A digital storage management system for manufacturing processing equipment

Abstract

The present invention discloses a digital management system for storage of manufacturing processing equipment, which belongs to the field of information technology of manufacturing industry, and specifically comprises: collecting and preprocessing humidity data of equipment storage environment in real time through humidity sensors, and wirelessly transmitting the data to a monitoring center by adopting a data compression strategy; the monitoring center decompresses and analyzes the data by using a machine learning algorithm, and predicts humidity changes in combination with a time series prediction algorithm; a rust risk assessment model is constructed according to the prediction results and a preset rust humidity threshold, an alarm is triggered and the risk level is displayed; the operation of dehumidification equipment is dynamically adjusted by using an optimization strategy according to the risk level; and a report is regularly generated to summarize the environmental conditions, early warning conditions and scheduling effects. The present invention improves the humidity management efficiency and safety of the equipment storage environment.

Description

Technical Field

[0001] The invention belongs to the technical field of manufacturing information technology, and specifically is a digital storage management system for manufacturing processing equipment. Background Art

[0002] As an important pillar of the national economy, the production efficiency and quality control level of the manufacturing industry are directly related to the competitiveness of the entire industrial chain. However, the traditional storage management method of manufacturing processing equipment has many shortcomings, such as relying on manual records and physical labels, resulting in inaccurate information records, difficult data traceability, untimely equipment maintenance, etc., and it is difficult to achieve real-time monitoring and efficient scheduling of equipment status. These problems have seriously restricted the further development of the manufacturing industry.

[0003] For example, a Chinese patent application with publication number CN116165983A discloses a digital storage management system for manufacturing processing equipment, including: processing equipment, a server and an equipment access auxiliary device, the processing equipment is used to form field data in the production and processing stage, the field data includes a first data set and a second data set, the server is used to monitor the status of the connected processing equipment, the equipment access auxiliary device is used to establish communication connections with the processing equipment to be connected and the gateway respectively, and send the equipment information of the processing equipment to be connected to the server through the gateway, by collecting the first data set and the second data set of the processing equipment, and uploading them to the server, unified digital management of CNC processing equipment is achieved, the collected data is not easy to lose, can be traced, and the data statistics are accurate and efficient. According to the results of data statistics, managers can timely grasp the processing status and operating status of CNC processing equipment, and effectively improve the management quality of CNC processing equipment.

[0004] For example, the Chinese patent with the authorization announcement number CN113762753B discloses a digital material management system, method, equipment and storage medium, including: automatically identifying the license plate information of the target vehicle to enter the factory through the license plate recognition system and automatically controlling the release operation for the target vehicle, and directly sending the license plate information of the target vehicle to the material control system, without the need to manually register the license plate information that needs material control in the material control system, thereby improving the efficiency of license plate information registration; at the same time, the material control system automatically controls the quality inspection system to perform quality inspection on the materials transported by the target vehicle, and based on the quality inspection results returned by the quality inspection system, automatically controls the target vehicle to enter the unloading production line for unloading through the central control system. As a result, automated control is achieved in multiple links from the target vehicle entering the factory, quality inspection to unloading, reducing the links of manual participation in material management, and improving the efficiency, accuracy and effectiveness of material management.

[0005] The above existing technologies all have the following problems: lack of environmental humidity management; lack of rust risk assessment and early warning; lack of decision support function; low flexibility. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a digital management system for storage of manufacturing processing equipment. The humidity data of the equipment storage environment is collected and pre-processed in real time by a humidity sensor, and the data is wirelessly transmitted to a monitoring center using a data compression strategy. The monitoring center uses a machine learning algorithm to decompress and analyze the data, and combines a time series prediction algorithm to predict humidity changes. A rust risk assessment model is constructed based on the prediction results and a preset rust humidity threshold, which triggers an alarm and displays the risk level. The operation of the dehumidification equipment is dynamically adjusted according to the risk level using an optimization strategy. A report is regularly generated to summarize the environmental conditions, early warning conditions and scheduling effects. The present invention improves the humidity management efficiency and safety of the equipment storage environment.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A digital storage management system for manufacturing processing equipment, comprising:

[0009] The data processing module includes a humidity sensor unit and a data compression unit. The humidity sensor unit is used to collect humidity data of the device storage environment in real time. The data compression unit is configured with a data compression strategy, which is used to compress the pre-processed humidity data.

