A temperature and humidity control system for drying vermicelli

Abstract

The application belongs to the field of non-electric variable control, and specifically discloses a temperature and humidity control system for drying vermicelli, which comprises an environment feature extraction module, a device adjustment module and a process fusion module. The environment feature extraction module is used for collecting real-time temperature and humidity data in a drying environment through a distributed sensor network and extracting an environment feature vector in combination with external meteorological data. The device adjustment module is used for mapping and processing drying device operation parameters according to the environment feature vector to generate a device adjustment instruction sequence. The process fusion module is used for extracting a process parameter change trend from historical drying records and fusing the process parameter and the current environment feature vector by using a time series analysis method to obtain a comprehensive regulation vector. The interference evaluation module is used for calling a real-time data interface to obtain external environmental interference factors through an edge computing node. The application aims to solve the problems of inaccurate temperature and humidity regulation, data response lag and insufficient linkage between environmental variables and devices in the drying process of vermicelli.

Description

Technical Field

[0001] This invention relates to the field of variable control technology, specifically to a temperature and humidity control system for drying vermicelli. Background Technology

[0002] In the production process of drying vermicelli, the core technical challenge lies in achieving precise control of temperature and humidity to ensure a balance between drying efficiency and product quality. This involves real-time monitoring and adjustment of dynamic changes in temperature and humidity within the drying environment. However, traditional methods may suffer from response lag or inaccurate parameter adjustments when dealing with complex environmental variables, making it difficult to achieve the desired drying effect and affecting the consistency of the finished product quality.

[0003] In addition, when converting temperature and humidity data into control commands and linking them with the equipment's operating status, insufficient sensor accuracy or poor compatibility of the control system may lead to parameter deviations and reduce the accuracy of regulation.

[0004] Furthermore, when introducing external environmental data to assist decision-making, there is a matching problem between the data collection time interval and the actual drying demand. If the sampling frequency is too low or the transmission delay is too large, it may affect the timeliness of the environmental data, thereby weakening the reliability of comprehensive regulation.

[0005] Ultimately, when the temperature and humidity control logic is executed through the automated system, the algorithm design may result in the control results failing to fully meet the process requirements due to unreasonable weight allocation or numerous interference factors. Furthermore, when tracing the control path, the system's recorded operation logs may contain incomplete information if data synchronization is not timely or storage is scattered, affecting the credibility of optimization and improvement.

[0006] This comprehensive issue spans the entire process from data acquisition and parameter transformation to dynamic control, involving complex interactions between environmental perception, data integration, real-time assurance, and system storage, and directly affects the efficiency and quality stability of vermicelli drying. Summary of the Invention

[0007] This invention provides a temperature and humidity control system for drying rice noodles, aiming to solve the technical problems of inaccurate temperature and humidity control, delayed data response, and insufficient linkage between environmental variables and equipment during the rice noodle drying process. To solve the above technical problems, the technical solution adopted by this invention is:

[0008] A temperature and humidity control system for drying vermicelli includes: an environmental feature extraction module for collecting real-time temperature and humidity data in the drying environment through a distributed sensor network and extracting an environmental feature vector by combining it with external meteorological data; an equipment adjustment module for mapping the operating parameters of the drying equipment according to the environmental feature vector to generate a sequence of equipment adjustment instructions; a process fusion module for extracting the trend of process parameter changes from historical drying records and fusing the process parameters with the current environmental feature vector using time series analysis to obtain a comprehensive control vector; an interference assessment module for obtaining external environmental interference factors by calling a real-time data interface through an edge computing node, determining whether the intensity of the interference factors exceeds a preset threshold, and if not, determining that the environmental stability meets the requirements; a dynamic scoring module for performing multi-dimensional weighted calculations based on the comprehensive control vector and the environmental stability results to generate a dynamic control score for the drying process; an execution trigger module for triggering the equipment adjustment logic through an automated execution unit if the dynamic control score is higher than a preset threshold, distributing the control process records using an encryption algorithm to generate an immutable operation log; and a path verification module for retrieving relevant control paths from the distributed storage network based on the immutable operation log, determining the path integrity, and if complete, determining the final output of the control result.

[0009] In one aspect of this disclosure, the steps of collecting real-time temperature and humidity data in a dry environment through a distributed sensor network and extracting environmental feature vectors in conjunction with external meteorological data include: obtaining raw data uploaded by temperature and humidity sensor nodes from the distributed sensor network and obtaining a timestamp-aligned temperature and humidity dataset using a data synchronization protocol; obtaining real-time meteorological information released by meteorological stations through an external meteorological data interface and extracting air pressure, wind speed, and precipitation fields using data parsing techniques to generate meteorological feature vectors; normalizing the temperature and humidity dataset and meteorological feature vectors, and using principal component analysis to reduce the dimensionality of multidimensional features to generate an initial environmental feature vector; if the dimensionality of the initial environmental feature vector is higher than a preset threshold, further optimizing the feature dimension using linear discriminant analysis to generate an optimized environmental feature vector; classifying the dry environment using a support vector machine algorithm based on the optimized environmental feature vector to generate environmental state labels; and generating a structured environmental description by matching the drying conditions using the environmental state labels and a preset process requirement template.

[0010] In one aspect of this disclosure, the step of mapping the operating parameters of a drying device based on an environmental feature vector to generate a sequence of device adjustment instructions includes: obtaining an environmental feature vector and extracting key feature values ​​from it; generating a feature priority list using a feature weight allocation method; initializing the operating parameters of the drying device; associating the feature priority list with the device parameters using parameter mapping rules to generate preliminary adjustment instructions; optimizing the preliminary adjustment instructions using a fuzzy logic algorithm to generate an optimized sequence of adjustment instructions; if there are conflicting instructions in the optimized sequence of adjustment instructions, identifying conflict points using a conflict detection algorithm, eliminating conflicts using a priority sorting method, and generating a final sequence of adjustment instructions; verifying the execution effect of the instructions using simulation technology for the final sequence of adjustment instructions and generating an execution effect evaluation report; and adjusting the parameter range of the sequence of adjustment instructions based on the execution effect evaluation report to generate adaptive adjustment instructions.

