High vocational support breeding, delivery and education fusion resource matching and quality evaluation system

Abstract

The invention relates to the technical field of educational resource management, and discloses a higher vocational support breeding, delivery and education fusion resource matching and quality evaluation system. Comprising a multi-source growth data acquisition and block chain evidence storage module, an infant growth token casting module, a school-enterprise cooperation token collection and bonus sharing module, a token upside-down triggering and punishment execution module and a resource matching weight self-adaptive redistribution module. According to the system, a unique identity label is generated for each infant entering the support, multi-dimensional growth data are collected every day and are uploaded to a chain for evidence storage, and an infant growth token which cannot be added is cast in real time according to the growth increment and the up-to-standard condition; and automatically reallocating resources according to the accumulated total effective token amount and the upside-down state. According to the invention, through combination of the multi-source growth data acquisition and block chain evidence storage module and the infant growth token casting module, objective and traceable quantification of the nursing service quality is realized, and subjectivity and hysteresis of traditional manual evaluation are avoided.

Description

Technical Field

[0001] This invention relates to the field of educational resource management technology, and in particular to a resource matching and quality evaluation system for vocational college childcare industry-education integration. Background Technology

[0002] With the deepening of vocational education reform in my country, the integration of industry and education between higher vocational colleges and the childcare industry has become an important path to improve the quality of childcare services and the relevance of talent training. The industry-education integration resource matching and quality evaluation system, through a school-enterprise cooperation platform, achieves resource docking and quality monitoring, and has been widely applied in the field of education management.

[0003] Existing technologies typically employ a tag-based matching and human evaluation approach. Specifically, businesses post their childcare service needs, vocational colleges fill in resource tags such as teachers and training bases, and the system calculates the matching degree and recommends partners using rule-weighted or bilateral matching algorithms. After a partnership is formed, quality evaluation mainly relies on periodic questionnaires, expert scoring, or fuzzy comprehensive evaluation methods. Administrators or third parties score the partners based on indicators, and an evaluation report is generated accordingly. This approach has a clear structure and is easy to implement.

[0004] However, in the process of realizing the technical solution of this application, the inventors of this application discovered that the above-mentioned technology has at least the following technical problems: the existing quality evaluation relies on manual scoring and periodic post-event assessment, which makes it difficult to achieve objective and real-time quantification of childcare service output. Summary of the Invention

[0005] To address the above shortcomings, this invention provides a resource matching and quality evaluation system for vocational college childcare that integrates industry and education. It aims to improve the existing quality evaluation system, which relies on manual scoring and periodic post-event assessments, making it difficult to achieve objective and real-time quantification of childcare service output.

[0006] This invention provides the following technical solution: a resource matching and quality evaluation system for vocational college childcare industry-education integration, comprising the following modules:

[0007] The multi-source growth data collection and blockchain evidence storage module is used to generate a unique identity for each child enrolled in the care facility. It collects multi-dimensional growth data from wearable devices, smart toy interactions, teacher tablet records, and parent APP feedback in real time every day. After being signed by multiple parties, the data is written into the consortium blockchain to form an immutable growth archive block.

[0008] The Infant Growth Token Minting Module is used to mint non-renewable Infant Growth Tokens at fixed times every day based on the positive increment of each infant's multidimensional growth data relative to the national benchmark value for that month and whether the standard has been met.

[0009] The "School-Enterprise Cooperation Token Collection and Profit Sharing" module is used to directly collect a portion of the infant growth tokens minted by all infants and toddlers actually served by the same school-enterprise cooperation entity in the current month to the childcare institution, and another portion to the corresponding higher vocational college to form long-term profit sharing.

[0010] The token inversion trigger and penalty execution module is used to calculate the resource efficiency ratio of each school-enterprise cooperation entity every month. When the resource efficiency ratio is lower than the lower quartile of the entire network for several consecutive months, the token inversion state is automatically triggered, and the resource matching weight is forcibly reduced and the historically accumulated infant growth tokens are deducted at an increasing monthly rate during the subsequent penalty period.

[0011] The resource matching weight adaptive reallocation module is used to automatically recalculate and execute the allocation weights of training quotas, teacher assignments, and financial subsidies before the start of each natural month, based on the total number of valid infant growth tokens of each school-enterprise cooperation entity up to the previous month and whether it is in a token inversion state.

[0012] Preferably, the specific process of the multi-source growth data acquisition and blockchain evidence storage module is as follows:

[0013] The system first generates a globally unique identifier for each infant;

[0014] Subsequently, four raw data streams are continuously received daily. Each stream of data is standardized and then weighted and merged to form a growth event vector for the day.

[0015] Then, the teacher, parent, and wearable device terminals complete the on-chain multi-signature process, and finally, the daily growth event vector and multi-signature are packaged and written into a new block of the consortium blockchain.

[0016] Preferably, the specific process of the infant growth token casting module is as follows:

[0017] The system initiates the minting process at a fixed time each day, reading the growth event vectors of each infant that have been uploaded to the blockchain that day, comparing them with the national benchmark value corresponding to that age month, calculating the positive incremental part and minting basic infant growth tokens. At the same time, additional achievement reward infant growth tokens are minted for infants who meet all dimensions. Finally, the total amount of newly added infant growth tokens on the entire network that day is recorded in the consortium blockchain ledger.

