obtain the distance of each device corresponding to each connection line;

[0051] Each device region whose distance to each device is less than the preset distance threshold is defined as a first-level region, and each device region whose distance to each device is greater than or equal to the preset distance threshold is defined as a second-level region.

[0052] Based on the area of ​​each region, sort each primary region and each secondary region from largest to smallest to obtain the sequence of primary regions and the sequence of secondary regions.

[0053] Based on the index, each first indicator and each second indicator in the differentiated indicator set are sorted from largest to smallest to obtain the first-level indicator sequence and the second-level indicator sequence.

[0054] Sequence fusion is performed on the first-level regional sequence and the second-level regional sequence, and on the second-level indicator sequence and the first-level indicator sequence, respectively, to obtain the regional fusion sequence and the indicator fusion sequence.

[0055] Based on the regional fusion sequence, the regions corresponding to each indicator in the indicator fusion sequence are determined sequentially, and new corporate culture data are generated based on the large model.

[0056] Optionally, in one possible implementation of the first aspect, the method further includes:

[0057] The system determines the device region corresponding to any of the aforementioned indicators, identifies the distributed display device corresponding to the device region as the current display device, and generates corporate culture sub-data corresponding to the corporate culture display data of the aforementioned indicator on the current display device based on the large model.

[0058] The current display interface of each distributed display device is obtained based on the image acquisition unit, and the display area of ​​each distributed display device is determined.

[0059] Determine the percentage of blank area in the display area corresponding to the current display device; if the percentage of blank area is less than a preset first percentage, determine the minimum font size corresponding to the corporate culture sub-data.

[0060] If the minimum font size is less than a preset font size threshold, the current display device corresponding to the display area is determined as the first joint display device, and based on the current display interface, it is determined whether there is any corresponding idle display area that is adjacent to the first joint display device.

[0061] If the response is confirmed to exist, the distributed display device corresponding to the display area in the idle state is identified as the second joint display device, and the display areas corresponding to the first joint display device and the second joint display device are merged based on the current display interface to obtain a joint area;

[0062] The corporate culture sub-data is updated and displayed based on the aforementioned joint region.

[0063] Optionally, in one possible implementation of the first aspect, the method further includes:

[0064] When the proportion of the blank area is greater than the preset second proportion, the current display device corresponding to the blank area is determined as the disassembly display device, and each disassembly contour corresponding to the display area corresponding to the disassembly display device is determined based on the current display interface.

[0065] The current display interface is binarized to obtain a binarized image including each blank pixel with corresponding blank pixel value and each noise pixel with corresponding noise pixel value;

[0066] Based on the binarized image, the disassembly contour perpendicular to the forward extension direction of the character is determined as the target contour, and target indicator lines parallel to the reverse extension direction of the character are generated based on the target contour.

[0067] In response to any overlap between a target indicator line and any noise pixel, the indicator distance of the target indicator line is determined, and the difference between the indicator distance and a preset interval is calculated to obtain the target width.

[0068] Starting from the target contour, a disassembly region with the target width is generated, and the region fusion sequence is updated based on the area of ​​the corresponding disassembly region.

[0069] A second aspect of the present invention is a corporate culture data processing system based on time series and blockchain, characterized in that it includes:

[0070] The extraction module is used to obtain enterprise operation information from the database at preset time intervals, and extract corresponding enterprise culture indicators from the enterprise operation information according to preset dimensions.

[0071] The analysis module is used to perform multi-dimensional analysis of the information form, information level, and information volume of corporate culture indicators in the database to obtain the indicator index of each corporate culture indicator.

[0072] The comparison module is used to compare the currently extracted corporate culture indicators with historical corporate culture data, identify the same first indicators and the different second indicators, and generate a set of differentiated indicators based on the first and second indicators.

[0073] The generation module is used by large models to adjust the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets, and generate new corporate culture data.

[0074] A third aspect of the present invention provides a storage medium storing a computer program, which, when executed by a processor, is used to implement the method described in the first aspect of the present invention and various possible designs of the first aspect.

[0075] This invention provides a method and system for processing corporate culture data based on time-series data and blockchain. The core of this invention is to construct an intelligent system for evaluating and optimizing the effectiveness of corporate culture work. This system utilizes big data analytics, machine learning algorithms, and natural language processing technology to collect, analyze, and optimize data from each stage of corporate culture work. The system can monitor the effectiveness of corporate culture work in real time, provide scientific evaluation reports, and propose optimization suggestions based on data analysis results, thereby improving the efficiency and quality of corporate culture work. Attached Figure Description

[0076] Figure 1 A flowchart illustrating a time-series and blockchain-based approach to corporate culture data processing.

[0077] Figure 2 This is a schematic diagram of the device connection cables;

[0078] Figure 3 A schematic diagram of the target indicator line;

[0079] Figure 4 This is a structural diagram of a corporate culture data processing system based on time series and blockchain. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0082] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0083] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0084] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0085] It should be understood that in this invention, "B corresponding to A", "B corresponding to A", "A and B correspond", or "B and A correspond" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0086] Depending on the context, "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection."

[0087] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0088] This invention collects multi-source data related to corporate culture activities, including but not limited to employee behavior data (such as activity participation duration and interaction frequency), corporate operation data (such as brand search index and customer satisfaction), and social media data (such as the number of discussions and likes related to the activity). Data cleaning and standardization techniques are used to preprocess the collected heterogeneous data, transforming it into a unified format for easier subsequent analysis.

[0089] This invention employs dynamic indicator screening based on time series analysis: It utilizes time series analysis algorithms, such as ARIMA and LSTM, to perform trend analysis and anomaly detection on preprocessed data. By analyzing the changing trends of data across different time periods, key indicators closely related to the effectiveness of corporate culture activities and exhibiting dynamic characteristics are identified. For example, after a company implements new cultural values, time series analysis reveals a significant correlation between the frequency of employee discussions about these values ​​on internal forums and overall corporate cohesion, thus incorporating this indicator into the evaluation system.