[0011] In one aspect of this disclosure, the steps of extracting process parameter variation trends from historical drying records and fusing process parameters with current environmental feature vectors using time series analysis to obtain a comprehensive control vector include: extracting process parameter data from a historical drying record database; segmenting the data using a sliding window algorithm to generate time series segments; fitting the time series segments to a trend using time series analysis, generating a fitting curve using the least squares method, and extracting the process parameter variation trends; fusing the process parameter variation trends with current environmental feature vectors, and generating a preliminary comprehensive control vector using a weighted average method; if the fluctuation range of the preliminary comprehensive control vector exceeds a preset threshold, smoothing the vector using a Kalman filter algorithm to generate an optimized comprehensive control vector; classifying drying conditions using cluster analysis based on the optimized comprehensive control vector to generate process condition labels; and generating a validation dataset for the comprehensive control vector using the process condition labels and the distribution of abnormal data in historical records.

[0012] In one aspect of this disclosure, the steps of obtaining external environmental interference factors by calling a real-time data interface through an edge computing node, determining whether the intensity of the interference factors exceeds a preset threshold, and determining that the environmental stability meets the requirements if it does not exceed the threshold, include: obtaining interference factor data from external environmental monitoring equipment through a real-time data interface; removing noise data using data cleaning techniques to generate a clean dataset; quantifying the intensity of interference factors in the clean dataset and generating an interference intensity index using a standardization method; comparing the interference intensity index with a preset interference threshold; generating an environmental stability judgment result if the interference intensity index is lower than the interference threshold; obtaining supplementary external environmental data by calling the real-time data interface based on the environmental stability judgment result; verifying the reliability of the supplementary data using a data integrity verification algorithm to generate a verified supplementary dataset; predicting future interference trends using regression analysis based on the verified supplementary dataset to generate an interference prediction model; and generating an external environmental interference assessment report by combining the interference prediction model with the environmental stability judgment result.

[0013] In one aspect of this disclosure, the step of generating a dynamic control score for the drying process by performing multi-dimensional weighted calculations based on a comprehensive control vector and environmental stability results includes: obtaining the comprehensive control vector and environmental stability results; normalizing the data of each dimension according to a preset weight allocation rule to generate a normalized dataset; determining the weight coefficients of each dimension using the analytic hierarchy process (AHP) based on the normalized dataset to generate a weight allocation matrix; if the weight coefficient of a certain dimension in the weight allocation matrix is ​​higher than a preset upper limit, redistributing the weights using a weight adjustment algorithm to generate an adjusted weight allocation matrix; calculating the normalized dataset using a weighted summation method based on the adjusted weight allocation matrix to generate a preliminary control score; iteratively calculating the preliminary control score using a nonlinear optimization algorithm to generate an optimized control score; and generating a dynamic control score for the drying process by combining the optimized control score with a preset scoring standard.

[0014] In one aspect of this disclosure, if the dynamic control score exceeds a preset threshold, the following steps are taken: triggering equipment adjustment logic through an automated execution unit, distributing the control process records using an encryption algorithm, and generating an immutable operation log; if the dynamic control score exceeds the preset threshold, obtaining the equipment adjustment instruction to be executed through the triggering logic of the automated execution unit, performing the adjustment operation, and generating an adjustment result; encrypting the adjustment result and the control process records using an encryption algorithm to generate an encrypted data block; writing the encrypted data block to multiple storage nodes through a distributed storage network to generate a storage address list; obtaining the encrypted data block in the storage node according to the storage address list, verifying the data integrity using a data integrity verification algorithm, and generating an integrity verification result; if the integrity verification result is complete, generating an operation log through the distributed storage network to record the adjustment operation and storage information; synchronizing log data among network nodes using a consensus algorithm based on the operation log content to generate a synchronization confirmation; obtaining feedback information from the network nodes through the synchronization confirmation to generate the final operation log status.

[0015] In one aspect of this disclosure, the steps of retrieving relevant control paths from a distributed storage network based on immutable operation logs, determining path integrity, and if complete, determining the final output of the control result include: obtaining operation logs from the distributed storage network; extracting a log dataset containing timestamps and operation hashes using a distributed query protocol to generate original operation logs; parsing the log structure of the original operation logs, extracting node sequences and hash chains from the control paths, and generating path datasets; if the node sequences in the path datasets are consistent with a preset distributed storage topology, calculating the hash values ​​of each node in the path using a hash tree verification algorithm to determine path integrity and generate integrity verification results; based on the integrity verification results, if the integrity verification passes, extracting operation timestamps and associated metadata from the path datasets to generate review datasets; performing hash calculations on the review datasets using the SHA-3 algorithm to generate review hash values ​​and determine the uniqueness of the review results; and comparing the review hash values ​​with preset audit standards through a communication protocol to determine whether the review results meet the standards and generate the final control result.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] This invention utilizes a distributed sensor network to collect real-time temperature and humidity data in the drying environment. Combined with external meteorological information, it constructs an environmental feature vector, enabling precise perception and dynamic response to drying conditions. Based on this environmental feature vector, it intelligently maps and optimizes equipment operating parameters, significantly improving the accuracy and response speed of temperature and humidity control. By fusing historical process parameters with real-time environmental data to generate a comprehensive control vector, the system can adapt to the process requirements of different drying stages. Edge computing nodes are introduced to evaluate and predict external interference factors in real time, effectively enhancing the system's anti-interference capability and stability. Multi-dimensional weighted calculations generate dynamic control scores, and automated execution units achieve precise equipment control, ensuring the intelligence and consistency of the drying process. Encryption algorithms and distributed storage technology are used to construct an immutable operation log, achieving full-process traceability and transparent management. Finally, path integrity verification ensures reliable output of control results. This system effectively solves the problems of inaccurate temperature and humidity control, delayed response, and poor environmental adaptability in traditional vermicelli drying processes, significantly improving drying efficiency and product quality consistency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a temperature and humidity control system for drying rice noodles according to the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments described. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the protection scope of the present invention.

[0021] Please see Figure 1 As shown in the figure, this embodiment discloses a temperature and humidity control system for drying vermicelli. The system is implemented by the following method, and the specific steps are as follows:

[0022] S101. Collect real-time temperature and humidity data in a dry environment through a distributed sensor network, and extract environmental feature vectors by combining them with external meteorological data.