[0018] Preferably, the specific process of the token collection and profit-sharing module of the university-enterprise cooperation entity is as follows:

[0019] The system initiates a monthly data collection process at the beginning of each month, and compiles all infant growth passes generated by each infant in the previous month based on the correspondence between the actual service institution and the training institution for each infant.

[0020] A portion of the proceeds was then transferred directly to the corresponding childcare institution's account, while the other portion was transferred to the long-term dividend account of the vocational college from which the child graduated.

[0021] Preferably, the specific process of the token inversion triggering and penalty execution module in terms of trigger judgment is as follows:

[0022] The system calculates the resource efficiency ratio of each school-enterprise cooperation entity for the previous month at the beginning of each month;

[0023] Subsequently, it is checked whether the resource efficiency ratio of the collaboration has been lower than the lower quartile of the entire network for several consecutive months. If the condition is met, the collaboration is immediately marked as a token inversion state on the consortium blockchain and broadcast to the entire network.

[0024] Preferably, the specific process of the token inversion triggering and penalty execution module in terms of penalty execution is as follows:

[0025] For several consecutive months after the token inversion state is triggered, the resource matching weight will be forcibly reduced and deducted from the historical accumulated infant growth tokens at a monthly time until the resource efficiency ratio recovers to above the network median or the penalty period ends naturally, at which point the token inversion state will be automatically lifted.

[0026] Preferably, the specific process of the resource matching weight adaptive reallocation module is as follows:

[0027] The system starts the weight calculation process at a set time at the beginning of each month.

[0028] First, we will tally the total number of valid infant and toddler growth passes accumulated by each school-enterprise cooperation entity up to last month, after deductions.

[0029] Then, the penalty factor is determined based on whether the token is in an inverted state, and the final weight is calculated.

[0030] Subsequently, the allocation of all training slots, faculty assignment slots, and government subsidy slots for the month will be automatically completed according to the final weighting ratio and pushed to each school-enterprise cooperation entity.

[0031] Preferably, the process for maintaining the total number of valid infant growth tokens is as follows:

[0032] The system accumulates newly acquired infant growth tokens in real time during each monthly collection and subtracts the deducted infant growth tokens in real time during each penalty execution, thereby ensuring that the total amount of cumulative valid infant growth tokens used for weight calculation at any time is the actual stock after deduction.

[0033] Preferably, the automatic unwinding process for the token inverted state is as follows:

[0034] When the resource efficiency ratio of the school-enterprise cooperation entity recovers to above the median of the entire network in any month, the system immediately removes the token inversion mark of the school-enterprise cooperation entity on the consortium blockchain, restores its normal resource matching weight, and stops the deduction of subsequent historical accumulated infant growth tokens.

[0035] Preferably, the calculation process for the resource efficiency ratio is as follows:

[0036] The system automatically obtains the total number of infant growth certificates actually collected by each school-enterprise cooperation entity in the previous month as the numerator, and the total number of resource points converted from all resources actually obtained by the school-enterprise cooperation entity in the previous month as the denominator. The two are divided to obtain the resource efficiency ratio of the school-enterprise cooperation entity in the current month.

[0037] The present invention has the following beneficial effects:

[0038] 1. This invention combines multi-source growth data collection with a blockchain notarization module and an infant growth token minting module. It uses real, multi-dimensional growth data of infants as the sole measurement basis to mint non-renewable infant growth tokens in real time, thereby achieving objective, traceable, and quantifiable quality of childcare services and avoiding the subjectivity and lag of traditional manual evaluation.

[0039] 2. This invention combines a token collection and profit-sharing module for school-enterprise cooperation with a token inversion triggering and penalty execution module. Part of the infant growth tokens are collected to childcare institutions, and another part is used to form long-term profit-sharing for higher vocational colleges. At the same time, inefficient cooperation entities are penalized by deducting historical accumulated tokens and reducing resource weight, forming a continuous positive incentive and reverse elimination mechanism.

[0040] 3. This invention combines a resource matching weight adaptive redistribution module with a cumulative effective infant growth token total maintenance process, so that resources such as training slots, teacher assignments, and financial subsidies are automatically concentrated in high-efficiency school-enterprise cooperation entities, realizing closed-loop automatic correction and dynamic optimization of resource matching and quality evaluation. Attached Figure Description

[0041] Figure 1 This is a system architecture diagram of a vocational college childcare industry-education integration resource matching and quality evaluation system proposed in this invention;

[0042] Figure 2 This is a schematic diagram of the multi-source growth data collection and on-chain evidence storage process of a higher vocational childcare industry-education integration resource matching and quality evaluation system proposed in this invention.

[0043] Figure 3 This is a schematic diagram of token casting and token collection for a vocational college childcare industry-education integration resource matching and quality evaluation system proposed in this invention. Detailed Implementation

[0044] The technical solutions in 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.

[0045] Reference Figures 1-3 This invention provides a resource matching and quality evaluation system for vocational college childcare industry-education integration, comprising the following modules:

[0046] The multi-source growth data collection and blockchain evidence storage module is used to generate a unique identity for each child enrolled in the care facility. It collects multi-dimensional growth data from wearable devices, smart toy interactions, teacher tablet records, and parent APP feedback in real time every day. After being signed by multiple parties, the data is written into the consortium blockchain to form an immutable growth archive block.

[0047] The Infant Growth Token Minting Module is used to mint non-renewable Infant Growth Tokens at fixed times every day based on the positive increment of each infant's multidimensional growth data relative to the national benchmark value for that month and whether the standard has been met.