[0010] The data analysis module is used to receive the compressed humidity data transmitted from the data processing module, decompress and analyze it, and identify the trend and law of humidity change;

[0011] The risk assessment module includes a humidity prediction unit and a risk assessment unit. The humidity prediction unit is configured with a time series prediction algorithm, and the time series prediction algorithm is used to predict the humidity change of the equipment; the risk assessment unit is used to build a rust risk assessment model, and evaluate the rust risk level according to the humidity prediction result and the preset rust humidity threshold;

[0012] The equipment scheduling module includes an optimization strategy unit and an equipment scheduling unit. The optimization strategy unit is used to formulate a scheduling optimization strategy for the dehumidification equipment according to the rust risk level and the early warning information; the equipment scheduling unit is used to schedule the dehumidification equipment according to the scheduling optimization strategy for the dehumidification equipment;

[0013] The report generation module is used to regularly generate reports summarizing the storage environment status of the equipment, the rust risk warning status and the scheduling effect of the dehumidification equipment.

[0014] Specifically, the data compression unit adopts a data compression strategy, and the specific steps of the data compression strategy include:

[0015] A1: Get the preprocessed humidity data, check each humidity value in the preprocessed humidity data set one by one, and for each humidity value, check whether the humidity value is already in the pre-built hash table;

[0016] If it already exists, add 1 to the number of occurrences corresponding to the humidity value;

[0017] If it does not exist, add the humidity value to the pre-built hash table and initialize the number of occurrences to 1;

[0018] A2: After the traversal is completed, the hash table will contain all humidity values ​​and their corresponding occurrence times, and the frequency of occurrence of each humidity value in the preprocessed humidity data will be obtained. , and recorded as a frequency distribution table;

[0019] A3: Based on the frequency information in the frequency distribution table, a priority tree is constructed, in which nodes with lower frequencies have higher priorities;

[0020] A4: Generate a unique binary code for each humidity value from the priority tree to obtain a coding mapping table for humidity data;

[0021] A5: Use the binary code in the humidity data encoding mapping table to replace the humidity value in the preprocessed humidity data, so as to obtain compressed humidity data;

[0022] A6: The compressed humidity data is transmitted to the monitoring center via a wireless network.

[0023] Specifically, the specific steps of constructing the priority tree in A3 include:

[0024] A3.1: Read the frequency distribution table, obtain each humidity value and its corresponding frequency, and create a node for each humidity value and its frequency. At the same time, put all created nodes into a node list;

[0025] A3.2: Select the two lowest-frequency nodes from the node list as the child nodes of the current operation, and create a new parent node for these two lowest-frequency nodes, and satisfy ,in, Represents the frequency value of the parent node, and Respectively represent the frequency values ​​of the two nodes with the lowest frequency.

[0026] Specifically, the specific steps of constructing the priority tree in A3 also include:

[0027] A3.3: Add the newly created parent node to the node list, remove the two selected nodes with the lowest frequency, and update the left and right child node pointers of the new node to point to the two originally selected nodes with the lowest frequency. The left and right child node pointers refer to the left and right child nodes of the new node.

[0028] A3.4: Repeat the iteration until only one node is left in the node list, and obtain the root node of the priority tree;

[0029] A3.5: After the construction is completed, output the structure of the priority tree.

[0030] Specifically, the specific steps of A4 include:

[0031] A4.1: Create an empty hash table as a coding mapping table to store humidity values ​​and their corresponding binary codes;

[0032] A4.2: Define a recursive function and call it starting from the root node of the priority tree. In the recursive function, use a string variable as the encoding prefix of the current node and set the prefix to an empty string initially.

[0033] A4.3: For the current node, if it is a leaf node in the priority tree, the current coding prefix is ​​associated with the humidity value and stored in the coding mapping table;

[0034] A4.4: If the current node is not a leaf node in the priority tree, recursively process its left child node and right child node respectively, and when recursively called, expand the current encoding prefix to prefix plus zero and prefix plus 1 respectively;

[0035] A4.5: After the recursive function traverses the entire tree, it returns the encoding mapping table to obtain the encoding mapping table of humidity data.