[0023] Raw data uploaded by temperature and humidity sensor nodes from a distributed sensor network is acquired, and a timestamp-aligned temperature and humidity dataset is obtained using a data synchronization protocol. Real-time meteorological information released by weather stations is obtained through an external meteorological data interface, and data parsing techniques are used to extract fields of air pressure, wind speed, and precipitation to generate meteorological feature vectors. The temperature and humidity dataset and meteorological feature vectors are normalized, and principal component analysis is used to reduce the dimensionality of multidimensional features to generate an initial environmental feature vector. If the dimensionality of the initial environmental feature vector is higher than a preset threshold, linear discriminant analysis is used to further optimize the feature dimensionality to generate an optimized environmental feature vector. Based on the optimized environmental feature vector, a support vector machine algorithm is used to classify the dry environment and generate environmental state labels. Using the environmental state labels and a preset process requirement template, drying conditions are matched to generate a structured environmental description.

[0024] For example, assume that multiple temperature and humidity sensor nodes are arranged inside the drying chamber, installed in the top, middle, and bottom areas of the drying chamber to ensure comprehensive data coverage. Each sensor node uploads the collected data to the central processing unit via wireless communication technology, and timestamps the data using the NTP time synchronization protocol to generate a temperature and humidity dataset, for example, temperature data ranging from 25℃ to 35℃, and humidity data ranging from 40% to 60%. Simultaneously, an external meteorological data interface obtains real-time meteorological information from a weather station via HTTP protocol, parsing out air pressure of 101.3 kPa, wind speed of 3.5 m / s, and precipitation of 0 mm to generate a meteorological feature vector. Subsequently, the system performs Min-Max normalization on the temperature and humidity dataset and the meteorological feature vector, and reduces the data dimension from 10 dimensions to 3 dimensions using principal component analysis, generating an initial environmental feature vector. If the initial environmental feature vector has a dimension of 5 dimensions, exceeding the preset threshold of 3 dimensions, then the feature dimension is further optimized using linear discriminant analysis, ultimately generating an optimized environmental feature vector, for example, [0.75, 0.62, 0.58]. Based on the optimized environmental feature vector, the support vector machine algorithm is used to classify the drying environment and generate environmental status labels such as "suitable for drying". Combined with the process requirement template, the drying conditions are matched to generate a structured environmental description, such as "temperature set at 30℃, humidity set at 50%".

[0025] S102. Based on the environmental feature vector, the operating parameters of the drying equipment are mapped to generate a sequence of equipment adjustment instructions.

[0026] The process involves acquiring environmental feature vectors, extracting key feature values, and generating a feature priority list using a feature weighting method. Initial configuration of the drying equipment's operating parameters is then performed, and parameter mapping rules are used to associate the feature priority list with the equipment parameters, generating preliminary adjustment instructions. These preliminary adjustment instructions are then optimized using a fuzzy logic algorithm, generating an optimized adjustment instruction sequence. If conflicting instructions exist in the optimized sequence, a conflict detection algorithm is used to identify conflict points, and a priority ranking method is employed to eliminate these conflicts, generating a final adjustment instruction sequence. For the final adjustment instruction sequence, simulation technology is used to verify the instruction execution effect, generating an execution effect evaluation report. Based on the execution effect evaluation report, the parameter range of the adjustment instruction sequence is adjusted to generate adaptive adjustment instructions.

[0027] For example, assuming the environmental feature vector contains a temperature fluctuation feature value of 0.75 and a humidity change feature value of 0.62, a feature priority list is generated using a feature weight allocation method, for example, a temperature weight of 0.6 and a humidity weight of 0.4. Subsequently, the operating parameters of the drying equipment are initialized, for example, the heater power is initially set to 5kW and the fan speed to 1000rpm. The feature priority list is then associated with the equipment parameters to generate preliminary adjustment instructions, such as "increase the heater power to 6kW and decrease the fan speed to 800rpm". The preliminary adjustment instructions are optimized using a fuzzy logic algorithm, taking into account equipment response delay and energy consumption factors, to generate an optimized sequence of adjustment instructions. If conflicts exist in the optimized instruction sequence, such as simultaneously requiring an increase in temperature and a decrease in humidity that the equipment cannot simultaneously meet, a conflict detection algorithm is used to identify the conflict points, and a priority ranking method is employed to eliminate the conflicts, ultimately generating the final sequence of adjustment instructions. In addition, the execution effect of the instructions is verified by simulation technology, and an execution effect evaluation report is generated, such as "temperature adjustment response time 2 seconds, humidity fluctuation range ±2%", and the parameter range of the adjustment instruction sequence is adjusted accordingly to generate adaptive adjustment instructions, such as "heater power range 5-7kW, fan speed range 800-1200rpm".

[0028] S103. Extract the trend of process parameter changes from historical drying records, and use time series analysis to fuse process parameters with current environmental feature vectors to obtain a comprehensive control vector.

[0029] Process parameter data is extracted from a historical drying record database. A sliding window algorithm is used to segment the data, generating time series segments. Time series analysis methods are used to fit the trends of these segments, and the least squares method is used to generate fitted curves, extracting the trends in process parameter changes. These trends are then fused with current environmental feature vectors, and a weighted average method is used to generate a preliminary comprehensive control vector. If the fluctuation range of the preliminary comprehensive control vector exceeds a preset threshold, a Kalman filter algorithm is used to smooth the vector, generating an optimized comprehensive control vector. Based on the optimized comprehensive control vector, cluster analysis is used to classify drying conditions, generating process condition labels. Using these process condition labels, combined with the distribution of abnormal data in historical records, a validation dataset for the comprehensive control vector is generated.

[0030] For example, process parameter data from the past 100 drying processes, including temperature, humidity, and drying time, are extracted from a historical drying record database. A sliding window algorithm with a window size of 10 is used to segment the data, generating time series segments. Time series analysis methods are used to fit the trends of these segments, and the least squares method is used to generate fitted curves to extract the changing trends of the process parameters; for example, the temperature trend equation is y = 0.5x + 25. The changing trends of the process parameters are fused with the current environmental feature vector, and a weighted average method is used to generate a preliminary comprehensive control vector, for example, [0.70, 0.65, 0.60]. If the fluctuation range of the preliminary comprehensive control vector exceeds a preset threshold of ±0.1, the vector is smoothed using a Kalman filter algorithm to generate an optimized comprehensive control vector, for example, [0.68, 0.66, 0.61]. Based on the optimized comprehensive control vector, cluster analysis is used to classify the drying conditions, generating process condition labels such as "rapid drying mode." Combined with the distribution of abnormal data in historical records, such as humidity anomalies, a validation dataset for the comprehensive control vector is generated for subsequent control validation.