[0048] The "School-Enterprise Cooperation Token Collection and Profit Sharing" module is used to directly collect a portion of the infant growth tokens minted by all infants and toddlers actually served by the same school-enterprise cooperation entity in the current month to the childcare institution, and another portion to the corresponding higher vocational college to form long-term profit sharing.

[0049] The token inversion trigger and penalty execution module is used to calculate the resource efficiency ratio of each school-enterprise cooperation entity every month. When the resource efficiency ratio is lower than the lower quartile of the entire network for several consecutive months, the token inversion state is automatically triggered, and the resource matching weight is forcibly reduced and the historically accumulated infant growth tokens are deducted at an increasing monthly rate during the subsequent penalty period.

[0050] The resource matching weight adaptive reallocation module is used to automatically recalculate and execute the allocation weights of training quotas, teacher assignments, and financial subsidies before the start of each natural month, based on the total number of valid infant growth tokens of each school-enterprise cooperation entity up to the previous month and whether it is in a token inversion state.

[0051] Specifically, the system is deployed in a consortium blockchain network environment composed of childcare institution nodes, vocational school nodes, and regulatory nodes. It completes data recording, calculation, and resource allocation through an on-chain account system, on-chain data storage structure, and on-chain token management contracts. The system includes a multi-source growth data collection and blockchain notarization module, an infant and toddler growth token minting module, a school-enterprise cooperation token collection and profit-sharing module, a token inversion triggering and penalty execution module, and a resource matching weight adaptive reallocation module.

[0052] The multi-source growth data collection and blockchain notarization module is used to generate a unique identity for each enrolled infant and toddler, and to receive multi-dimensional growth data generated by wearable devices, smart toy interactive terminals, teacher tablet terminals, and parent mobile terminals. After standardization and weighted fusion of the data from each source, a growth event vector is formed, and multiple signatures are generated by the teacher, parent, and wearable device terminals. After on-chain verification, the data is written into the consortium blockchain to generate an immutable growth profile block.

[0053] The Infant Growth Token Minting module executes the token minting process at fixed times each day. The system reads the growth event vectors written to the blockchain and calculates the positive increment based on the corresponding baseline value for each month's age, generating basic Infant Growth Tokens. If all growth dimensions meet the baseline requirements, a qualifying reward Infant Growth Token is generated. Infant Growth Tokens are non-incremental; their creation and quantity are recorded through the on-chain token management contract.

[0054] The token collection and profit-sharing module for the industry-university cooperation is used at the beginning of each month to collect the infant growth tokens generated in the previous month into the token accounts of the childcare institutions and the long-term profit-sharing accounts of the vocational colleges, based on the correspondence between the childcare institutions serving each infant and the training institutions. The collection process is completed through on-chain transfer instructions, and all transfer records are stored in the on-chain ledger.

[0055] The token inversion triggering and penalty execution module is used to determine whether a token inversion state has occurred based on the resource efficiency ratio of each university-enterprise cooperation entity, and to execute penalty operations if a state of inversion occurs. The system calculates the resource efficiency ratio of each university-enterprise cooperation entity monthly. Calculate using the following formula:

[0056] ;

[0057] In the formula, For school-enterprise cooperation The total number of infant and toddler growth tokens collected in month t; For school-enterprise cooperation The total number of resource points obtained in month t is calculated by adding up the number of training slots, the duration of teacher assignments, and the amount of financial subsidies according to preset coefficients.

[0058] The system performs quantile statistics on the resource efficiency ratio of all university-enterprise cooperation entities to obtain the lower quartile Q1(t). When the university-enterprise cooperation entity... When the resource efficiency ratio is lower than the lower quartile Q1(t) of the corresponding month for several consecutive months, the university-enterprise cooperation entity is marked as being in a token inversion state and recorded on the blockchain. During the penalty period, the university-enterprise cooperation entity in a token inversion state will have its resource matching weight reduced and its accumulated infant growth tokens deducted proportionally, with the deduction ratio increasing with the penalty month number.

[0059] The resource matching weight adaptive reallocation module calculates resource matching weights before the start of each calendar month based on the total number of valid infant growth tokens up to the previous month and whether the school-enterprise cooperation entity is in an inverted state. The total number of valid infant growth tokens is denoted as... The newly generated tokens are accumulated from token aggregation and deducted proportionally during the penalty period. The weight calculation is performed according to the following formula:

[0060] ;

[0061] In the formula, For school-enterprise cooperation The penalty factor is set to the penalty coefficient value when the system is inverted, and to 1 when it is not inverted. The system allocates the monthly training quota, teacher assignment quota, and financial subsidy amount according to the calculated weights.

[0062] Through the coordinated operation of the above modules, the system completes daily growth data recording, daily growth token minting, and monthly resource matching weight calculation; completes resource efficiency ratio statistics of school-enterprise cooperation entities, judgment of inverted status, and execution of penalties; and realizes the resource matching logic, token recording logic, and on-chain resource allocation logic of childcare industry-education integration based on verifiable on-chain data.

[0063] Furthermore, the specific process of the multi-source growth data collection and blockchain evidence storage module is as follows:

[0064] The system first generates a globally unique identifier for each infant;

[0065] Subsequently, four raw data streams are continuously received daily. Each stream of data is standardized and then weighted and merged to form a growth event vector for the day.