[0090] This invention constructs a deep neural network model, using historical corporate culture activity data and their corresponding effectiveness evaluation results as training samples. The model's input layer receives preprocessed and filtered indicator data, and through multiple layers of neurons in the hidden layer, it performs feature extraction and nonlinear transformation on the data. The output layer obtains the evaluation results of the effectiveness of corporate culture activities. During training, an improved stochastic gradient descent algorithm (such as Adagrad, Adadelta, etc.) is used in conjunction with a genetic algorithm to optimize the weights of the neural network. The genetic algorithm simulates natural selection and the genetic process, performing crossover and mutation operations on the weights of the neural network to find the optimal weight combination. This allows the model to automatically learn the degree of influence of different indicators on the effectiveness of corporate culture activities in different scenarios, achieving dynamic optimization of the weights.

[0091] This invention constructs a deep neural network model with three hidden layers, using historical data and its corresponding effectiveness evaluation results as training samples. During training, the Adadelta algorithm combined with a genetic algorithm is used to optimize the weights of the neural network. After multiple iterations of training, the model can automatically learn the influence weights of each indicator on the effectiveness of corporate culture activities at different time periods. For example, during a company's business transformation, the weight of the "innovation culture dissemination" indicator automatically increases.

[0092] This invention stores the collected raw data and key data from the evaluation process, such as indicator selection results and weight calculation results, using blockchain technology. By leveraging the distributed ledger, hash encryption, and consensus mechanisms of blockchain, the immutability and traceability of the data are ensured. During the evaluation result verification phase, the authenticity of the data is verified through the blockchain network to prevent malicious data tampering and improve the credibility of the evaluation results.

[0093] This invention utilizes a pre-trained deep neural network model to predict the effectiveness of future corporate culture activities. Based on the prediction results, and combined with the company's strategic goals and actual needs, it provides targeted suggestions for activity optimization. For example, if it predicts a decline in participation in a certain type of cultural activity in the future, it suggests that the company adjust the activity's format or content. Simultaneously, new evaluation data and optimization measures are fed back into the model for continuous training and optimization. This invention uses a pre-trained model to predict the effectiveness of corporate culture activities in the next quarter. The prediction showed that employee satisfaction with a certain offline team-building activity would decrease. Based on this prediction, the company adjusted the content and format of the team-building activity, and after its implementation, employee satisfaction significantly improved. Furthermore, new evaluation data is fed back into the model for further optimization.

[0094] This invention provides a method for processing corporate culture data based on time series and blockchain, starting with step S102, which includes the following:

[0095] The system retrieves enterprise business information from the database at preset time intervals and extracts corresponding corporate culture indicators from the enterprise business information according to preset dimensions.

[0096] For example, in this embodiment, in order to meet the needs of enterprises, the management terminal can set a preset time period in the server in advance according to its own needs. For example, the preset time period can be one month, that is, the server will retrieve the enterprise operation information contained in the database once every month.

[0097] Since the amount of business information obtained may be large, the server will extract the business information according to preset dimensions. These preset dimensions can be pre-set by the management side, such as contract dimensions, activity dimensions, etc., so that the server will obtain the corresponding corporate culture indicators.

[0098] Furthermore, the aforementioned "obtaining enterprise operating information from the database at preset time intervals and extracting corresponding enterprise culture indicators from the enterprise operating information according to preset dimensions" also includes the following steps:

[0099] Collect multi-source data related to corporate culture activities, including but not limited to employee behavior data, business operation data, and social media data;

[0100] By preprocessing the collected heterogeneous data through data cleaning and standardization, and transforming it into a unified format, corporate culture indicators are obtained.

[0101] For example, in this embodiment, since corporate culture activities include many types, the server will first collect multi-source data related to corporate culture activities, including but not limited to employee behavior data, corporate operation data and social media data.

[0102] Employee behavior data can be understood as employee activity participation data, daily behavior data, etc.; business operation data can be understood as employee turnover rate, activity budget ratio, etc.; social media data can be understood as external evaluation data, industry comparison data, etc.

[0103] Since the multi-source data acquired at this time may contain different formats and structures, that is, heterogeneous data, such as structured data like dates, unstructured data like images, and semi-structured data like questionnaire answers, the server will use data cleaning and standardization to preprocess the collected heterogeneous data and convert it into a unified format to obtain corporate culture indicators.

[0104] The server will store the original data corresponding to the collected enterprise operation information and the key data in the evaluation process through blockchain technology. By using the distributed ledger, hash encryption and consensus mechanism of blockchain, the immutability and traceability of the data can be ensured.

[0105] Furthermore, during the evaluation result verification phase, the server verifies the authenticity of the data through the blockchain network, which can prevent the data from being maliciously tampered with, thereby improving the credibility of the evaluation results. At the same time, it can also break down data silos, reduce trust costs, and make the evaluation process more objective and efficient.

[0106] Furthermore, the aforementioned "obtaining enterprise operating information from the database at preset time intervals and extracting corresponding enterprise culture indicators from the enterprise operating information according to preset dimensions" also includes the following steps:

[0107] In response to any sending end transmitting information to any receiving end, the transmission information corresponding to the information transmission is uploaded to the database;

[0108] The transmitted information is identified, and the identification result indicates that the information format of the transmitted information is a document or a shortcut. Based on the semantic recognition model, the transmitted information is semantically compared with a preset business semantic database, wherein the preset business semantic database includes various preset dimensions.

[0109] If the response comparison result shows that the transmitted information matches any preset dimension, the transmitted information is identified as enterprise business information.

[0110] The system retrieves enterprise business information from the database at preset time intervals and extracts corresponding corporate culture indicators from the enterprise business information according to preset dimensions.

[0111] For example, in this embodiment, the sending end and the receiving end can be understood as ports with data sending, receiving and storage functions for enterprise employees to exchange information. When any sending end transmits information to other receiving ends, the server will upload the transmission information corresponding to the information transmission to the database and perform information identification on the transmission information to determine whether the information format of the transmission information is a document format or a shortcut format.