[0031] S104. Obtain external environmental interference factors by calling the real-time data interface through the edge computing node, and determine whether the intensity of the interference factors exceeds the preset threshold. If it does not exceed the threshold, it is determined that the environmental stability meets the requirements.

[0032] Interference factor data is acquired from external environmental monitoring equipment via a real-time data interface. Noise is removed using data cleaning techniques to generate a clean dataset. The intensity of interference factors in the clean dataset is quantified, and a standardization method is used to generate an interference intensity index. The interference intensity index is compared with a preset interference threshold. If the interference intensity index is lower than the threshold, an environmental stability assessment result is generated. Based on the environmental stability assessment result, supplementary external environmental data is obtained by calling the real-time data interface. The reliability of the supplementary data is verified using a data integrity verification algorithm, generating a verified supplementary dataset. Based on the verified supplementary dataset, regression analysis is used to predict future interference trends, generating an interference prediction model. Finally, an external environmental interference assessment report is generated using the interference prediction model and the environmental stability assessment result.

[0033] For example, interference factor data, such as a sudden temperature change from 25°C to 30°C, is acquired from external environmental monitoring equipment via a real-time data interface. Noise is removed using data cleaning techniques to generate a clean dataset. The intensity of interference factors in the clean dataset is quantified, and a standardization method is used to generate an interference intensity index, such as a temperature change intensity of 0.8. The interference intensity index is compared with a preset interference threshold of 0.9. Since 0.8 is lower than 0.9, an environmental stability judgment result of "stable" is generated. Based on the environmental stability judgment result, supplementary external environmental data, such as humidity change data, is obtained by calling the real-time data interface. The data integrity is verified using the SHA-256 algorithm, generating a verified supplementary dataset. Based on the verified supplementary dataset, regression analysis is used to predict future interference trends, generating an interference prediction model. For example, a linear regression model predicts a future temperature change rate of 0.1°C / min. Combined with the environmental stability judgment result, an external environmental interference assessment report is generated for subsequent dynamic scoring.

[0034] S105. Based on the comprehensive control vector and environmental stability results, perform multi-dimensional weighted calculations to generate dynamic control scores for the drying process.

[0035] The process involves obtaining comprehensive control vectors and environmental stability results, normalizing the data for each dimension using pre-defined weight allocation rules to generate a normalized dataset, and determining the weight coefficients for each dimension using the analytic hierarchy process (AHP) to generate a weight allocation matrix. If the weight coefficient of a certain dimension in the weight allocation matrix exceeds a pre-defined upper limit, the weights are reallocated using a weight adjustment algorithm to generate an adjusted weight allocation matrix. Based on the adjusted weight allocation matrix, a weighted summation method is used to calculate the normalized dataset to generate a preliminary control score. A non-linear optimization algorithm is then used to iteratively calculate the preliminary control score to generate an optimized control score. Finally, the optimized control score, combined with pre-defined scoring criteria, generates a dynamic control score for the drying process.

[0036] For example, the comprehensive control vector [0.68, 0.66, 0.61] and the environmental stability result "stable" are obtained. The data for each dimension are normalized according to a preset weight allocation rule to generate a normalized dataset, such as [0.75, 0.70, 0.65]. Based on the normalized dataset, the weight coefficients of each dimension are determined using the analytic hierarchy process (AHP) to generate a weight allocation matrix, for example, temperature weight 0.5, humidity weight 0.3, and stability weight 0.2. If the temperature weight (0.5) in the weight allocation matrix is ​​higher than the preset upper limit of 0.45, the weights are reallocated using a weight adjustment algorithm to generate an adjusted weight allocation matrix, for example, temperature weight 0.45, humidity weight 0.35, and stability weight 0.2. Based on the adjusted weight allocation matrix, a weighted summation method is used to calculate the normalized dataset, generating an initial control score of 0.72. For the initial control score, a nonlinear optimization algorithm is used for iterative calculation to generate an optimized control score of 0.75. By optimizing the control score and combining it with the preset scoring criteria (such as a score ≥ 0.7 being qualified), a dynamic control score of 0.75 is generated for the drying process, which is used for subsequent trigger judgment.

[0037] S106. If the dynamic control score is higher than the preset threshold, the device adjustment logic is triggered by the automated execution unit, and the control process record is distributed and stored using an encryption algorithm to generate an unalterable operation log.

[0038] If the dynamic adjustment score exceeds a preset threshold, the device adjustment command to be executed is obtained through the triggering logic of the automated execution unit, the adjustment operation is performed, and the adjustment result is generated. An encryption algorithm is used to encrypt the adjustment result and the control process record, generating an encrypted data block. The encrypted data block is written to multiple storage nodes through a distributed storage network, generating a storage address list. The encrypted data block in the storage node is retrieved based on the storage address list, and a data integrity verification algorithm is used to verify data integrity, generating an integrity verification result. If the integrity verification result is complete, an operation log is generated through the distributed storage network, recording the adjustment operation and storage information. Based on the operation log content, a consensus algorithm is used to synchronize log data among network nodes, generating a synchronization confirmation. Through the synchronization confirmation, feedback information from the network nodes is obtained, generating the final operation log status.

[0039] For example, if the dynamic control score of 0.75 is higher than the preset threshold of 0.7, the device adjustment command to be executed, such as "adjust heater power to 6.5kW", is obtained through the trigger logic of the automated execution unit. The adjustment operation is executed, and the adjustment result "power adjustment successful" is generated. The adjustment result and the control process record are encrypted using the AES-256 encryption algorithm to generate an encrypted data block. The encrypted data block is written to 5 storage nodes through a distributed storage network to generate a storage address list, such as ["node1 / addr1", "node2 / addr2"]. The encrypted data block in the storage node is obtained according to the storage address list, and the data integrity is verified using the SHA-3 algorithm to generate an integrity verification result "complete". If the integrity verification result is complete, an operation log is generated through the distributed storage network to record the adjustment operation and storage information, such as "timestamp: 20XX-XX-XX, XX:XX:XX; operation: power adjustment; storage address: node1 / addr1". According to the operation log content, the log data is synchronized among network nodes using the PBFT consensus algorithm to generate a synchronization confirmation "5 nodes synchronized". By synchronizing and confirming, feedback information from network nodes is obtained, and the final operation log status "confirmed" is generated, ensuring that the log cannot be tampered with.