[0066] Then, the teacher, parent, and wearable device terminals complete the on-chain multi-signature process, and finally, the daily growth event vector and multi-signature are packaged and written into a new block of the consortium blockchain.

[0067] The specific process of creating the Infant Growth Token module is as follows:

[0068] The system initiates the minting process at a fixed time each day, reading the growth event vectors of each infant that have been uploaded to the blockchain that day, comparing them with the national benchmark value corresponding to that age month, calculating the positive incremental part and minting basic infant growth tokens. At the same time, additional achievement reward infant growth tokens are minted for infants who meet all dimensions. Finally, the total amount of newly added infant growth tokens on the entire network that day is recorded in the consortium blockchain ledger.

[0069] Specifically, the process of the multi-source growth data collection and blockchain evidence storage module is as follows: The system generates a globally unique identifier for each infant. This identifier can be formed by calling a random number generation algorithm or a serial number generation algorithm during the system initialization phase. The identifier can be represented as... Where i is the infant index, Maintain uniqueness within the consortium blockchain network and serve as an index key for its on-chain growth profile.

[0070] During daily operation, the system continuously receives four types of raw data from wearable devices, smart toy interactive terminals, teacher tablet terminals, and parent mobile terminals. Data from wearable devices may include indicators such as step count, body movement amplitude, sleep duration, and body surface temperature; data from smart toys may include interaction frequency, voice recognition results, and action response parameters; data from teacher tablets may include behavioral observation records and classroom activity performance scores; and data from parent terminals may include family behavior feedback, dietary records, and activity records.

[0071] The system standardizes the four types of data mentioned above. For any original data value x, the standardized value x' can be calculated using the following formula:

[0072] ;

[0073] In the formula, μ is the reference mean of this data dimension; This represents the standard deviation for this data dimension. Standardized data is mapped to a numerical space with a mean of 0 and a standard deviation of 1, used to eliminate dimensional differences between data from different sources.

[0074] The system merges the standardized data from the four sources according to preset weights to form a daily growth event vector. Fusion computing can be performed using the following formula:

[0075] ;

[0076] In the formula, A standardized data vector from wearable devices; Standardized data vectors from smart toys; Standardized data vectors recorded on teacher tablets; Standardized data vectors recorded for parental terminals; , , ,

[0077] These are the weighting coefficients for the four types of data mentioned above, and each weight satisfies... .

[0078] Daily Growth Event Vector After generation, the system generates signatures on the teacher's, parent's, and wearable device's ends. These three signatures can be implemented using the same on-chain signature algorithm, with each signature containing authentication information and a data digest. The system verifies the three signatures on-chain; once verified, the signature is generated. All signatures are combined into an on-chain data packet, which is written into a new block through the consortium blockchain consensus process, thus forming an immutable growth record.

[0079] The specific process of the infant growth token minting module is as follows: The system initiates the token minting process at a fixed time each day. The system reads the growth event vector of infant i on date t. and will The national benchmark growth vector for the infant's age group In comparison, among them, The age in months for infants and young children.

[0080] positive increment vector It can be calculated using the following formula:

[0081] ;

[0082] In the formula, This is the non-negative increment vector obtained after dimension-by-dimensional comparison; max() indicates taking the part greater than 0 according to the components.

[0083] Number of Basic Infant Growth Passes It can be calculated using the following formula:

[0084] ;

[0085] In the formula, The conversion factor for the basic token; Summing is performed on each dimension of the positive increment vector.

[0086] The system determines whether infant i has reached or exceeded the baseline value across all dimensions on date t. If the baseline value is met...

[0087] When the dimensional conditions are met, the system will create a reward token for infant growth, with a quantity... It can be calculated using the following formula:

[0088] ;

[0089] In the formula, β is the target reward coefficient, which is a preset fixed value.

[0090] Total number of Infant Growth Tokens Given by the following formula:

[0091] ;

[0092] The system will This module adds the total amount of newly added tokens to the network on the day of the transaction and records it in the consortium blockchain ledger through the token management contract. This module completes the initial definition of token creation, quantity registration, and token ownership on the blockchain; tokens cannot be created or modified repeatedly.

[0093] During the data processing and token minting process, the system verifies various intermediate variables and on-chain transaction data to ensure consistency of growth event vectors, positive increment calculation results, and token quantities on the chain. Modules within the system interact through predefined data interfaces, enabling a continuous processing flow from data collection and fusion to on-chain recording and token minting. The on-chain ledger stores growth event blocks, signature records, and a token ledger, providing a data foundation for subsequent token aggregation, resource efficiency ratio calculation, and resource matching weight redistribution.

[0094] Furthermore, the specific process of the token collection and profit-sharing module for the university-enterprise cooperation entity is as follows:

[0095] The system initiates a monthly data collection process at the beginning of each month, and compiles all infant growth passes generated by each infant in the previous month based on the correspondence between the actual service institution and the training institution for each infant.

[0096] A portion of the proceeds was then transferred directly to the corresponding childcare institution's account, while the other portion was transferred to the long-term dividend account of the vocational college from which the child graduated.

[0097] Specifically, the system identifies each infant and toddler in the childcare service. With the logo of the training institution ,in and All data is written to the blockchain during infant enrollment and remains unchanged. When the system initiates monthly data aggregation, it records the tokens of infant i within month t-1. Statistical analysis was conducted. The aggregation ratio can be denoted as γ, where γ is allocated to the childcare institution account and 1−γ is allocated to the long-term dividend account of higher vocational colleges.