[0112] A document format can be understood as a file containing text content, while a shortcut format can be understood as text content that the sender directly sends to the receiver.

[0113] When the recognition result indicates that the information being transmitted is in document or shortcut form, the server will directly compare the transmitted information with the preset business semantic database based on the semantic recognition model. The preset business semantic database includes various preset dimensions.

[0114] When the comparison result shows that the transmitted information matches any preset dimension, it means that the transmitted information belongs to that preset dimension, and therefore the server will identify the transmitted information as enterprise business information.

[0115] The server will retrieve the enterprise operation information from the database at preset time intervals, and then extract the enterprise operation information according to preset dimensions to obtain the corresponding enterprise culture indicators.

[0116] For example, after extracting the business information of "participating in dragon boat racing" according to the preset dimension of "activity", the resulting corporate culture indicator is "dragon boat racing".

[0117] Furthermore, the above method also includes the following steps:

[0118] The response recognition result is that the corresponding transmitted information includes any image information in the form of an image, and image recognition is performed on the image information based on the image recognition model;

[0119] If the response identification result indicates the existence of an image semantic corresponding to the image information, the image information is identified as image information, and the information format of the current state of the transmission information corresponding to the image information is identified as a shortcut format.

[0120] If the response identification result is that there is no image semantic corresponding to the image information, the transmission time of the transmission information corresponding to the image information is obtained;

[0121] Using the transmission time as the median and a time interval determined based on a preset time threshold, the image information of each enterprise's business information corresponding to the transmission time falling within the time interval is determined as each comparison information.

[0122] Based on the image recognition model, the comparison pixel values ​​of each comparison pixel point that makes up each comparison information and the image pixel values ​​of each image pixel point that makes up the image information are determined;

[0123] Determine the percentage of identical pixels in each comparison pixel value corresponding to the same comparison information and each image pixel value. If any percentage of identical pixels is greater than a preset percentage, determine the image semantics of the comparison information corresponding to the same percentage of pixels as the image semantics of the image information.

[0124] For example, in this embodiment, when the recognition result is that the transmitted information includes any image information in the form of an image, the server will perform image recognition on the image information according to the image recognition model. The image information can be understood as a photograph, etc.

[0125] When the recognition result indicates that there is image semantics corresponding to the image information, it means that there are text characters in the image information. In other words, the server can determine whether the image information matches any preset dimension through image semantics.

[0126] Therefore, when the recognition result indicates that there is an image semantic corresponding to the image information, the server will first determine the image information as image information, and then determine the current state information form of the transmission information corresponding to the image information as a shortcut form;

[0127] When the recognition result indicates that there is no corresponding image semantics, it means that there are no text characters in the image information. Therefore, the server will further obtain the transmission time of the transmission information corresponding to the image information.

[0128] Since images of the same activities are often transmitted frequently within a short period of time, the server will use the transmission time as the median and determine the time interval based on a preset time threshold. For example, the preset time threshold can be 1 day. That is, the server will determine the time interval as 1 day before the transmission time to 1 day after the transmission time. Then, the server will determine the image information of each enterprise's business information that corresponds to the transmission time within the time interval as each comparison information, so as to facilitate the subsequent indirect determination of the image semantics of the corresponding image information based on each comparison information.

[0129] Next, the server will determine the comparison pixel values ​​of each comparison pixel that makes up each comparison information and the image pixel values ​​of each image pixel that makes up each image information based on the image recognition model. Then, the comparison pixel values ​​of the same comparison information are compared with the image pixel values ​​of the corresponding image information to obtain the proportion of each identical pixel.

[0130] When any identical pixel percentage exceeds the preset pixel percentage, it indicates that the comparison information corresponding to the identical pixel percentage is close to the image content of the image information. Therefore, the server will determine the image semantics of the comparison information corresponding to the identical pixel percentage as the image semantics of the image information.

[0131] Step S104 includes the following:

[0132] By conducting multi-dimensional analysis of the information format, information level, and information volume of corporate culture indicators within the database, the index of each corporate culture indicator is obtained.

[0133] For example, in this embodiment, when determining the corresponding positions of each corporate culture indicator on each distributed display device, the more important corporate culture indicators will be placed on the more conspicuous distributed display devices.

[0134] Therefore, the server will conduct multi-dimensional analysis of the information form, information level and information volume of corporate culture indicators in the database to obtain the indicator index of each corporate culture indicator. In other words, the indicator index can represent the importance of corporate culture indicators.

[0135] Furthermore, the aforementioned "multi-dimensional analysis of the information form, information level, and information volume of corporate culture indicators within the database to obtain the indicator index for each corporate culture indicator" also includes the following steps:

[0136] Retrieve the form evaluation table, wherein the form evaluation table includes each information form and each preset evaluation value corresponding to each information form;

[0137] Based on the information format of the initial state of the corporate culture indicators in the database, the preset evaluation values ​​of the corresponding corporate culture indicators are determined in the formal evaluation table.

[0138] The preset evaluation values ​​corresponding to the same corporate culture indicator are summed, and the summation result is multiplied by the preset formal coefficient to obtain the formal evaluation value of the corresponding corporate culture indicator.

[0139] Based on the information level and information volume of corporate culture indicators in the database, the corresponding level evaluation value and quantity evaluation value are determined. The formal evaluation value, level evaluation value and quantity evaluation value are then weighted and summed to determine the indicator index for each corporate culture indicator.

[0140] For example, in this embodiment, the server will first retrieve the form evaluation table, which includes various information formats and corresponding preset evaluation values ​​for each information format. Since in daily work, more important activities are mostly transmitted in document format, the preset evaluation value for the corresponding information format is higher than the preset evaluation value for the corresponding information format is in the shortcut format.

[0141] Next, the server will determine the preset evaluation value of each corporate culture indicator in the form evaluation table according to the information form of the corresponding initial state of the corporate culture indicator in the database. Since a corporate culture indicator may be transmitted in multiple information forms, the server will sum up the preset evaluation values ​​corresponding to the same corporate culture indicator, and then multiply the summation result with the preset form coefficient to obtain the form evaluation value of the corresponding corporate culture indicator.