[0040] S107. Based on the immutable operation log, retrieve the relevant control path from the distributed storage network, determine the path integrity, and if complete, determine the final output of the control result.

[0041] Operation logs are retrieved from the distributed storage network. A distributed query protocol is used to extract a log dataset containing timestamps and operation hashes, generating the raw operation logs. For the raw operation logs, the log structure is parsed, and the node sequence and hash chain in the control path are extracted to generate a path dataset. If the node sequence in the path dataset matches the preset distributed storage topology, a hash tree verification algorithm is used to calculate the hash value of each node in the path to determine path integrity and generate an integrity verification result. Based on the integrity verification result, if the integrity verification passes, the operation timestamps and associated metadata are extracted from the path dataset to generate an audit dataset. The SHA-3 algorithm is used to hash the audit dataset to generate an audit hash value, ensuring the uniqueness of the audit result. Through a communication protocol, the audit hash value is compared with preset audit standards to determine whether the audit result meets the standards, generating the final control result.

[0042] For example, operation logs are obtained from a distributed storage network. A distributed query protocol is used to extract a log dataset containing timestamps and operation hashes, generating the original operation logs, such as "Timestamp: 2025-10-10, 10:00:00; Hash: abc123". For the original operation logs, the log structure is parsed, and the node sequence and hash chain in the control path are extracted to generate a path dataset, such as ["node1", "abc123"]-["node2", "def456"]. If the node sequence in the path dataset is consistent with the preset distributed storage topology, the Merkle tree verification algorithm is used to calculate the hash value of each node in the path to determine the path integrity, generating an integrity verification result "complete". Based on the integrity verification result, if the integrity verification passes, the operation timestamp and associated metadata are extracted from the path dataset to generate a review dataset, such as "Timestamp: 20XX-XX-XX, XX:XX:XX; Operation: Power Adjustment". The SHA-3 algorithm is used to hash the review dataset, generating the review hash value "xyz789", determining the uniqueness of the review result. The system uses the HTTPS communication protocol to compare the audit hash value with the preset audit standards, determines whether the audit result meets the standards, generates the final control result "Control successful", and outputs it to the user interface.

[0043] Through the coordinated operation of the above steps, this invention achieves precise control of the temperature and humidity of the drying environment. For example, during a drying cycle, when the external ambient temperature suddenly rises, the system can quickly sense the temperature change through a distributed sensor network and generate an optimized environmental feature vector by combining it with external meteorological data. Subsequently, the equipment adjustment module generates adjustment commands based on the environmental feature vector, dynamically adjusting the heater power and fan speed, thereby effectively avoiding uneven drying of the vermicelli due to excessively high temperatures. This process ensures a balance between drying efficiency and product quality, solving the technical problems of response lag and inaccurate parameter adjustment in traditional methods.

[0044] It achieves precise control of temperature and humidity in dry environments through the coordinated operation of modules such as distributed sensor networks, edge computing nodes, automated execution units, and distributed storage networks. The functional implementation process of each module will be described in detail below with reference to specific embodiments of the present invention.

[0045] In practical applications, the system of this invention includes several key modules: an environmental feature extraction module, an equipment adjustment module, a process fusion module, an interference assessment module, a dynamic scoring module, an execution triggering module, and a path verification module. These modules work closely together through data interfaces and communication protocols to complete the entire process from environmental data acquisition to the output of control results.

[0046] First, the environmental feature extraction module collects real-time temperature and humidity data in the dry environment through a distributed sensor network and combines this data with external meteorological data to extract environmental feature vectors. In practice, the distributed sensor network consists of multiple temperature and humidity sensor nodes, each installed at a different location within the drying chamber to ensure comprehensive data coverage. These sensor nodes connect to the central processing unit via wireless communication technology and use a time synchronization protocol to timestamp-align the uploaded raw data, generating a temperature and humidity dataset. Simultaneously, the external meteorological data interface obtains real-time meteorological information from a weather station via HTTP, parsing out air pressure, wind speed, and precipitation fields to generate meteorological feature vectors. Next, the temperature and humidity dataset and meteorological feature vectors are normalized and then subjected to dimensionality reduction using principal component analysis (PCA) to generate initial environmental feature vectors. If the dimensionality of the initial environmental feature vector exceeds a preset threshold, linear discriminant analysis (LDA) is used to further optimize the feature dimension, ultimately generating an optimized environmental feature vector. This process ensures that the environmental feature vector accurately reflects the current state of the dry environment.

[0047] The equipment adjustment module maps the operating parameters of the drying equipment based on environmental feature vectors, generating a sequence of equipment adjustment commands. Specifically, the module first extracts key feature values ​​from the environmental feature vectors and generates a feature priority list using a feature weight allocation method. Then, the module initializes the operating parameters of the drying equipment, associating the feature priority list with the equipment parameters to generate preliminary adjustment commands. To improve the usability of the commands, the module optimizes the preliminary adjustment commands using a fuzzy logic algorithm, generating an optimized adjustment command sequence. If conflicting commands exist in the optimized sequence, a conflict detection algorithm identifies the conflict points, and a priority ranking method is used to eliminate the conflicts, ultimately generating the final adjustment command sequence. Furthermore, the module verifies the command execution effect using simulation technology, generates an execution effect evaluation report, and adjusts the parameter range of the adjustment command sequence accordingly, generating adaptive adjustment commands. This process ensures the scientific validity and operability of the equipment adjustment commands.

[0048] The process fusion module extracts the trend of process parameter changes from historical drying records and uses time series analysis to fuse the process parameters with the current environmental feature vector to obtain a comprehensive control vector. In practice, the module extracts process parameter data from the historical drying record database and uses a sliding window algorithm to segment the data, generating time series segments. Time series analysis is then used to fit the trends of these segments, and the least squares method is used to generate fitted curves to extract the trend of process parameter changes. Subsequently, the module fuses the trend of process parameter changes with the current environmental feature vector and uses a weighted average method to generate a preliminary comprehensive control vector. If the fluctuation range of the preliminary comprehensive control vector exceeds a preset threshold, a Kalman filter algorithm is used to smooth the vector, generating an optimized comprehensive control vector. Finally, the module uses cluster analysis to classify drying conditions, generating process condition labels, and combines this with the distribution of abnormal data in historical records to generate a validation dataset for the comprehensive control vector. This process ensures the accuracy and reliability of the comprehensive control vector.