[0098] Number of tokens corresponding to childcare institutions Determine using the following formula:

[0099] ;

[0100] Number of tokens corresponding to the training institutions Determine using the following formula:

[0101] ;

[0102] The system provides on-chain services for childcare institutions and training institutions The system executes transfer instructions to transfer the corresponding number of tokens to the respective on-chain accounts. Before executing each transfer instruction, the system checks the balance of the token source account to ensure the consistency of the values ​​on the on-chain ledger. After a successful transfer, the token management contract updates the holdings of the corresponding account.

[0103] After the system completes the above statistics and transfer for all infants and young children, it will then process the data from the school-enterprise cooperation entities. The total monthly token collection is accumulated. The system records the total amount of tokens collected by the university-enterprise cooperation entity according to the following formula. The token collection volume in month t :

[0104] ;

[0105] In the formula, This refers to the collection of all infants and toddlers jointly served or nurtured by childcare institution k and higher vocational college j.

[0106] After completing the aggregation process, the system writes the token aggregation results of the university-enterprise cooperation entity into the on-chain data structure. The on-chain content includes the cooperation entity identifier. Monthly t, Total amount collected Childcare facilities and the colleges and universities section The record writing is performed through the consortium blockchain consensus module. Once the write is successful, an immutable on-chain ledger entry is formed, providing a data foundation for subsequent resource efficiency ratio calculations, inverted state judgments, and resource allocation weight calculations.

[0107] During the data collection process, the system performs timestamp verification on cross-month token data to ensure that the statistical scope is limited to month t-1. Simultaneously, it verifies the account indexes of childcare institutions and vocational colleges to ensure that the token collection flow is consistent with the infant and toddler service relationship. The system uses a batch processing method to execute monthly collection tasks, processing them one by one according to the infant and toddler index order until the collection of all infants and toddlers is completed.

[0108] Through the above processing flow, the system realizes the circular collection and long-term dividend record of tokens among university-enterprise cooperation entities, providing basic data input for resource efficiency ratio statistics. This module connects with the on-chain token management contract to realize the precise definition, allocation, and on-chain recording of token quantity, ensuring that the token ledger has structural consistency and cross-month traceability.

[0109] Furthermore, the specific process of trigger judgment in the token inversion trigger and penalty execution module is as follows:

[0110] The system calculates the resource efficiency ratio of each school-enterprise cooperation entity for the previous month at the beginning of each month;

[0111] Subsequently, it is checked whether the resource efficiency ratio of the collaboration has been lower than the lower quartile of the entire network for several consecutive months. If the condition is met, the collaboration is immediately marked as a token inversion state on the consortium blockchain and broadcast to the entire network.

[0112] The specific process of penalty execution in the token inversion triggering and penalty execution module is as follows:

[0113] For several consecutive months after the token inversion state is triggered, the resource matching weight will be forcibly reduced and deducted from the historical accumulated infant growth tokens at a monthly time until the resource efficiency ratio recovers to above the network median or the penalty period ends naturally, at which point the token inversion state will be automatically lifted.

[0114] The automatic unwinding process for token inversion is as follows:

[0115] When the resource efficiency ratio of the school-enterprise cooperation entity recovers to above the median of the entire network in any month, the system immediately removes the token inversion mark of the school-enterprise cooperation entity on the consortium blockchain, restores its normal resource matching weight, and stops the deduction of subsequent historical accumulated infant growth tokens.

[0116] Specifically, the triggering and penalty execution module for token inversion works as follows: At the beginning of each month, the system calculates the resource efficiency ratio of each university-enterprise cooperation entity for the previous month. The resource efficiency ratio is denoted as... Calculate using the following formula:

[0117] ;

[0118] In the formula, For school-enterprise cooperation The total number of infant and toddler growth tokens collected in month t; For school-enterprise cooperation The total number of resource points obtained in month t is calculated based on the number of training slots for childcare institutions, the duration of teacher assignments, and the amount of government subsidies, using a preset conversion factor.

[0119] The system performs quantile statistics based on the resource efficiency ratio set R(t) of all university-enterprise cooperation entities in month t, calculating the lower quartile Q1(t). The system then analyzes the resource efficiency ratios of all university-enterprise cooperation entities. The system retrieves resource efficiency ratio records from the most recent M months, where M is a preset threshold for the number of consecutive months. The system then determines whether the following formula is satisfied:

[0120] ;

[0121] When all conditions are met, the system will [implement the school-enterprise cooperation mechanism]. The token is marked as being in an inverted state. This marking action is executed via on-chain state update instructions, which update the collaborator identifier. The system displays an inverted state flag. Upon successful update, the system broadcasts the state change information to all nodes via the consortium blockchain broadcast mechanism, enabling relevant modules to use the new inverted state in subsequent calculations.

[0122] The specific process of the token inversion triggering and penalty execution module in terms of penalty execution is as follows: For N consecutive months after the token inversion state is triggered, the system executes penalty operations on the university-enterprise cooperation entity during the monthly initialization phase. N is the preset penalty period length. The system applies penalties to university-enterprise cooperation entities in the inverted state. Perform a forced reduction operation on the resource matching weights, so that the calculated weights must be multiplied by a penalty factor. Penalty factor Defined by the following formula:

[0123] ;

[0124] In the formula, It is a fixed coefficient less than 1, which remains unchanged throughout the penalty period.