[0142] It can be understood that when a corporate culture indicator is communicated by a higher-level employee within the company, that corporate culture indicator can be considered relatively important; similarly, the more frequently a corporate culture indicator is communicated within the company, the more important that corporate culture indicator can be.

[0143] Therefore, the server will determine the level evaluation value and the quantity evaluation value based on the information level and information volume of the corporate culture indicators in the database. Then, the form evaluation value, level evaluation value and quantity evaluation value will be weighted and summed to finally determine the indicator index corresponding to each corporate culture indicator.

[0144] Furthermore, the aforementioned "determining the corresponding level evaluation value and quantity evaluation value based on the information level and information volume of corporate culture indicators in the database" also includes the following steps:

[0145] Determine the port level of each sending end and each receiving end corresponding to the same corporate culture indicator, calculate the difference between the maximum port level and the minimum port level, and retrieve the information level corresponding to the obtained level difference.

[0146] The information level is multiplied by the retrieved preset level coefficient to obtain the level evaluation value corresponding to the corporate culture indicator.

[0147] Based on the database, the number of each information corresponding to each information form in the initial state of the same corporate culture indicator is determined, and the number of each information form is multiplied by the preset evaluation value of each information form to obtain the evaluation value of each number of information forms.

[0148] The evaluation values ​​of each frequency corresponding to the same corporate culture indicator are summed, and the summation result is multiplied by a preset quantity coefficient to obtain the quantitative evaluation value of the corresponding corporate culture indicator.

[0149] For example, in this embodiment, the server will determine the port level of each sending end and each receiving end corresponding to the same corporate culture indicator, and then calculate the difference between the maximum port level and the minimum port level to obtain the level difference.

[0150] Next, the server will retrieve the information level corresponding to the level difference, and then multiply the information level with the retrieved preset level coefficient to calculate the level evaluation value corresponding to the corporate culture indicator.

[0151] Next, the server will determine the frequency of each piece of information corresponding to the initial state of the same corporate culture indicator based on the database. Then, it will multiply each frequency of information with the preset evaluation value of each information form to obtain the evaluation value of each frequency of information.

[0152] Finally, the server will sum the evaluation values ​​of each time corresponding to the same corporate culture indicator, and then multiply the summation result by a preset quantity coefficient to obtain the quantity evaluation value of the corresponding corporate culture indicator.

[0153] Step S106 includes the following:

[0154] The extracted corporate culture indicators are compared with historical corporate culture data to identify the same first indicator and the different second indicator. A set of differentiated indicators is then generated based on the first and second indicators.

[0155] For example, in this embodiment, since the activities and other content corresponding to a corporate culture indicator may continue for a long time in the enterprise, the server will compare the currently extracted corporate culture indicator with historical corporate culture data to determine the same first indicator and the different second indicator. Then, a differentiated indicator set will be generated based on the first and second indicators, which will facilitate the subsequent determination of the distributed display device corresponding to each corporate culture indicator based on the differentiated indicator set.

[0156] Furthermore, the aforementioned "comparing the currently extracted corporate culture indicators with historical corporate culture data to identify identical first indicators and differing second indicators, and generating a differentiated indicator set based on the first and second indicators" also includes the following steps:

[0157] The extracted corporate culture indicators are compared with historical corporate culture data. If any corporate culture indicator is found to be included in the corporate culture data, the corporate culture display data corresponding to the corporate culture indicator is determined based on a preset comparison strategy.

[0158] Compare the data displayed by each company culture with the corresponding historical sub-data of the company culture data.

[0159] The corporate culture indicator corresponding to the corporate culture display data that is identical to the comparison result data is determined as the first indicator;

[0160] The corporate culture indicators corresponding to the corporate culture display data that differ from the comparison results are identified as the second indicator, and a differentiated indicator set is generated based on the first and second indicators.

[0161] For example, in this embodiment, historical corporate culture data can be understood as the overall data being displayed by various distributed display devices. The server will compare the currently extracted corporate culture indicators with the historical corporate culture data. When any corporate culture indicator is included in the corporate culture data, the server will further determine the corporate culture display data corresponding to that corporate culture indicator based on a preset comparison strategy.

[0162] Next, the server will compare the data displayed by each enterprise culture with the corresponding historical cultural sub-data of the enterprise culture data, and the sub-data displayed by each historical cultural sub-data on each distributed display device.

[0163] The server will identify the corporate culture indicators corresponding to the corporate culture display data that are the same as the comparison results as the first indicator, and then identify the corporate culture indicators corresponding to the corporate culture display data that are different from the comparison results as the second indicator, thereby generating a set of differentiated indicators based on the first and second indicators.

[0164] Furthermore, the aforementioned "determining the corporate culture display data corresponding to the corporate culture indicators based on a preset comparison strategy" also includes the following steps:

[0165] The information format corresponding to the corporate culture indicators includes document format, and the transmission time of each enterprise's business information corresponding to the corporate culture indicators in the document format is compared over time.

[0166] The business information of the enterprise corresponding to the maximum transmission time is determined as the enterprise culture display data corresponding to the enterprise culture indicators;

[0167] The information form of responding to the initial state of the corresponding corporate culture indicators includes image form. Based on the image recognition model, the business information of each enterprise corresponding to the corporate culture indicators in the image file form is image recognized. The number of each person and the percentage of each person with open eyes in the business information of each enterprise are weighted and summed to obtain the information evaluation value of each enterprise business information.

[0168] Add the enterprise's business information corresponding to the maximum information evaluation value to the enterprise culture display data corresponding to the enterprise culture indicators.

[0169] For example, in this embodiment, when the information format of the corresponding corporate culture indicator includes a document format, there may be multiple modifications to the corporate culture indicator in the document format. In order to ensure that the final version of the corporate culture indicator in the document format is displayed on the distributed display device, the server will compare the transmission time of each enterprise operation information corresponding to the corporate culture indicator in the document format, and then determine the enterprise operation information with the maximum transmission time as the corporate culture display data corresponding to the corporate culture indicator.