[0049] The interference assessment module obtains external environmental interference factors by calling a real-time data interface through edge computing nodes and determines whether the intensity of these interference factors exceeds a preset threshold. In practice, the module acquires interference factor data from external environmental monitoring equipment via the real-time data interface, removes noise using data cleaning techniques, and generates a clean dataset. Subsequently, the module quantifies the intensity of interference factors in the clean dataset and generates an interference intensity index using a standardization method. This index is compared to a preset interference threshold; if the index is lower than the threshold, an environmental stability assessment result is generated. To further verify environmental stability, the module obtains supplementary external environmental data via the real-time data interface and verifies the reliability of the supplementary data using a data integrity verification algorithm, generating a verified supplementary dataset. Finally, the module uses regression analysis to predict future interference trends, generates an interference prediction model, and combines this with the environmental stability assessment result to produce an external environmental interference assessment report. This process ensures a scientific assessment of environmental stability.

[0050] The dynamic scoring module performs multi-dimensional weighted calculations based on the comprehensive control vector and environmental stability results to generate a dynamic control score for the drying process. In its implementation, the module first acquires the comprehensive control vector and environmental stability results, and then normalizes the data for each dimension using a preset weight allocation rule to generate a normalized dataset. Subsequently, the module uses the analytic hierarchy process (AHP) to determine the weight coefficients for each dimension, generating a weight allocation matrix. If the weight coefficient of a certain dimension in the weight allocation matrix exceeds a preset upper limit, the weights are reallocated using a weight adjustment algorithm to generate an adjusted weight allocation matrix. Finally, the module uses a weighted summation method to calculate the normalized dataset, generating a preliminary control score, and then iteratively calculates using a nonlinear optimization algorithm to generate an optimized control score. This process ensures the scientific validity and accuracy of the dynamic control score.

[0051] When the dynamic adjustment score exceeds a preset threshold, the execution trigger module triggers the device adjustment logic through the automated execution unit. It then uses an encryption algorithm to distribute the adjustment process records, generating an immutable operation log. In practice, the module first obtains the device adjustment command to be executed through the trigger logic of the automated execution unit, performs the adjustment operation, and generates the adjustment result. Subsequently, the module uses an encryption algorithm to encrypt the adjustment result and the adjustment process record, generating an encrypted data block. This encrypted data block is written to multiple storage nodes through a distributed storage network, generating a storage address list. The module retrieves the encrypted data block from the storage nodes based on the storage address list and verifies the data integrity using a data integrity verification algorithm, generating an integrity verification result. If the integrity verification result is complete, an operation log is generated through the distributed storage network, recording the adjustment operation and storage information. Finally, the module uses a consensus algorithm to synchronize log data among network nodes, generating a synchronization confirmation. It then obtains feedback information from the network nodes through the synchronization confirmation, generating the final operation log status. This process ensures the security and integrity of the operation log.

[0052] The path verification module retrieves relevant control paths from the distributed storage network based on immutable operation logs and determines path integrity. In practice, the module first obtains operation logs from the distributed storage network and uses a distributed query protocol to extract a log dataset containing timestamps and operation hashes, generating the raw operation logs. Then, the module parses the log structure, extracts the node sequence and hash chain from the control path, and generates a path dataset. If the node sequence in the path dataset matches the preset distributed storage topology, a hash tree verification algorithm is used to calculate the hash value of each node in the path, determining path integrity and generating an integrity verification result. Finally, the module extracts operation timestamps and associated metadata from the path dataset to generate a review dataset, and uses the SHA-3 algorithm to hash the review dataset, generating a review hash value to determine the uniqueness of the review result. This process ensures the integrity and traceability of the control path.

[0053] Through the coordinated operation of the above modules, this invention achieves precise control of temperature and humidity in the drying environment, solving the technical problems of inaccurate temperature and humidity control, delayed data response, and insufficient linkage between environmental variables and equipment during the drying process.

[0054] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0055] In the actual vermicelli drying process, real-time temperature and humidity data within the drying chamber are first collected through an environmental feature extraction module. Multiple temperature and humidity sensor nodes are deployed inside the drying chamber, installed at the top, middle, and bottom to ensure comprehensive data coverage. Each sensor node uploads its collected data to the central processing unit via wireless communication technology, and the data is timestamped using a time synchronization protocol to generate a temperature and humidity dataset. Simultaneously, an external meteorological data interface obtains real-time meteorological information from a weather station, parsing out air pressure, wind speed, and precipitation fields to form a meteorological feature vector. Subsequently, the system normalizes the temperature and humidity dataset and the meteorological feature vector, and reduces the data dimensionality using principal component analysis to generate an initial environmental feature vector. If the initial environmental feature vector has a high dimensionality, linear discriminant analysis is further used to optimize the feature dimension, ultimately generating an optimized environmental feature vector that accurately reflects the current drying environment.

[0056] Next, the equipment adjustment module generates a sequence of equipment adjustment commands based on the optimized environmental feature vector. This module first extracts key feature values ​​from the environmental feature vector, such as temperature fluctuation range and humidity change rate, and generates a feature priority list using a feature weight allocation method. Then, the module initializes the operating parameters of the drying equipment, such as heater power and fan speed, and associates the feature priority list with the equipment parameters to generate preliminary adjustment commands. Based on this, the module optimizes the preliminary adjustment commands using a fuzzy logic algorithm to generate an optimized adjustment command sequence. If conflicting commands exist in the optimized adjustment command sequence, such as simultaneously requiring an increase in temperature and a decrease in humidity that the equipment cannot simultaneously meet, a conflict detection algorithm identifies the conflict points, and a priority ranking method is used to eliminate the conflict, ultimately generating the final adjustment command sequence. Furthermore, the module verifies the command execution effect using simulation technology, generates an execution effect evaluation report, and adjusts the parameter range of the adjustment command sequence accordingly to generate adaptive adjustment commands.