[0036] Specifically, the risk assessment unit includes:

[0037] B1: Collect the equipment's material, surface treatment, usage environment, and historical rust record information, and generate equipment humidity data through integration;

[0038] B2: Set the humidity threshold for equipment rusting based on the collected equipment humidity data and ;

[0039] B3: Obtain the humidity prediction result of the humidity prediction unit, and build a rust risk assessment model based on the preset equipment rust humidity threshold. Use the rust risk assessment model to assess the rust risk of the equipment, and give the corresponding risk level based on the output result of the rust risk assessment model.

[0040] Specifically, the specific steps of B3 include:

[0041] B3.1: Obtain humidity prediction results and preset equipment rust humidity threshold and ;

[0042] B3.2: Load a risk assessment framework, and in the risk assessment framework, determine the rust risk index to obtain a rust risk assessment model;

[0043] B3.3: Input the preset equipment rust humidity threshold into the rust risk assessment model, execute the risk assessment algorithm in the rust risk assessment model, and compare the humidity prediction result with the preset equipment rust humidity threshold;

[0044] B3.4: Based on the output of the risk assessment algorithm, obtain the rust risk assessment results of the equipment , where RL represents the rust risk assessment result, PH represents the humidity prediction result, and T represents the preset equipment rust humidity threshold set. represents the risk assessment function;

[0045] B3.5: Classify the rust risk level according to the rust risk assessment results of the equipment ,in, Indicates that the rust risk level is high. Indicates that the rust risk level is low, triggers the early warning mechanism, and sends an early warning message. This means there is no risk of rust.

[0046] Specifically, the scheduling optimization strategy of the dehumidification equipment includes:

[0047] C1: Collect data on rust risk level, early warning information, equipment usage and humidity change trend. Based on the collected data, determine the rust risk level and priority of equipment in different areas and formulate a scheduling strategy for dehumidification equipment.

[0048] C2: Develop operation plan and parameter settings for dehumidification equipment based on the dehumidification equipment scheduling strategy;

[0049] C3: Start the dehumidification equipment and perform dehumidification according to the operation plan of the dehumidification equipment;

[0050] C4: Monitor the operation of the dehumidification equipment in real time, and dynamically adjust the operation plan and parameters of the dehumidification equipment based on the monitoring results and actual conditions.

[0051] Specifically, the scheduling strategy of the dehumidification equipment in C1 includes the start-up sequence, operation time, parameter setting and formulation of backup plans of the dehumidification equipment.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention proposes a digital storage management system for manufacturing processing equipment, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0054] 2. The present invention proposes a digital management system for storage of manufacturing processing equipment. By real-time collection, preprocessing and transmission of humidity data of the equipment storage environment, and using machine learning algorithms and time series prediction algorithms to conduct in-depth analysis and prediction of the humidity data, the present invention realizes accurate monitoring and prediction of the humidity of the equipment storage environment, which helps to timely discover and deal with potential rust risks and improve the safety and reliability of the equipment.

[0055] 3. The present invention proposes a digital management system for the storage of manufacturing processing equipment. By constructing a rust risk assessment model and triggering an alarm mechanism, the scheduling of dehumidification equipment is optimized in combination with an optimization strategy, thereby realizing intelligent management of the equipment storage environment. This can not only effectively reduce the rust risk of the equipment and extend the service life of the equipment, but also dynamically adjust the operation process and parameters of the dehumidification equipment according to the actual use of the equipment and the humidity change trend, thereby improving the operation efficiency and energy-saving effect of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is an architecture diagram of a digital storage management system for manufacturing processing equipment of the present invention;

[0057] Figure 2 This is a flowchart of the overall implementation of a digital management system for storage of manufacturing processing equipment according to the present invention;

[0058] Figure 3 This is a flowchart of the algorithm of a data compression unit of a digital management system for storage of manufacturing processing equipment of the present invention;

[0059] Figure 4 This is an algorithm flow chart of a risk assessment unit of a digital storage management system for manufacturing processing equipment according to the present invention. DETAILED DESCRIPTION

[0060] Example 1

[0061] See also Figure 1 and Figure 2 , an embodiment provided by the present invention: a digital storage management system for manufacturing processing equipment, comprising the following steps:

[0062] Data processing module, data analysis module, risk assessment module, equipment scheduling module, report generation module;