[0125] During the penalty period, the system also accumulates valid infant and toddler growth certificates for the school-enterprise cooperation entities. Deductions will be made. The deduction percentage is related to the month number currently in the penalty period; the system defines a deduction percentage for each penalty month m. And execute according to the following formula:

[0126] ;

[0127] in, This is the m-th month after the school-enterprise cooperation entity entered a state of inversion. This is a non-negative preset value that increases with m, ensuring the deduction amount increases monthly. Updated after deduction. Write it into the on-chain storage structure as input for the resource matching weight calculation in the next month.

[0128] The system determines monthly whether the university-enterprise cooperation entity meets the conditions for resolving the token inversion. The automatic resolution process for the token inversion state is as follows: when the university-enterprise cooperation entity... In any month t, when the resource efficiency ratio satisfies the following formula, the system performs an unwinding operation:

[0129] ;

[0130] In the formula, This represents the median resource efficiency ratio for all university-enterprise cooperation entities in month t. When this condition is met, the system updates the inverted flag of the cooperation entity to an uninverted state via an on-chain state update instruction. The system then synchronously stops deductions for subsequent months and uses this data when calculating resource matching weights for that month. .

[0131] For university-enterprise cooperation entities that fail to meet the conditions for lifting the penalty within the N-month penalty period, the system will automatically remove the inverted tag at the end of the N-month period, but will not restore the deducted cumulative valid tokens. The system records the lifting time t on the blockchain and records the final cumulative valid token amount in the ledger to ensure that the subsequent resource allocation process is executed based on accurate stock data.

[0132] Throughout the entire process of triggering judgments, executing penalties, and resolving inversions, the system maintains the university-enterprise cooperation entity through on-chain state variables. Inverted status, penalty month number m, deduction ratio and penalty factors The on-chain storage uses a key-value structure, where the key consists of the collaborator identifier and the month, and the value is the efficiency ratio, status identifier, and cumulative token count for the corresponding month. This structure ensures that the system can directly obtain the status and parameters of each collaborator based on on-chain data during cross-month operations, providing complete data input for the resource matching and weight redistribution module.

[0133] Furthermore, the specific process of the resource matching weight adaptive reallocation module is as follows:

[0134] The system starts the weight calculation process at a set time at the beginning of each month.

[0135] First, we will tally the total number of valid infant and toddler growth passes accumulated by each school-enterprise cooperation entity up to last month, after deductions.

[0136] Then, the penalty factor is determined based on whether the token is in an inverted state, and the final weight is calculated.

[0137] Subsequently, the allocation of all training slots, faculty assignment slots, and government subsidy slots for the month will be automatically completed according to the final weighting ratio and pushed to each school-enterprise cooperation entity.

[0138] The process for maintaining the total number of valid Infant Growth Tokens is as follows:

[0139] The system accumulates newly acquired infant growth tokens in real time during each monthly collection and subtracts the deducted infant growth tokens in real time during each penalty execution, thereby ensuring that the total amount of cumulative valid infant growth tokens used for weight calculation at any time is the actual stock after deduction.

[0140] Specifically, the resource matching weight adaptive reallocation module works as follows: The system starts the weight calculation process at regular intervals at the beginning of each month. The system first reads the total number of valid infant growth tokens accumulated by each school-enterprise cooperation entity up to the previous month. This value is maintained in the on-chain storage structure and updated according to the penalty result during the penalty execution phase.

[0141] The system assesses the inverted status of all university-enterprise cooperation entities to determine the penalty factor to be applied in the current month. For university-enterprise cooperation entities in a normal status, the penalty factor is... The value is 1. For university-industry cooperation entities in an inverted state, the penalty factor is... The value is set to the preset penalty coefficient. It is a fixed parameter less than 1, set through an on-chain contract.

[0142] The system calculates the intermediate weighted value of the university-enterprise cooperation entity based on the cumulative valid tokens and penalty factors. Determine using the following formula:

[0143] ;

[0144] In the formula, For school-enterprise cooperation The cumulative number of valid tokens at the end of month t−1; The penalty factor for the school-enterprise cooperation entity in month t.

[0145] The system sums the intermediate weighted values ​​of all university-enterprise cooperation entities to obtain the normalized denominator:

[0146] ;

[0147] like If the value is greater than 0, the system calculates the industry-university cooperation body according to the following formula. Resource matching weight in month t :

[0148] ;

[0149] income The values ​​are between 0 and 1, and the sum of the weights of all university-enterprise cooperation entities is 1. The system will... This serves as the basis for determining the resource allocation ratio for the current month.

[0150] System basis Allocate resources proportionally for the current month. Let the total number of training slots available in month t be... The total number of teachers to be dispatched is The total number of government subsidy slots is

[0151] The system calculates the university-enterprise cooperation entities according to the following formulas. Resource allocation quantity:

[0152] ;

[0153] ;

[0154] ;

[0155] The system writes the allocation results of the above three types of resources into the on-chain resource allocation record structure. The on-chain write content includes the collaborator identifier, month, the allocation quantity of the three types of resources, and the data used for weight calculation. , and The write operation is performed through the consortium blockchain consensus mechanism, making the resource allocation record immutable.