[0170] When the initial state information of the corresponding corporate culture indicator includes an image, the server will use an image recognition model to perform image recognition on the corporate business information of each enterprise corresponding to the image file format, and obtain the number of each person and the percentage of each person with open eyes in the corresponding corporate business information.

[0171] Next, the server will perform a weighted summation of the number of people and the percentage of people with open eyes in each enterprise's business information to obtain the information evaluation value for each enterprise's business information. Since the more people in the corresponding enterprise's business information and the higher the percentage of people with open eyes, the more suitable the enterprise's business information is for final display, the number of people and the percentage of people with open eyes are directly proportional to the information evaluation value.

[0172] Finally, the server will add the enterprise's business information corresponding to the maximum information evaluation value to the enterprise culture display data corresponding to the enterprise culture indicators, so that the distributed display devices can not only display text characters, but also display related images, thereby enhancing the aesthetics.

[0173] Step S108 includes the following:

[0174] The large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data.

[0175] For example, in this embodiment, the server controls the large model to adjust the specifications and quantity of historical corporate culture data on the corresponding distributed display devices based on indicator indices and differentiated indicator sets, and finally generates new corporate culture data.

[0176] Furthermore, the aforementioned "large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data" also includes the following steps:

[0177] Obtain a panoramic view of each distributed display device, determine each device area based on the panoramic view, and obtain the area of ​​each device area.

[0178] Starting from the center point of the panoramic image of the corresponding device, generate connection lines connecting each device to the center point of each device in the corresponding device area, and obtain the distance of each device corresponding to each connection line;

[0179] Each device region whose distance to each device is less than the preset distance threshold is defined as a first-level region, and each device region whose distance to each device is greater than or equal to the preset distance threshold is defined as a second-level region.

[0180] Based on the area of ​​each region, sort each primary region and each secondary region from largest to smallest to obtain the sequence of primary regions and the sequence of secondary regions.

[0181] Based on the index, each first indicator and each second indicator in the differentiated indicator set are sorted from largest to smallest to obtain the first-level indicator sequence and the second-level indicator sequence.

[0182] Sequence fusion is performed on the first-level regional sequence and the second-level regional sequence, and on the second-level indicator sequence and the first-level indicator sequence, respectively, to obtain the regional fusion sequence and the indicator fusion sequence.

[0183] Based on the regional fusion sequence, the regions corresponding to each indicator in the indicator fusion sequence are determined sequentially, and new corporate culture data are generated based on the large model.

[0184] For example, in this embodiment, in order to better understand the server, the server will first obtain a panoramic view of each distributed display device, and then determine each device area of ​​each distributed display device based on the panoramic view of the device and obtain the area of ​​each area of ​​each device.

[0185] Next, the server will determine the center point of the panoramic image corresponding to the device and the center points of each device in each device area. Then, starting from the center point of the image, it will generate connection lines to each device's center point, such as... Figure 2 As shown, obtain the distance between each device and the corresponding device connection line;

[0186] When the device distance is less than the preset distance threshold, it means that the device area corresponding to the device distance is closer to the center point of the image, which is a better display position. Therefore, the server will determine each device area corresponding to each device distance less than the preset distance threshold as a first-level area.

[0187] When the device distance is greater than or equal to the preset distance threshold, it means that the device area corresponding to the device distance is far from the center point of the image and is not a good display position. Therefore, the server will determine each device area corresponding to each device distance greater than or equal to the preset distance threshold as a secondary area.

[0188] Next, the server will sort each primary region and each secondary region from largest to smallest according to the area, thus obtaining the primary region sequence and the secondary region sequence. Then, according to the index, each primary indicator and each secondary indicator in the differentiated indicator set will be sorted from largest to smallest, thus obtaining the primary indicator sequence and the secondary indicator sequence.

[0189] Then, the server will perform sequence fusion on the first-level regional sequence and the second-level regional sequence, and the second-level indicator sequence and the first-level indicator sequence respectively to obtain the regional fusion sequence and the indicator fusion sequence. Then, according to the regional fusion sequence, the corresponding regions of each indicator in the indicator fusion sequence are determined in sequence, and new corporate culture data is generated based on the large model.

[0190] This embodiment determines the regional fusion sequence and the indicator fusion sequence, so that the second indicator with differences can be placed in a better display position, thereby increasing the display effect of the distributed display device.

[0191] Furthermore, the above method also includes the following steps:

[0192] The system determines the device region corresponding to any of the aforementioned indicators, identifies the distributed display device corresponding to the device region as the current display device, and generates corporate culture sub-data corresponding to the corporate culture display data of the aforementioned indicator on the current display device based on the large model.

[0193] The current display interface of each distributed display device is obtained based on the image acquisition unit, and the display area of ​​each distributed display device is determined.

[0194] Determine the percentage of blank area in the display area corresponding to the current display device; if the percentage of blank area is less than a preset first percentage, determine the minimum font size corresponding to the corporate culture sub-data.

[0195] If the minimum font size is less than a preset font size threshold, the current display device corresponding to the display area is determined as the first joint display device, and based on the current display interface, it is determined whether there is any corresponding idle display area that is adjacent to the first joint display device.

[0196] If the response is confirmed to exist, the distributed display device corresponding to the display area in the idle state is identified as the second joint display device, and the display areas corresponding to the first joint display device and the second joint display device are merged based on the current display interface to obtain a joint area;

[0197] The corporate culture sub-data is updated and displayed based on the aforementioned joint region.

[0198] For example, in this embodiment, once the device region corresponding to any one indicator is determined, the server will identify the distributed display device corresponding to that device region as the current display device, and then generate corporate culture sub-data corresponding to the corporate culture display data of the indicator on the current display device based on the large model.

[0199] Next, the server will obtain the current display interface of each distributed display device according to the image acquisition unit, determine the display area of ​​each distributed display device, and then determine the blank area ratio of the display area corresponding to the current display device.