[0057] The process fusion module extracts the trend of process parameter changes from the historical drying record database and fuses it with the current environmental feature vector. Specifically, the module segments the process parameter data in the historical drying records using a sliding window algorithm to generate time series segments. Then, the module uses time series analysis methods to fit the trend of the time series segments and generates a fitting curve using the least squares method to extract the trend of process parameter changes. Next, the module fuses the trend of process parameter changes with the current environmental feature vector and uses a weighted average method to generate a preliminary comprehensive control vector. If the fluctuation range of the preliminary comprehensive control vector exceeds a preset threshold, the vector is smoothed using a Kalman filter algorithm to generate an optimized comprehensive control vector. Finally, the module uses cluster analysis to classify the drying conditions, generates process condition labels, and combines the distribution of abnormal data in historical records to generate a validation dataset for the comprehensive control vector.

[0058] The interference assessment module obtains external environmental interference factors by calling real-time data interfaces through edge computing nodes and determines whether their intensity exceeds a preset threshold. In practice, the module acquires interference factor data from external environmental monitoring equipment, such as sudden changes in temperature or humidity, and uses data cleaning techniques to remove noise, generating a clean dataset. Subsequently, the module quantifies the intensity of interference factors in the clean dataset and generates an interference intensity index using a standardization method. The interference intensity index is compared with a preset interference threshold; if the index is lower than the threshold, an environmental stability judgment result is generated. To further verify environmental stability, the module obtains supplementary external environmental data through the real-time data interface and uses a data integrity verification algorithm to verify the reliability of the supplementary data, generating a verified supplementary dataset. Finally, the module uses regression analysis to predict future interference trends, generates an interference prediction model, and combines this with the environmental stability judgment result to generate an external environmental interference assessment report.

[0059] The dynamic scoring module generates dynamic control scores for the drying process based on the comprehensive control vector and environmental stability results. The module first acquires the comprehensive control vector and environmental stability results, and then normalizes the data for each dimension using a preset weighting rule to generate a normalized dataset. Subsequently, the module uses the analytic hierarchy process (AHP) to determine the weight coefficients for each dimension, generating a weighting matrix. If the weight coefficient of a certain dimension in the weighting matrix exceeds a preset upper limit, a weight adjustment algorithm is used to reallocate the weights, generating an adjusted weighting matrix. Finally, the module uses a weighted summation method to calculate the normalized dataset, generating a preliminary control score, and then iteratively calculates using a nonlinear optimization algorithm to generate an optimized control score.

[0060] The execution trigger module initiates equipment adjustment logic when the dynamic adjustment score exceeds a preset threshold. First, the module obtains the equipment adjustment command to be executed through the automated execution unit, performs the adjustment operation, and generates the adjustment result. Then, the module uses an encryption algorithm to encrypt the adjustment result and the adjustment process record, generating an encrypted data block. This encrypted data block is written to multiple storage nodes through a distributed storage network, generating a storage address list. The module retrieves the encrypted data block from the storage nodes based on the storage address list and verifies data integrity using a data integrity verification algorithm, generating an integrity verification result. If the integrity verification result is complete, an operation log is generated through the distributed storage network, recording the adjustment operation and storage information. Finally, the module uses a consensus algorithm to synchronize log data among network nodes, generating a synchronization confirmation, and obtains feedback information from the network nodes through the synchronization confirmation, generating the final operation log status.

[0061] The path verification module retrieves relevant control paths based on immutable operation logs and determines path integrity. First, the module obtains operation logs from the distributed storage network and uses a distributed query protocol to extract a log dataset containing timestamps and operation hashes, generating the raw operation logs. Then, the module parses the log structure, extracts the node sequence and hash chain from the control path, and generates a path dataset. If the node sequence in the path dataset matches the preset distributed storage topology, a hash tree verification algorithm is used to calculate the hash value of each node in the path, determining path integrity and generating an integrity verification result. Finally, the module extracts operation timestamps and associated metadata from the path dataset to generate a review dataset, and uses the SHA-3 algorithm to hash the review dataset, generating a review hash value to determine the uniqueness of the review result.

[0062] Through the coordinated operation of the above steps, this invention achieves precise control of the temperature and humidity of the drying environment. For example, during a drying cycle, when the external ambient temperature suddenly rises, the system can quickly sense the temperature change through a distributed sensor network and generate an optimized environmental feature vector by combining it with external meteorological data. Subsequently, the equipment adjustment module generates adjustment commands based on the environmental feature vector, dynamically adjusting the heater power and fan speed, thereby effectively avoiding uneven drying of the vermicelli due to excessively high temperatures. This process ensures a balance between drying efficiency and product quality, solving the technical problems of response lag and inaccurate parameter adjustment in traditional methods.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A temperature and humidity control system for drying rice noodles, characterized in that, include: The environmental feature extraction module is used to collect real-time temperature and humidity data in a dry environment through a distributed sensor network and extract environmental feature vectors by combining them with external meteorological data. The equipment adjustment module is used to map the operating parameters of the drying equipment according to the environmental feature vector and generate a sequence of equipment adjustment instructions. The process fusion module is used to extract the changing trends of process parameters from historical drying records, and to fuse process parameters with current environmental feature vectors using time series analysis to obtain a comprehensive control vector. The interference assessment module is used to obtain external environmental interference factors by calling real-time data interfaces through edge computing nodes, and to determine whether the intensity of the interference factors exceeds a preset threshold. If it does not exceed the threshold, the environmental stability is determined to meet the requirements. This includes: Data on interference factors is obtained from external environmental monitoring equipment through a real-time data interface, and data cleaning techniques are used to remove noise data and generate a clean dataset. The intensity of interference factors in the clean dataset is quantified, and a standardization method is used to generate an interference intensity index. The interference intensity index is compared with a preset interference threshold. If the interference intensity index is lower than the interference threshold, an environmental stability determination result is generated. Based on the environmental stability determination result, the real-time data interface is called to obtain supplementary data of the external environment. The reliability of the supplementary data is verified by a data integrity verification algorithm, and a verified supplementary dataset is generated. Based on the verified supplementary dataset, regression analysis is used to predict future interference trends and generate an interference prediction model. An external environmental interference assessment report is generated by combining the interference prediction model with the environmental stability assessment results. The dynamic scoring module is used to perform multi-dimensional weighted calculations based on the comprehensive control vector and environmental stability results to generate a dynamic control score for the drying process. The execution trigger module is used to trigger the device adjustment logic through the automated execution unit if the dynamic adjustment score is higher than the preset threshold, and to use an encryption algorithm to distribute the control process records to generate an unalterable operation log. The path verification module is used to retrieve relevant control paths from the distributed storage network based on the immutable operation log, determine the path integrity, and if the path is complete, determine the final output of the control result.