[0156] After resource allocation is completed, the system pushes the monthly resource allocation results to childcare institutions and vocational colleges via an interface. The push content includes the quantity of each type of resource, the resource distribution time, and the on-chain record index. The receiving end can verify the resource allocation status through the on-chain records.

[0157] The process for maintaining the total number of valid infant and toddler growth tokens is as follows: After each monthly collection operation, the system reads the data from the school-enterprise cooperation entity. The number of new tokens acquired in month t Update the cumulative valid tokens as follows:

[0158] ;

[0159] During months when the industry-university cooperation entity is under penalty, the system will execute the penalty deduction operation according to the set deduction ratio. Update the cumulative valid tokens. The cumulative valid tokens after deduction are obtained using the following formula:

[0160] ;

[0161] In the formula, m represents the month number during which the school-enterprise cooperation entity is in the penalty period. This represents the deduction percentage for that month, a non-negative value that can increase according to preset rules.

[0162] After all update operations are complete, the system will display the updated version. Write the token to the on-chain token storage structure, replacing the previous month's record. The on-chain storage key contains the collaborator identifier. With month t, the stored value contains the cumulative valid tokens. Number of new tokens and deducted quantity This structure ensures that the system can obtain the accurate cumulative valid token inventory at any time, which serves as the input parameter for the weight calculation of the next month.

[0163] During the statistical, calculation, and writing operations described above, the system verifies the input data, including verifying the token source, token quantity, penalty factor value range, and total resource amount. Through interaction between on-chain and off-chain modules, the system ensures numerical consistency, temporal correctness, and storage traceability in the resource matching process. Through the aforementioned calculations and on-chain recordings, an automated resource matching weight allocation mechanism based on accumulated valid tokens and penalty status is implemented.

[0164] Furthermore, the calculation process for the resource efficiency ratio is as follows:

[0165] The system automatically obtains the total number of infant growth certificates actually collected by each school-enterprise cooperation entity in the previous month as the numerator, and the total number of resource points converted from all resources actually obtained by the school-enterprise cooperation entity in the previous month, such as training quotas, teacher assignments, and financial subsidies, as the denominator. The two are divided to obtain the resource efficiency ratio of the school-enterprise cooperation entity in the current month.

[0166] Specifically, the system provides each school-enterprise cooperation entity with... Maintain the token collection volume for month t This value is written to the on-chain ledger by the monthly token collection module and serves as input data for this module's calculations. The system also maintains the amount of resources acquired by the university-enterprise cooperation entity in month t, including training slots. Teacher deployment duration and the amount of government subsidies In order to convert different forms of resources into the same measurement system, the system uses resource conversion factors to convert the three types of resources into resource points.

[0167] Resource Points Calculate using the following formula:

[0168] ;

[0169] In the formula,

[0170] This is the conversion factor for the number of practical training slots;

[0171] A conversion factor for the duration of teacher assignments;

[0172] These are the conversion factors for fiscal subsidies. All three conversion factors mentioned above are non-negative real numbers and can be set according to regional policies or resource costs.

[0173] The system obtains Then, calculate the resource efficiency ratio. It is defined by the following formula:

[0174] ;

[0175] in, , If the resource points for a certain month If the value is 0, the system will mark the resource efficiency ratio corresponding to that month as invalid according to the preset rules, record the marking status on the chain, and exclude that month from the statistical scope of subsequent inversion judgment.

[0176] Before executing the above calculation process, the system performs integrity verification on the input data, including verifying whether the token collection amount has a corresponding record on the blockchain, verifying whether the resource item has a valid record in the resource management module, and verifying whether the value meets the preset input range. After completing the calculation, the system will... The calculation results are written to the on-chain state storage structure. The written content includes the collaborator identifier. Monthly t, resource efficiency ratio numerator Resource efficiency ratio denominator and the calculated The write operation is executed through the consortium blockchain consensus process, making resource efficiency superior to the immutable property of records.

[0177] The system performs time-series management on the calculated resource efficiency ratio data, indexing it by month. The value is stored in an on-chain key-value structure, allowing subsequent modules to retrieve it by month. The system uses resource efficiency ratios from multiple consecutive months in the inversion trigger judgment module; therefore, it's crucial to ensure that cross-month records are directly searchable. In the on-chain structure, the key can include the collaborator identifier and the month index, while the value is the resource efficiency ratio for the corresponding month. .

[0178] After recording the resource efficiency ratio, the system will aggregate the dataset R(t) of all university-enterprise cooperation entities for the inversion judgment module to perform quantile statistics. Through the above structured recording method, the system ensures that the calculation process of the resource efficiency ratio is traceable and verifiable, providing accurate input for token inversion judgment and resource matching weight calculation.

[0179] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-employment child care resource matching and quality evaluation system, characterized in that, Includes the following modules: The multi-source growth data collection and blockchain notarization module is used to generate a unique identity for each child enrolled in the care facility. It collects multi-dimensional growth data from wearable devices, smart toy interactions, teacher tablet records, and parent APP feedback in real time every day. After being signed by multiple parties, the data is written into the consortium blockchain to form an immutable growth archive block. The Infant Growth Token Minting Module is used to mint non-renewable Infant Growth Tokens at fixed times each day based on the positive increment of each infant's multidimensional growth data relative to the national benchmark value for that month and whether the standard has been met. The "School-Enterprise Cooperation Token Collection and Profit Sharing" module is used to directly collect a portion of the infant growth tokens minted by all infants and toddlers actually served by the same school-enterprise cooperation entity in the current month to the childcare institution, and another portion to the corresponding higher vocational college to form long-term profit sharing. The token inversion trigger and penalty execution module is used to calculate the resource efficiency ratio of each school-enterprise cooperation entity every month. When the resource efficiency ratio is lower than the lower quartile of the entire network for several consecutive months, the token inversion state is automatically triggered, and the resource matching weight is forcibly reduced and the historically accumulated infant growth tokens are deducted at an increasing monthly rate during the subsequent penalty period. The resource matching weight adaptive reallocation module is used to automatically recalculate and execute the allocation weights of training quotas, teacher assignments, and financial subsidies before the start of each natural month, based on the total number of valid infant growth tokens of each school-enterprise cooperation entity up to the previous month and whether it is in a token inversion state.