[0200] When the proportion of blank area is less than the preset first proportion, it means that the corporate culture sub-data has basically covered the display area, which may affect the viewing experience of the distributed display device. At this time, the server will determine the minimum font size corresponding to the corporate culture sub-data. When the minimum font size is less than the preset font size threshold, it means that the font size of the corresponding corporate culture sub-data is already very small, and it is impossible to increase the proportion of blank area by reducing the font size.

[0201] Therefore, the server will identify the current display device corresponding to the display area as the first joint display device, and then determine whether there is any corresponding idle display area that is adjacent to the first joint display device based on the current display interface.

[0202] When it is determined that there is any corresponding idle display area that is adjacent to the first joint display device, the server will identify the distributed display device corresponding to this idle display area as the second joint display device, and then merge the display areas corresponding to the first joint display device and the second joint display device according to the current display interface to obtain the joint area.

[0203] Finally, the server will update and display the corporate culture sub-data according to the joint region, so that there is a certain blank area on the final distributed display device, and the font size of the corresponding corporate culture sub-data is used for everyone to view.

[0204] Furthermore, the above method also includes the following steps:

[0205] When the proportion of the blank area is greater than the preset second proportion, the current display device corresponding to the blank area is determined as the disassembly display device, and each disassembly contour corresponding to the display area corresponding to the disassembly display device is determined based on the current display interface.

[0206] The current display interface is binarized to obtain a binarized image including each blank pixel with corresponding blank pixel value and each noise pixel with corresponding noise pixel value;

[0207] Based on the binarized image, the disassembly contour perpendicular to the forward extension direction of the character is determined as the target contour, and target indicator lines parallel to the reverse extension direction of the character are generated based on the target contour.

[0208] In response to any overlap between a target indicator line and any noise pixel, the indicator distance of the target indicator line is determined, and the difference between the indicator distance and a preset interval is calculated to obtain the target width.

[0209] Starting from the target contour, a disassembly region with the target width is generated, and the region fusion sequence is updated based on the area of ​​the corresponding disassembly region.

[0210] For example, in this embodiment, when the proportion of blank area is greater than the preset second proportion, in order to improve the utilization rate of distributed devices, the server will determine the current display device corresponding to the blank area as the disassembly display device, and then determine each disassembly outline corresponding to the display area corresponding to the disassembly display device according to the current display interface.

[0211] Next, the server binarizes the current display interface, obtaining a binarized image containing the blank pixels corresponding to their blank pixel values ​​and the noise pixels corresponding to their noise pixel values. Then, based on the binarized image, the deconstructed contour perpendicular to the forward extension direction of the characters is determined as the target contour. Since characters extend from top to bottom, the target contour is the target contour below the corresponding display area. Next, the server generates target indicator lines parallel to the reverse extension direction of the characters based on the target contour, such as... Figure 3 As shown;

[0212] When any target indicator line overlaps with any noise pixel, the server determines the indicator distance of the target indicator line and calculates the difference between the indicator distance and the preset interval to obtain the target width. This ensures that the subsequent disassembly regions generated with the target width, starting from the target outline, have a certain distance from the corporate culture sub-data. Finally, the server updates the region fusion sequence based on the area of ​​the disassembly regions.

[0213] Enterprise culture data processing system based on time series and blockchain, such as Figure 2 As shown, it includes:

[0214] The extraction module is used to obtain enterprise operation information from the database at preset time intervals, and extract corresponding enterprise culture indicators from the enterprise operation information according to preset dimensions.

[0215] The analysis module is used to perform multi-dimensional analysis of the information form, information level, and information volume of corporate culture indicators in the database to obtain the indicator index of each corporate culture indicator.

[0216] The comparison module is used to compare the currently extracted corporate culture indicators with historical corporate culture data, identify the same first indicators and the different second indicators, and generate a set of differentiated indicators based on the first and second indicators.

[0217] The generation module is used by large models to adjust the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets, and generate new corporate culture data.

[0218] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0219] The storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, the storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be a component of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). This ASIC can also be located within a user device. Alternatively, the processor and storage medium can exist as discrete components in a communication device. Storage media can be read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices, etc.

[0220] The present invention also provides a program product including execution instructions stored in a storage medium. At least one processor of the device can read the execution instructions from the storage medium, and the execution instructions by the at least one processor cause the device to implement the methods provided in the various embodiments described above.

