Hydropower station dangerous chemical intelligent warehouse management method and system based on RFID

Abstract

The invention discloses an RFID-based hydropower station dangerous chemical intelligent warehouse management method and system, and the method comprises the steps: deploying environment monitoring equipment, collecting environment parameters in real time, and collecting chemical information through an RFID reader; the chemical storage environment is dynamically adjusted; predicting a demand rate of chemicals in the adjusted chemical storage environment; generating a chemical supplement plan; and executing the supplement plan, and updating the stock state of the chemicals. The comprehensive monitoring of the chemical storage environment is realized; through data integration and preprocessing, the data quality is ensured, and the processing flow is simplified; the safety of a chemical storage environment is guaranteed, and potential safety accidents are avoided; through the demand prediction model, inventory management is more scientific and reasonable, and management efficiency and response speed are improved. Finally, comprehensive monitoring and intelligent management of the chemical storage environment are realized, the safety and reliability of chemical storage are ensured, and the efficiency and response speed of inventory management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent warehouse management, and in particular to an intelligent warehouse management method and system for hazardous chemicals in a hydropower station based on RFID. Background Art

[0002] As the demand for hazardous chemicals in hydropower stations continues to increase, traditional chemical storage management systems face many challenges. For a long time, the management of chemical warehouses in hydropower stations has mainly relied on manual records and regular inspections, which is not only inefficient but also prone to inaccurate information records and safety hazards. In recent years, with the rapid development of Internet of Things technology, wireless sensor networks (WSN) and radio frequency identification (RFID) technology, intelligent warehouse management systems have gradually become an effective means to improve the efficiency of chemical warehouse management. RFID technology has been widely used in chemical warehouse management due to its non-contact data reading capabilities and high-precision recognition advantages. By labeling chemicals with RFID tags, real-time tracking and efficient management of chemicals can be achieved, thereby improving the safety and management efficiency of chemical storage.

[0003] However, the existing chemical warehouse management system based on RFID technology still has some shortcomings. First, most systems can only provide basic inventory tracking functions and lack the ability to monitor and dynamically adjust the chemical storage environment in real time. Secondly, the demand forecast of chemicals usually relies on manual analysis of historical data and lacks intelligent forecasting models, making it difficult to adjust inventory plans in a timely manner according to actual demand changes. In addition, the existing system has shortcomings in data integration and preprocessing, and the data quality is not high, which affects the overall performance of the system. Finally, the traditional chemical replenishment plan formulation process is cumbersome and lacks automation mechanisms, making it difficult to respond to changes in chemical demand in a short period of time. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: insufficient environmental monitoring, inaccurate demand forecasting, shortcomings in data processing and low automation of supplementary planning in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent storage management method for hazardous chemicals in a hydropower station based on RFID, comprising:

[0007] Deploy environmental monitoring equipment to collect environmental parameters in real time and collect chemical information through RFID readers;

[0008] Integrate environmental parameters and chemical information into a unified database and perform pre-processing;

[0009] Based on environmental parameters and chemical information, analyze whether the chemical storage environment meets safety standards and dynamically adjust the chemical storage environment according to the analysis results;

[0010] Using historical data to train a demand forecasting model to predict the demand rate of chemicals in a regulated chemical storage environment;

[0011] Calculate the total demand for chemicals in the future based on the demand rate of chemicals and generate a chemical replenishment plan;

[0012] Execute replenishment schedule and update inventory status of chemicals.

[0013] As a preferred solution of the RFID-based intelligent warehouse management method for hazardous chemicals in a hydropower station described in the present invention, the environmental parameters include temperature, humidity and gas concentration;

[0014] The chemical information includes location information, storage status, usage frequency and consumption rate of the chemical.

[0015] As a preferred solution of the RFID-based intelligent warehouse management method for hazardous chemicals in hydropower stations described in the present invention, the preprocessing includes using the same processing rules for two types of data in the database to perform data cleaning, deduplication and format unification.

[0016] As a preferred solution of the RFID-based intelligent warehouse management method for hazardous chemicals in a hydropower station of the present invention, the analyzing whether the chemical storage environment meets the safety standards includes: if the current chemical storage environment meets the safety standards, there is no need to adjust the parameters;

[0017] If the current chemical storage environment does not meet the safety standards, the environmental parameters are adjusted, and the PID controller is used to calculate the optimal control intensity of the environmental parameter adjustment equipment. The expression is:

[0018]

[0019] Where u(t) represents the optimal control intensity of the environmental parameter adjustment device, K p It indicates the direct influence of the environmental parameter error value e(t) on the control quantity u(t). e(t) indicates the error value between the environmental parameter and the safety range. K r Indicates the degree of influence of the error value e(t) accumulated over time, Indicates the error change rate, K d Indicates the degree of influence of the error change rate on the control quantity;

[0020] The environmental parameter regulating device dynamically adjusts the chemical storage environment according to the optimal control intensity u(t) of the environmental parameter regulating device calculated by the PID controller.

[0021] As a preferred solution of the RFID-based intelligent warehouse management method for hazardous chemicals in a hydropower station described in the present invention, wherein: the demand forecasting model includes collecting historical data from the usage records of chemicals, including the usage frequency, storage time and consumption rate of the chemicals;

[0022] Select mean square error as the loss function;

[0023] Use the back-propagation algorithm to calculate the gradient of the loss function with respect to the model parameters and update the parameters based on the gradient;

[0024] Calculate the predicted value and loss function through forward propagation, start from the output layer and back propagate layer by layer, calculate the gradient of the loss function of each layer relative to the model parameters, and pass the gradient information from the previous layer to the next layer;

[0025] Based on the calculated gradients, an optimization algorithm is used to update the model’s parameters to minimize the loss function.