[0063] The data processing module is used to collect humidity data of the device storage environment in real time through the humidity sensor and perform preprocessing for subsequent data transmission and analysis;

[0064] The data analysis module is used to receive the compressed humidity data transmitted from the data processing module, decompress and analyze it, and identify the trend and law of humidity change;

[0065] The risk assessment module is used to build a rust risk assessment model based on the humidity prediction results and the preset equipment rust humidity threshold, and trigger the corresponding alarm mechanism;

[0066] The equipment scheduling module is used to optimize the scheduling of dehumidification equipment using optimization strategies according to the rust risk level and early warning information, and dynamically adjust the operation process and parameters of the dehumidification equipment;

[0067] The report generation module is used to regularly generate reports summarizing the storage environment status of the equipment, rust risk warning conditions, and the scheduling effect of the dehumidification equipment.

[0068] The data processing module includes: a humidity sensor unit, a data preprocessing unit, and a data compression unit;

[0069] Humidity sensor unit, used to collect humidity data of the device storage environment in real time;

[0070] The data preprocessing unit is used to perform preprocessing operations such as cleaning and denoising on the collected humidity data to improve the data quality;

[0071] The data compression unit is used to compress the pre-processed humidity data by adopting a data compression strategy to reduce the bandwidth requirement for data transmission.

[0072] The data analysis module includes: a data receiving unit, a data decompression unit, and a data analysis unit;

[0073] A data receiving unit, used for receiving compressed humidity data;

[0074] A data decompression unit, used to decompress the received data using a decompression algorithm corresponding to the data compression unit;

[0075] The data analysis unit is used to analyze the decompressed humidity data using a machine learning algorithm to identify the trend and pattern of humidity changes. In the present invention, the machine learning algorithm adopts a time series analysis model, and the time series analysis model is the prior art content in this field, and is not an inventive solution of the present application, and will not be elaborated here.

[0076] The risk assessment module includes: humidity prediction unit, risk assessment unit, and early warning mechanism unit;

[0077] A humidity prediction unit, which is used to combine the real-time monitored humidity data and use a time series prediction algorithm to predict the humidity change of the equipment, wherein the time series prediction algorithm is a prior art content in this field and is not an inventive solution of the present application, and is not described in detail here;

[0078] A risk assessment unit is used to construct a rust risk assessment model and assess the rust risk level according to the humidity prediction result and the preset rust humidity threshold;

[0079] The early warning mechanism unit is used to trigger the alarm mechanism according to the rust risk level assessment result and send early warning information to the management personnel.

[0080] The equipment scheduling module includes: optimization strategy unit, equipment scheduling unit, and parameter adjustment unit;

[0081] The optimization strategy unit is used to formulate the scheduling optimization strategy of the dehumidification equipment according to the rust risk level and early warning information;

[0082] The equipment scheduling unit is used to schedule the dehumidification equipment according to the scheduling optimization strategy of the dehumidification equipment to ensure the dehumidification effect;

[0083] The parameter adjustment unit is used to dynamically adjust the operating parameters of the dehumidification equipment according to the actual use of the equipment and the humidity change trend to improve the dehumidification efficiency.

[0084] The report generation module includes: a data summary unit, a report generation unit, and a report analysis unit;

[0085] A data aggregation unit, used to collect and aggregate data and information from the data processing module, data analysis module, risk assessment module, and equipment scheduling module;

[0086] A report generation unit, used to generate a detailed report based on the data and information provided by the data aggregation unit;

[0087] The report analysis unit is used to analyze the generated reports and extract the core information.

[0088] In summary, the digital management system for storage of manufacturing processing equipment realizes real-time monitoring, analysis, early warning and optimized scheduling of the humidity of the equipment storage environment through the collaborative work of data processing module, data analysis module, risk assessment module, equipment scheduling module, report generation module and its units, which effectively reduces the risk of equipment rusting and improves the management level of the equipment storage environment.