[0221] In the above-described terminal or server embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing corporate culture data based on time series and blockchain, characterized in that, include: The system retrieves enterprise business information from the database at preset time intervals and extracts corresponding corporate culture indicators from the enterprise business information according to preset dimensions. The index of each corporate culture indicator is obtained by multi-dimensional analysis of the information form, information level and information volume of corporate culture indicators in the database. The extracted corporate culture indicators are compared with historical corporate culture data to identify the same first indicator and the different second indicator, and a differentiated indicator set is generated based on the first and second indicators. The large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data. The large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data, including: Obtain a panoramic view of each distributed display device, determine each device area based on the panoramic view, and obtain the area of ​​each device area. Starting from the center point of the panoramic image of the corresponding device, generate connection lines connecting each device to the center point of each device in the corresponding device area, and obtain the distance of each device corresponding to each connection line; Each device region whose distance to each device is less than the preset distance threshold is defined as a first-level region, and each device region whose distance to each device is greater than or equal to the preset distance threshold is defined as a second-level region. Based on the area of ​​each region, sort each primary region and each secondary region from largest to smallest to obtain the sequence of primary regions and the sequence of secondary regions. Based on the index, each first indicator and each second indicator in the differentiated indicator set are sorted from largest to smallest to obtain the first-level indicator sequence and the second-level indicator sequence. Sequence fusion is performed on the first-level regional sequence and the second-level regional sequence, and on the second-level indicator sequence and the first-level indicator sequence, respectively, to obtain the regional fusion sequence and the indicator fusion sequence. Based on the regional fusion sequence, the regions corresponding to each indicator in the indicator fusion sequence are determined sequentially, and new corporate culture data are generated based on the large model. The method further includes: The system determines the device region corresponding to any of the aforementioned indicators, identifies the distributed display device corresponding to the device region as the current display device, and generates corporate culture sub-data corresponding to the corporate culture display data of the aforementioned indicator on the current display device based on the large model. The current display interface of each distributed display device is obtained based on the image acquisition unit, and the display area of ​​each distributed display device is determined. Determine the percentage of blank area in the display area corresponding to the current display device; if the percentage of blank area is less than a preset first percentage, determine the minimum font size corresponding to the corporate culture sub-data. If the minimum font size is less than a preset font size threshold, the current display device corresponding to the display area is determined as the first joint display device, and based on the current display interface, it is determined whether there is any corresponding idle display area that is adjacent to the first joint display device. If the response is confirmed to exist, the distributed display device corresponding to the display area in the idle state is identified as the second joint display device, and the display areas corresponding to the first joint display device and the second joint display device are merged based on the current display interface to obtain a joint area; The corporate culture sub-data is updated and displayed based on the aforementioned joint region; The method further includes: When the proportion of the blank area is greater than the preset second proportion, the current display device corresponding to the blank area is determined as the disassembly display device, and each disassembly contour corresponding to the display area corresponding to the disassembly display device is determined based on the current display interface. The current display interface is binarized to obtain a binarized image that includes each blank pixel with corresponding blank pixel value and each noise pixel with corresponding noise pixel value; Based on the binarized image, the disassembly contour perpendicular to the forward extension direction of the character is determined as the target contour, and target indicator lines parallel to the reverse extension direction of the character are generated based on the target contour. In response to any overlap between a target indicator line and any noise pixel, the indicator distance of the target indicator line is determined, and the difference between the indicator distance and a preset interval is calculated to obtain the target width. Starting from the target contour, a disassembly region with the target width is generated, and the region fusion sequence is updated based on the area of ​​the corresponding disassembly region.

2. The method according to claim 1, characterized in that, The system retrieves enterprise operating information from the database at preset time intervals, and extracts corresponding corporate culture indicators from the enterprise operating information according to preset dimensions, including: Collect multi-source data related to corporate culture activities, including but not limited to employee behavior data, business operation data, and social media data; By preprocessing the collected heterogeneous data through data cleaning and standardization, and transforming it into a unified format, corporate culture indicators are obtained.

3. The method according to claim 1, characterized in that, The original data corresponding to the collected enterprise operation information and the key data in the evaluation process will be stored using blockchain technology; By leveraging the distributed ledger, hash encryption, and consensus mechanisms of blockchain, the immutability and traceability of data can be ensured. During the evaluation result verification phase, the authenticity of the data is verified through a blockchain network to prevent malicious data tampering and improve the credibility of the evaluation results.

4. The method according to claim 2, characterized in that, The system retrieves enterprise operating information from the database at preset time intervals, and extracts corresponding corporate culture indicators from the enterprise operating information according to preset dimensions, including: In response to any sending end transmitting information to any receiving end, the transmission information corresponding to the information transmission is uploaded to the database; The transmitted information is identified, and the identification result indicates that the information format of the transmitted information is a document or a shortcut. Based on the semantic recognition model, the transmitted information is semantically compared with a preset business semantic database, wherein the preset business semantic database includes various preset dimensions. If the response comparison result shows that the transmitted information matches any preset dimension, the transmitted information is identified as enterprise business information. The system retrieves enterprise business information from the database at preset time intervals and extracts corresponding corporate culture indicators from the enterprise business information according to preset dimensions.

5. The method according to claim 4, characterized in that, The method further includes: The response recognition result is that the corresponding transmitted information includes any image information in the form of an image, and image recognition is performed on the image information based on the image recognition model; If the response identification result indicates the existence of an image semantic corresponding to the image information, the image information is identified as image information, and the information format of the current state of the transmission information corresponding to the image information is identified as a shortcut format. If the response identification result is that there is no image semantic corresponding to the image information, the transmission time of the transmission information corresponding to the image information is obtained; Using the transmission time as the median and a time interval determined based on a preset time threshold, the image information of each enterprise's business information corresponding to the transmission time falling within the time interval is determined as each comparison information. Based on the image recognition model, the comparison pixel values ​​of each comparison pixel point that makes up each comparison information and the image pixel values ​​of each image pixel point that makes up the image information are determined; Determine the percentage of identical pixels in each comparison pixel value corresponding to the same comparison information and each image pixel value. If any percentage of identical pixels is greater than a preset percentage, determine the image semantics of the comparison information corresponding to the same percentage of pixels as the image semantics of the image information.

6. The method according to claim 4, characterized in that, A multi-dimensional analysis of the information format, information level, and information volume of corporate culture indicators within the database yields the indicator index for each corporate culture indicator, including: Retrieve the form evaluation table, wherein the form evaluation table includes each information form and each preset evaluation value corresponding to each information form; Based on the information format of the initial state of the corporate culture indicators in the database, the preset evaluation values ​​of the corresponding corporate culture indicators are determined in the formal evaluation table. The preset evaluation values ​​corresponding to the same corporate culture indicator are summed, and the summation result is multiplied by the preset formal coefficient to obtain the formal evaluation value of the corresponding corporate culture indicator. Based on the information level and information volume of corporate culture indicators in the database, the corresponding level evaluation value and quantity evaluation value are determined. The formal evaluation value, level evaluation value and quantity evaluation value are then weighted and summed to determine the indicator index for each corporate culture indicator.