2. The high-employment child care resource matching and quality evaluation system according to claim 1, characterized in that, The specific process of the multi-source growth data acquisition and blockchain evidence storage module is as follows: The system first generates a globally unique identifier for each infant; Subsequently, four raw data streams are continuously received daily. Each stream of data is standardized and then weighted and merged to form a growth event vector for the day. Then, the teacher, parent, and wearable device terminals complete the on-chain multi-signature process, and finally, the daily growth event vector and multi-signature are packaged and written into a new block of the consortium blockchain.

3. The high-employment child care resource matching and quality evaluation system according to claim 2, characterized in that, The specific process of the infant growth token casting module is as follows: The system initiates the minting process at a fixed time each day, reading the growth event vectors of each infant that have been uploaded to the blockchain that day, comparing them with the national benchmark value corresponding to that age month, calculating the positive incremental part and minting basic infant growth tokens. At the same time, additional achievement reward infant growth tokens are minted for infants who meet all dimensions. Finally, the total amount of newly added infant growth tokens on the entire network that day is recorded in the consortium blockchain ledger.

4. The vocational college childcare industry-education integration resource matching and quality evaluation system according to claim 1, characterized in that, The specific process of the token collection and profit-sharing module for the university-enterprise cooperation entity is as follows: The system initiates a monthly data collection process at the beginning of each month, and compiles all infant growth passes generated by each infant in the previous month based on the correspondence between the actual service institution and the training institution for each infant. A portion of the proceeds was then transferred directly to the corresponding childcare institution's account, while the other portion was transferred to the long-term dividend account of the vocational college from which the child graduated.

5. The vocational college childcare industry-education integration resource matching and quality evaluation system according to claim 1, characterized in that, The specific process of trigger judgment in the token inversion triggering and penalty execution module is as follows: The system calculates the resource efficiency ratio of each school-enterprise cooperation entity for the previous month at the beginning of each month; Subsequently, it is checked whether the resource efficiency ratio of the collaboration has been lower than the lower quartile of the entire network for several consecutive months. If the condition is met, the collaboration is immediately marked as a token inversion state on the consortium blockchain and broadcast to the entire network.

6. The vocational college childcare industry-education integration resource matching and quality evaluation system according to claim 5, characterized in that, The specific process of the token inversion triggering and penalty execution module in terms of penalty execution is as follows: For several consecutive months after the token inversion state is triggered, the resource matching weight will be forcibly reduced and deducted from the historical accumulated infant growth tokens at a monthly time until the resource efficiency ratio recovers to above the network median or the penalty period ends naturally, at which point the token inversion state will be automatically lifted.

7. The vocational college childcare industry-education integration resource matching and quality evaluation system according to claim 1, characterized in that, The specific process of the resource matching weight adaptive reallocation module is as follows: The system starts the weight calculation process at a set time at the beginning of each month. First, we will tally the total number of valid infant and toddler growth passes accumulated by each school-enterprise cooperation entity up to last month, after deductions. Then, the penalty factor is determined based on whether the token is in an inverted state, and the final weight is calculated. Subsequently, the allocation of all training slots, faculty assignment slots, and government subsidy slots for the month will be automatically completed according to the final weighting ratio and pushed to each school-enterprise cooperation entity.

8. The vocational college childcare industry-education integration resource matching and quality evaluation system according to claim 7, characterized in that, The process for maintaining the total number of valid infant growth tokens is as follows: The system accumulates newly acquired infant growth tokens in real time during each monthly collection and subtracts the deducted infant growth tokens in real time during each penalty execution, thereby ensuring that the total amount of cumulative valid infant growth tokens used for weight calculation at any time is the actual stock after deduction.

9. A resource matching and quality evaluation system for vocational college childcare industry-education integration as described in claim 6, characterized in that, The automatic unwinding process for the token inverted state is as follows: When the resource efficiency ratio of the school-enterprise cooperation entity recovers to above the median of the entire network in any month, the system immediately removes the token inversion mark of the school-enterprise cooperation entity on the consortium blockchain, restores its normal resource matching weight, and stops the deduction of subsequent historical accumulated infant growth tokens.

10. A resource matching and quality evaluation system for vocational college childcare industry-education integration as described in claim 1, characterized in that, The calculation process for the resource efficiency ratio is as follows: The system automatically obtains the total number of infant growth certificates actually collected by each school-enterprise cooperation entity in the previous month as the numerator, and the total number of resource points converted from all resources actually obtained by the school-enterprise cooperation entity in the previous month as the denominator. The two are divided to obtain the resource efficiency ratio of the school-enterprise cooperation entity in the current month.

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