[0089] Specifically, the overall implementation process of a digital management system for storage of manufacturing processing equipment includes:

[0090] S1: collect humidity data of the equipment storage environment in real time through the humidity sensor, pre-process the collected humidity data of the equipment storage environment, and use the data compression strategy to transmit the pre-processed humidity data to the monitoring center through the wireless network;

[0091] S2: The monitoring center receives the compressed humidity data and uses a machine learning algorithm to decompress and analyze the compressed humidity data to identify the trend and pattern of humidity changes. At the same time, it combines the real-time monitored humidity data and uses a time series prediction algorithm to predict the humidity changes of the equipment.

[0092] Furthermore, the specific steps of S2 include:

[0093] S2.1: The monitoring center receives compressed humidity data from the sensor through the network, and decompresses the received compressed data using the GZIP decompression algorithm to obtain the original humidity data;

[0094] S2.2: Clean the decompressed humidity data to remove noise, outliers and missing values. If the data is time series data, ensure that the data is arranged in chronological order;

[0095] S2.3: Extract useful features from humidity data, such as timestamp, date, season, weather conditions, and use recursive feature elimination method for feature screening;

[0096] S2.4: Select a machine learning algorithm based on a time series model to model humidity data, and use historical humidity data as a training set to train the time series model;

[0097] S2.5: Receive real-time monitoring humidity data from the sensor, and use the trained time series model to predict the real-time monitoring data to obtain humidity changes;

[0098] S2.6: Output the prediction results to the monitoring center or related systems.

[0099] S3: Build a rust risk assessment model, and use the humidity prediction results as the input of the rust risk assessment model. Combined with the preset equipment rust humidity threshold, classify and evaluate the prediction results. According to the evaluation results, trigger the alarm mechanism, and display the rust risk level and warning information to the management personnel through a visual interface;

[0100] S4: According to the rust risk level and early warning information, the optimization strategy is used to optimize the scheduling of the dehumidification equipment, and the operation process and parameters of the dehumidification equipment are dynamically adjusted according to the actual use of the equipment and the humidity change trend;

[0101] S5: Generate reports regularly to summarize the storage environment status of the equipment, rust risk warning status, and the scheduling effect of the dehumidification equipment.

[0102] Example 2

[0103] See also Figure 3 and Figure 4 In this embodiment, the data compression unit adopts a data compression strategy, and the specific steps of the data compression strategy include:

[0104] A1: Get the preprocessed humidity data, check each humidity value in the preprocessed humidity data set one by one, and for each humidity value, check whether the humidity value is already in the pre-built hash table;

[0105] If it already exists, add 1 to the number of occurrences corresponding to the humidity value;

[0106] If it does not exist, add the humidity value to the pre-built hash table and initialize the number of occurrences to 1;

[0107] A2: After the traversal is completed, the hash table will contain all humidity values ​​and their corresponding occurrence times, and the frequency of occurrence of each humidity value in the preprocessed humidity data will be obtained. , and recorded as a frequency distribution table;

[0108] For example, (1) create an empty humidity_count dictionary to store humidity values ​​and their occurrence counts;

[0109] (2) Use a loop or iterator to traverse the preprocessed humidity dataset;

[0110] (3) For each humidity value, check whether it is already in the humidity_count dictionary. If the humidity value is not in the humidity_count dictionary, add it to the humidity_count dictionary and set its occurrence count to 1; if the humidity value is already in the humidity_count dictionary, add 1 to its occurrence count.

[0111] (4) After the traversal is completed, the humidity_count dictionary is a frequency distribution table, where the key is the humidity value and the value is the corresponding number of occurrences.

[0112] A3: Based on the frequency information in the frequency distribution table, a priority tree is constructed, in which nodes with lower frequencies have higher priorities;

[0113] A4: Generate a unique binary code for each humidity value from the priority tree to obtain a coding mapping table for humidity data;

[0114] The length of the binary code is inversely proportional to the frequency of the humidity value, that is, the higher the frequency, the shorter the code, and the output is a code mapping table that records each humidity value and its corresponding binary code.

[0115] A5: Use the binary code in the humidity data encoding mapping table to replace the humidity value in the preprocessed humidity data, so as to obtain compressed humidity data;

[0116] A6: The compressed humidity data is transmitted to the monitoring center via a wireless network.