7. The method according to claim 6, characterized in that, Based on the information level and amount of corporate culture indicators within the database, corresponding level evaluation values ​​and quantity evaluation values ​​are determined, including: Determine the port level of each sending end and each receiving end corresponding to the same corporate culture indicator, calculate the difference between the maximum port level and the minimum port level, and retrieve the information level corresponding to the obtained level difference. The information level is multiplied by the retrieved preset level coefficient to obtain the level evaluation value corresponding to the corporate culture indicator. Based on the database, the number of each information corresponding to each information form in the initial state of the same corporate culture indicator is determined, and the number of each information form is multiplied by the preset evaluation value of each information form to obtain the evaluation value of each number of information forms. The evaluation values ​​of each frequency corresponding to the same corporate culture indicator are summed, and the summation result is multiplied by a preset quantity coefficient to obtain the quantitative evaluation value of the corresponding corporate culture indicator.

8. The method according to claim 7, characterized in that, The extracted corporate culture indicators are compared with historical corporate culture data to identify identical primary indicators and differing secondary indicators. A differentiated indicator set is then generated based on these primary and secondary indicators, including: The extracted corporate culture indicators are compared with historical corporate culture data. If any corporate culture indicator is found to be included in the corporate culture data, the corporate culture display data corresponding to the corporate culture indicator is determined based on a preset comparison strategy. Compare the data displayed by each company culture with the corresponding historical sub-data of the company culture data. The corporate culture indicator corresponding to the corporate culture display data that is identical to the comparison result data is determined as the first indicator; The corporate culture indicators corresponding to the corporate culture display data that differ from the comparison results are identified as the second indicator, and a differentiated indicator set is generated based on the first and second indicators.

9. The method according to claim 8, characterized in that, Based on a preset comparison strategy, the corporate culture display data corresponding to the aforementioned corporate culture indicators is determined, including: The information format corresponding to the corporate culture indicators includes document format, and the transmission time of each enterprise's business information corresponding to the corporate culture indicators in the document format is compared over time. The business information of the enterprise corresponding to the maximum transmission time is determined as the enterprise culture display data corresponding to the enterprise culture indicators; The information form of responding to the initial state of the corresponding corporate culture indicators includes image form. Based on the image recognition model, the business information of each enterprise corresponding to the corporate culture indicators in the image file form is image recognized. The number of each person and the percentage of each person with open eyes in the business information of each enterprise are weighted and summed to obtain the information evaluation value of each enterprise business information. Add the enterprise's business information corresponding to the maximum information evaluation value to the enterprise culture display data corresponding to the enterprise culture indicators.

10. A corporate culture data processing system based on time series and blockchain, characterized in that, include: The extraction module is used to obtain enterprise operation information from the database at preset time intervals, and extract corresponding enterprise culture indicators from the enterprise operation information according to preset dimensions. The analysis module is used to perform multi-dimensional analysis of the information form, information level, and information volume of corporate culture indicators in the database to obtain the indicator index of each corporate culture indicator. The comparison module is used to compare the currently extracted corporate culture indicators with historical corporate culture data, identify the same first indicators and the different second indicators, and generate a set of differentiated indicators based on the first and second indicators. The generation module is used by large models to adjust the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicators and differentiated indicator sets, and generate new corporate culture data. The large model adjusts the specifications and quantity of historical corporate culture data on corresponding distributed display devices based on indicator indices and differentiated indicator sets to generate new corporate culture data, including: Obtain a panoramic view of each distributed display device, determine each device area based on the panoramic view, and obtain the area of ​​each device area. Starting from the center point of the panoramic image of the corresponding device, generate connection lines connecting each device to the center point of each device in the corresponding device area, and obtain the distance of each device corresponding to each connection line; Each device region whose distance to each device is less than the preset distance threshold is defined as a first-level region, and each device region whose distance to each device is greater than or equal to the preset distance threshold is defined as a second-level region. Based on the area of ​​each region, sort each primary region and each secondary region from largest to smallest to obtain the sequence of primary regions and the sequence of secondary regions. Based on the index, each first indicator and each second indicator in the differentiated indicator set are sorted from largest to smallest to obtain the first-level indicator sequence and the second-level indicator sequence. Sequence fusion is performed on the first-level regional sequence and the second-level regional sequence, and on the second-level indicator sequence and the first-level indicator sequence, respectively, to obtain the regional fusion sequence and the indicator fusion sequence. Based on the regional fusion sequence, the regions corresponding to each indicator in the indicator fusion sequence are determined sequentially, and new corporate culture data are generated based on the large model. The method further includes: The system determines the device region corresponding to any of the aforementioned indicators, identifies the distributed display device corresponding to the device region as the current display device, and generates corporate culture sub-data corresponding to the corporate culture display data of the aforementioned indicator on the current display device based on the large model. The current display interface of each distributed display device is obtained based on the image acquisition unit, and the display area of ​​each distributed display device is determined. Determine the percentage of blank area in the display area corresponding to the current display device; if the percentage of blank area is less than a preset first percentage, determine the minimum font size corresponding to the corporate culture sub-data. If the minimum font size is less than a preset font size threshold, the current display device corresponding to the display area is determined as the first joint display device, and based on the current display interface, it is determined whether there is any corresponding idle display area that is adjacent to the first joint display device. If the response is confirmed to exist, the distributed display device corresponding to the display area in the idle state is identified as the second joint display device, and the display areas corresponding to the first joint display device and the second joint display device are merged based on the current display interface to obtain a joint area; The corporate culture sub-data is updated and displayed based on the aforementioned joint region; The method further includes: When the proportion of the blank area is greater than the preset second proportion, the current display device corresponding to the blank area is determined as the disassembly display device, and each disassembly contour corresponding to the display area corresponding to the disassembly display device is determined based on the current display interface. The current display interface is binarized to obtain a binarized image that includes each blank pixel with corresponding blank pixel value and each noise pixel with corresponding noise pixel value; Based on the binarized image, the disassembly contour perpendicular to the forward extension direction of the character is determined as the target contour, and target indicator lines parallel to the reverse extension direction of the character are generated based on the target contour. In response to any overlap between a target indicator line and any noise pixel, the indicator distance of the target indicator line is determined, and the difference between the indicator distance and a preset interval is calculated to obtain the target width. Starting from the target contour, a disassembly region with the target width is generated, and the region fusion sequence is updated based on the area of ​​the corresponding disassembly region.

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