2. The temperature and humidity control system for drying vermicelli according to claim 1, characterized in that: The process of collecting real-time temperature and humidity data in a dry environment through a distributed sensor network and extracting environmental feature vectors by combining them with external meteorological data includes: Raw data uploaded by temperature and humidity sensor nodes are obtained from a distributed sensor network, and a time-stamp-aligned temperature and humidity dataset is obtained using a data synchronization protocol. Real-time meteorological information released by meteorological stations is obtained through external meteorological data interfaces. Data parsing technology is used to extract fields of air pressure, wind speed and precipitation to generate meteorological feature vectors. The temperature and humidity dataset and the meteorological feature vector are normalized, and the multidimensional features are reduced using the principal component analysis algorithm to generate an initial environmental feature vector. If the dimension of the initial environmental feature vector is higher than a preset threshold, the feature dimension is further optimized by linear discriminant analysis to generate an optimized environmental feature vector. Based on the optimized environmental feature vector, the dry environment is classified using the support vector machine algorithm to generate environmental status labels; By combining the environmental status tags with the preset process requirement templates and matching the drying conditions, a structured environmental description is generated.

3. The temperature and humidity control system for drying vermicelli according to claim 1, characterized in that: The step of mapping the operating parameters of the drying equipment based on the environmental feature vector to generate a sequence of equipment adjustment instructions includes: The environmental feature vector is obtained, and key feature values ​​are extracted from it. A feature priority list is generated by using a feature weight allocation method. The operating parameters of the drying equipment are initialized and configured. Using parameter mapping rules, the feature priority list is associated with the equipment parameters to generate preliminary adjustment instructions. The initial adjustment instructions are optimized using a fuzzy logic algorithm to generate an optimized sequence of adjustment instructions. If there are conflicting instructions in the optimized adjustment instruction sequence, the conflict points are identified by a conflict detection algorithm, and the conflict is eliminated by a priority sorting method to generate the final adjustment instruction sequence. For the final adjustment command sequence, simulation technology is used to verify the command execution effect and generate an execution effect evaluation report; Based on the performance evaluation report, the parameter range of the adjustment instruction sequence is adjusted to generate adaptive adjustment instructions.

4. A temperature and humidity control system for drying vermicelli according to claim 1, characterized in that: The process parameter variation trend is extracted from historical drying records, and a time series analysis method is used to fuse the process parameters with the current environmental feature vector to obtain a comprehensive control vector, including: Process parameter data are extracted from the historical drying record database, and the data is segmented using a sliding window algorithm to generate time series segments; The time series segments were trend-fitted using time series analysis methods, and the least squares method was used to generate fitting curves to extract the changing trends of process parameters. The process parameter variation trend is fused with the current environmental feature vector, and a weighted average method is used to generate a preliminary comprehensive control vector; If the fluctuation range of the preliminary integrated control vector exceeds the preset threshold, the vector is smoothed by the Kalman filter algorithm to generate an optimized integrated control vector. Based on the optimized integrated control vector, cluster analysis is used to classify drying conditions and generate process condition labels. By combining the process condition labels with the distribution of abnormal data in historical records, a verification dataset for the comprehensive control vector is generated.

5. A temperature and humidity control system for drying vermicelli according to claim 1, characterized in that: The step of generating a dynamic control score for the drying process by performing multi-dimensional weighted calculations based on the comprehensive control vector and environmental stability results includes: The comprehensive regulation vector and environmental stability results are obtained, and the data of each dimension are normalized according to the preset weight allocation rules to generate a normalized dataset. Based on the normalized dataset, the weight coefficients of each dimension are determined using the analytic hierarchy process (AHP) to generate a weight allocation matrix. If the weight coefficient of a certain dimension in the weight allocation matrix is ​​higher than the preset upper limit, the weights are redistributed through a weight adjustment algorithm to generate an adjusted weight allocation matrix. Based on the adjusted weight allocation matrix, a weighted summation method is used to calculate the normalized dataset to generate preliminary control scores; For the initial control score, a nonlinear optimization algorithm is used for iterative calculation to generate an optimized control score; The optimized control score, combined with the preset scoring criteria, generates a dynamic control score for the drying process.

6. A temperature and humidity control system for drying vermicelli according to claim 1, characterized in that: If the dynamic adjustment score is higher than a preset threshold, the device adjustment logic is triggered by the automated execution unit, and the control process record is distributed and stored using an encryption algorithm to generate an immutable operation log, including: If the dynamic adjustment score is higher than the preset threshold, the device adjustment command to be executed is obtained through the triggering logic of the automated execution unit, the adjustment operation is executed, and the adjustment result is generated. The adjustment results and control process records are encrypted using an encryption algorithm to generate encrypted data blocks; The encrypted data block is written to multiple storage nodes through a distributed storage network to generate a list of storage addresses. The encrypted data blocks in the storage nodes are obtained according to the storage address list, and the data integrity is verified by a data integrity verification algorithm to generate an integrity verification result. If the integrity verification result is complete, an operation log is generated through the distributed storage network to record the adjustment operation and storage information; Based on the operation log content, a consensus algorithm is used to synchronize log data among network nodes and generate a synchronization confirmation. Through the synchronization confirmation, feedback information from network nodes is obtained, and the final operation log status is generated.

7. A temperature and humidity control system for drying vermicelli according to claim 1, characterized in that: The step of retrieving relevant control paths from the distributed storage network based on the immutable operation log, determining the path integrity, and if complete, determining the final output of the control result includes: The operation logs are obtained from the distributed storage network, and the log dataset containing timestamps and operation hashes is extracted using a distributed query protocol to generate the original operation logs. For the original operation log, the log structure is parsed, the node sequence and hash chain in the control path are extracted, and a path dataset is generated; If the node sequence in the path dataset is consistent with the preset distributed storage topology, then the hash tree verification algorithm is used to calculate the hash value of each node in the path, determine the path integrity, and generate an integrity verification result. Based on the integrity verification result, if the integrity verification passes, the operation timestamp and associated metadata are extracted from the path dataset to generate an audit dataset; The SHA-3 algorithm is used to perform hash calculation on the review dataset to generate review hash values ​​and determine the uniqueness of the review results; The review hash value is compared with the preset audit standard through the communication protocol to determine whether the review result meets the standard and generate the final control result.

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