[0117] The specific steps of constructing the priority tree in A3 include:

[0118] A3.1: Read the frequency distribution table, obtain each humidity value and its corresponding frequency, and create a node for each humidity value and its frequency. At the same time, put all created nodes into a node list;

[0119] A3.2: Select the two lowest-frequency nodes from the node list as the child nodes of the current operation, and create a new parent node for these two lowest-frequency nodes, and satisfy ,in, Represents the frequency value of the parent node, and Respectively represent the frequency values ​​of the two lowest frequency nodes;

[0120] A3.3: Add the newly created parent node to the node list, remove the two selected nodes with the lowest frequency, and update the left and right child node pointers of the new node to point to the two originally selected nodes with the lowest frequency. The left and right child node pointers refer to the left and right child nodes of the new node.

[0121] A3.4: Repeat the iteration until only one node is left in the node list, and obtain the root node of the priority tree;

[0122] A3.5: After the construction is completed, output the structure of the priority tree.

[0123] The specific steps of A4 include:

[0124] A4.1: Create an empty hash table as a coding mapping table for storing humidity values ​​and their corresponding binary codes. The creation of the hash table is a prior art in the art and is not an inventive solution of the present application, and is not described in detail here.

[0125] A4.2: Define a recursive function and call it starting from the root node of the priority tree. In the recursive function, use a string variable as the encoding prefix of the current node and set the prefix to an empty string initially.

[0126] Among them, a recursive function refers to a function that calls itself directly or indirectly in its definition. This function calling method allows the function to process complex data structures, such as trees, graphs or linked lists, by breaking down the problem into smaller sub-problems. In the present invention, for the priority tree, each node represents a value and has a left child node and a right child node, which represent operations or values ​​with lower and higher priorities respectively, and recursive functions are generally used to traverse the tree, calculate the value of an expression, and find a specific node.

[0127] A4.3: For the current node, if it is a leaf node in the priority tree, the current coding prefix is ​​associated with the humidity value and stored in the coding mapping table;

[0128] A4.4: If the current node is not a leaf node in the priority tree, recursively process its left child node and right child node respectively, and when recursively called, expand the current encoding prefix to prefix plus zero and prefix plus 1 respectively;

[0129] A4.5: After the recursive function traverses the entire tree, it returns the encoding mapping table to obtain the encoding mapping table of humidity data.

[0130] The risk assessment unit includes:

[0131] B1: Collect the equipment's material, surface treatment, usage environment, and historical rust record information, and generate equipment humidity data through integration;

[0132] B2: Set the humidity threshold for equipment rusting based on the collected equipment humidity data and ;

[0133] B3: Obtain the humidity prediction result of the humidity prediction unit, and build a rust risk assessment model based on the preset equipment rust humidity threshold. Use the rust risk assessment model to assess the rust risk of the equipment, and give the corresponding risk level based on the output result of the rust risk assessment model.

[0134] The specific steps of B3 include:

[0135] B3.1: Obtain humidity prediction results and preset equipment rust humidity threshold and ;

[0136] B3.2: Load a risk assessment framework, and determine the rust risk index in the risk assessment framework to obtain a rust risk assessment model, wherein the risk assessment framework is a prior art content in this field and is not an inventive solution of the present application, and is not described in detail here;

[0137] B3.3: Input the preset equipment rust humidity threshold into the rust risk assessment model, execute the risk assessment algorithm in the rust risk assessment model, and compare the humidity prediction result with the preset equipment rust humidity threshold;

[0138] Among them, the risk assessment algorithm takes the humidity prediction value and the preset equipment rust humidity threshold as input, and outputs the corresponding risk assessment result.

[0139] B3.4: Based on the output of the risk assessment algorithm, obtain the rust risk assessment results of the equipment , where RL represents the rust risk assessment result, PH represents the humidity prediction result, and T represents the preset equipment rust humidity threshold set. represents the risk assessment function;

[0140] B3.5: Classify the rust risk level according to the rust risk assessment results of the equipment ,in, Indicates that the rust risk level is high. Indicates that the rust risk level is low, triggers the early warning mechanism, and sends an early warning message. This means there is no risk of rust.

[0141] The scheduling optimization strategies for dehumidification equipment include:

[0142] C1: Collect data on rust risk level, early warning information, equipment usage and humidity change trend. Based on the collected data, determine the rust risk level and priority of equipment in different areas and formulate a scheduling strategy for dehumidification equipment.

[0143] The scheduling strategy of the dehumidification equipment in C1 includes the startup sequence, operation time, parameter setting and formulation of backup plans of the dehumidification equipment.

[0144] Specifically, they include:

[0145] (1) Start-up sequence: Determine the start-up sequence of the dehumidification equipment based on task priority and equipment performance, giving priority to areas or equipment with high rust risk levels or high importance;

[0146] (2) Operating time: According to the ambient humidity data and equipment performance, reasonably arrange the operating time of the dehumidification equipment, increase the operating time when the humidity is high, and reduce the operating time when the humidity is low, so as to achieve the purpose of energy saving and consumption reduction;

[0147] (3) Parameter setting: According to the equipment performance and environmental requirements, set the parameters of the dehumidification equipment, such as humidity setting value, wind speed, etc., to ensure that the parameter settings are reasonable and can meet the dehumidification requirements and reduce energy consumption;

[0148] (4) Backup plan: Develop a backup plan to deal with emergencies, such as equipment failure and abnormal humidity. The backup plan should include emergency response measures and a plan for the use of alternative equipment.

[0149] C2: Develop operation plan and parameter settings for dehumidification equipment based on the dehumidification equipment scheduling strategy;

[0150] C3: Start the dehumidification equipment and perform dehumidification according to the operation plan of the dehumidification equipment;

[0151] C4: Monitor the operation of the dehumidification equipment in real time, including changes in temperature and humidity, and dynamically adjust the operation plan and parameters of the dehumidification equipment based on the monitoring results and actual conditions.

[0152] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. A digital storage management system for manufacturing processing equipment, characterized in that: include: Data processing module, data analysis module, risk assessment module, equipment scheduling module, report generation module; The data processing module includes a humidity sensor unit and a data compression unit. The humidity sensor unit is used to collect humidity data of the device storage environment in real time. The data compression unit is configured with a data compression strategy, which is used to compress the pre-processed humidity data. The data analysis module is used to receive the compressed humidity data transmitted from the data processing module, decompress and analyze it, and identify the trend and law of humidity change; The risk assessment module includes a humidity prediction unit and a risk assessment unit. The humidity prediction unit is configured with a time series prediction algorithm, and the time series prediction algorithm is used to predict the humidity change of the equipment; the risk assessment unit is used to build a rust risk assessment model, and evaluate the rust risk level according to the humidity prediction result and the preset rust humidity threshold; The equipment scheduling module includes an optimization strategy unit and an equipment scheduling unit, wherein the optimization strategy unit is used to formulate a scheduling optimization strategy for the dehumidification equipment according to the rust risk level and the early warning information; The equipment scheduling unit is used to schedule the dehumidification equipment according to the scheduling optimization strategy of the dehumidification equipment; The report generation module is used to regularly generate reports summarizing the storage environment status of the equipment, the rust risk warning status, and the scheduling effect of the dehumidification equipment; The data compression unit adopts a data compression strategy, and the specific steps of the data compression strategy include: A1: Get the preprocessed humidity data, check each humidity value in the preprocessed humidity data set one by one, and for each humidity value, check whether the humidity value is already in the pre-built hash table; If it already exists, add 1 to the number of occurrences corresponding to the humidity value; If it does not exist, add the humidity value to the pre-built hash table and initialize the number of occurrences to 1; A2: After the traversal is completed, the hash table will contain all humidity values ​​and their corresponding occurrence times, and the frequency of occurrence of each humidity value in the preprocessed humidity data will be obtained. , and recorded as a frequency distribution table; A3: Based on the frequency information in the frequency distribution table, a priority tree is constructed, in which nodes with lower frequencies have higher priorities; A4: Generate a unique binary code for each humidity value from the priority tree to obtain a coding mapping table for humidity data; A5: Use the binary code in the humidity data encoding mapping table to replace the humidity value in the preprocessed humidity data, so as to obtain compressed humidity data; A6: The compressed humidity data is transmitted to the monitoring center via a wireless network.

2. A digital storage management system for manufacturing processing equipment as claimed in claim 1, characterized in that: The specific steps of constructing the priority tree in A3 include: A3.1: Read the frequency distribution table, obtain each humidity value and its corresponding frequency, and create a node for each humidity value and its frequency. At the same time, put all created nodes into a node list; A3.2: Select the two lowest-frequency nodes from the node list as the child nodes of the current operation, and create a new parent node for these two lowest-frequency nodes, and satisfy ,in, Represents the frequency value of the parent node, and Respectively represent the frequency values ​​of the two nodes with the lowest frequency.

3. A digital storage management system for manufacturing processing equipment as claimed in claim 2, characterized in that: The specific steps of constructing the priority tree in A3 also include: A3.3: Add the newly created parent node to the node list, remove the two selected nodes with the lowest frequency, and update the left and right child node pointers of the new node to point to the two originally selected nodes with the lowest frequency. The left and right child node pointers refer to the left and right child nodes of the new node. A3.4: Repeat the iteration until only one node is left in the node list, and obtain the root node of the priority tree; A3.5: After the construction is completed, output the structure of the priority tree.

4. A digital storage management system for manufacturing processing equipment as claimed in claim 3, characterized in that: The specific steps of A4 include: A4.1: Create an empty hash table as a coding mapping table to store humidity values ​​and their corresponding binary codes; A4.2: Define a recursive function and call it starting from the root node of the priority tree. In the recursive function, use a string variable as the encoding prefix of the current node and set the prefix to an empty string initially. A4.3: For the current node, if it is a leaf node in the priority tree, the current coding prefix is ​​associated with the humidity value and stored in the coding mapping table; A4.4: If the current node is not a leaf node in the priority tree, recursively process its left child node and right child node respectively, and when recursively called, expand the current encoding prefix to prefix plus zero and prefix plus 1 respectively; A4.5: After the recursive function traverses the entire tree, it returns the encoding mapping table to obtain the encoding mapping table of humidity data.

5. A digital storage management system for manufacturing processing equipment as claimed in claim 4, characterized in that: The risk assessment unit comprises: B1: Collect the equipment's material, surface treatment, usage environment, and historical rust record information, and generate equipment humidity data through integration; B2: Set the humidity threshold for equipment rusting based on the collected equipment humidity data and ; B3: Obtain the humidity prediction result of the humidity prediction unit, and build a rust risk assessment model based on the preset equipment rust humidity threshold. Use the rust risk assessment model to assess the rust risk of the equipment, and give the corresponding risk level based on the output result of the rust risk assessment model.

6. A digital storage management system for manufacturing processing equipment as claimed in claim 5, characterized in that: The specific steps of B3 include: B3.1: Obtain humidity prediction results and preset equipment rust humidity threshold and ; B3.2: Load a risk assessment framework, and in the risk assessment framework, determine the rust risk index to obtain a rust risk assessment model; B3.3: Input the preset equipment rust humidity threshold into the rust risk assessment model, execute the risk assessment algorithm in the rust risk assessment model, and compare the humidity prediction result with the preset equipment rust humidity threshold; B3.4: Based on the output of the risk assessment algorithm, obtain the rust risk assessment results of the equipment , where RL represents the rust risk assessment result, PH represents the humidity prediction result, and T represents the preset equipment rust humidity threshold set. represents the risk assessment function; B3.5: Classify the rust risk level according to the rust risk assessment results of the equipment ,in, Indicates that the rust risk level is high. Indicates that the rust risk level is low, triggers the early warning mechanism, and sends an early warning message. This means there is no risk of rust.

7. A digital storage management system for manufacturing processing equipment as claimed in claim 6, characterized in that: The scheduling optimization strategy of the dehumidification equipment includes: C1: Collect data on rust risk level, early warning information, equipment usage and humidity change trend. Based on the collected data, determine the rust risk level and priority of equipment in different areas and formulate a scheduling strategy for dehumidification equipment. C2: Develop operation plan and parameter settings for dehumidification equipment based on the dehumidification equipment scheduling strategy; C3: Start the dehumidification equipment and perform dehumidification according to the operation plan of the dehumidification equipment; C4: Monitor the operation of the dehumidification equipment in real time, and dynamically adjust the operation plan and parameters of the dehumidification equipment based on the monitoring results and actual conditions.

8. A digital storage management system for manufacturing processing equipment as claimed in claim 7, characterized in that: The scheduling strategy of the dehumidification equipment in C1 includes the start-up sequence, operation time, parameter setting and formulation of backup plans of the dehumidification equipment.

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