[0026] As a preferred solution of the RFID-based intelligent warehouse management method for hazardous chemicals in a hydropower station described in the present invention, the demand rate includes inputting chemical information into a trained demand prediction model to predict the demand rate of chemicals in the adjusted chemical storage environment, and the expression is:

[0027] Y(t)=ReLU(exp(-γ∥h(t-1)-x(t)∥ 2 )+b)

[0028] Among them, Y(t) represents the chemical demand rate at time t, γ is the width parameter of the Gaussian kernel, x(t) is the chemical information at time t, h(t-1) represents the model's memory state of chemical information at time (t-1), and b is the bias term.

[0029] As a preferred solution of the RFID-based intelligent warehouse management method for hazardous chemicals in hydropower stations described in the present invention, the total demand for chemicals in the future is calculated based on the demand rate of chemicals and the current inventory level, and the expression is:

[0030]

[0031] Among them, Q is the total amount of chemicals that need to be replenished in the predicted future time period T, I(t) represents the inventory level at the current time t, Y(t+i) represents the chemical demand rate at time t+1, and T represents the length of the predicted future time period; max means taking the maximum value so that the chemical compensation amount cannot be negative;

[0032] Based on the predicted total amount of chemicals Q that need to be replenished in the future time period T, a chemical replenishment plan is generated, including the types and quantities of chemicals to be replenished and the time points for replenishment.

[0033] An intelligent storage management system for hazardous chemicals in a hydropower station based on RFID using any of the methods described in the present invention is characterized by:

[0034] Data collection module: Environmental monitoring equipment is deployed at key locations in chemical warehouses to collect environmental parameters such as temperature, humidity, and gas concentration in real time, and transmit the data to the central control system through wireless communication technology. At the same time, RFID readers are used to collect location information, storage status, usage frequency, and consumption rate of chemicals;

[0035] Data integration and preprocessing module: The central control system receives environmental parameters transmitted by environmental monitoring equipment and chemical information collected by RFID readers, integrates them into a unified database, and performs preprocessing through data cleaning, deduplication and format unification to ensure data integrity and consistency;

[0036] Intelligent analysis and dynamic adjustment module: Based on the safety standards for chemical storage, the safety range of environmental parameters is set. The central control system receives environmental data in real time and determines whether the environmental parameters exceed the safety range based on the safety range.

[0037] Demand forecasting module: collects historical data from the usage records of chemicals, selects LSTM neural network as the basic model, uses historical data to train the demand forecasting model, and predicts the demand rate of chemicals in the adjusted chemical storage environment;

[0038] Inventory management module: Set safety inventory levels based on historical data and business needs, calculate the total demand for chemicals in the future based on the demand rate and current inventory level of chemicals, and generate a chemical replenishment plan, including the type, quantity and time of replenishment of chemicals;

[0039] Inventory status update module: The warehouse manager replenishes chemicals according to the chemical replenishment plan. After each replenishment, the inventory status of the chemicals is updated in real time through the RFID system, and the updated inventory status is recorded as the basis for subsequent environmental monitoring and chemical demand forecasting.

[0040] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0041] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0042] Beneficial effects of the present invention: The RFID-based intelligent warehouse management method for hazardous chemicals in hydropower stations provided by the present invention realizes comprehensive monitoring of the chemical storage environment and improves safety; through data integration and preprocessing, data quality is ensured and the processing flow is simplified; through intelligent analysis and dynamic adjustment, the safety of the chemical storage environment is guaranteed and potential safety accidents are avoided; through the demand forecasting model, inventory management is made more scientific and reasonable, and management efficiency and response speed are improved. Finally, the present invention realizes comprehensive monitoring and intelligent management of the chemical storage environment, ensures the safety and reliability of chemical storage, and improves the efficiency and response speed of inventory management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 An overall flow chart of an intelligent storage management method for hazardous chemicals in a hydropower station based on RFID provided in the first embodiment of the present invention;

[0045] Figure 2 An environmental judgment process in an RFID-based intelligent warehouse management method for hazardous chemicals in a hydropower station provided by the first embodiment of the present invention;

[0046] Figure 3 A framework diagram of an RFID-based intelligent storage management system for hazardous chemicals in a hydropower station provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides an intelligent warehouse management method for hazardous chemicals in a hydropower station based on RFID, comprising:

[0049] S1. Deploy environmental monitoring equipment to collect environmental parameters in real time and collect chemical information through RFID readers.

[0050] Furthermore, a series of environmental monitoring equipment, including temperature and humidity sensors and gas detectors, are deployed at key locations in chemical warehouses, including near shelves, entrances and exits, and high-risk areas.

[0051] Environmental monitoring equipment collects chemical storage environment parameters in real time, including temperature, humidity, gas concentration and other parameters, and transmits the data to the central control system via wireless communication.

[0052] Chemical information is collected through RFID readers, including the location information, storage status, usage frequency and consumption rate of the chemicals.

[0053] It should also be noted that these environmental monitoring devices transmit the collected parameters such as temperature, humidity and gas concentration to the central control system in real time through wireless communication technology, which facilitates real-time monitoring and timely response to abnormal situations. In addition, by collecting detailed information such as the location information, storage status, usage frequency and consumption rate of chemicals through RFID readers and writers, accurate management and tracking of chemicals can be achieved. This deployment can not only improve the safety of chemical storage, but also support intelligent management through real-time data, ensure that the storage environment of chemicals meets safety standards, and realize refined management of chemicals through RFID technology, improving overall storage efficiency and safety.

[0054] S2. Integrate environmental parameters and chemical information into a unified database and perform preprocessing.

[0055] Furthermore, the central control system receives environmental parameters transmitted by environmental monitoring equipment and chemical information collected by RFID readers.

[0056] Integrate environmental parameters and chemical information into a unified database (preprocessing in the same database can save resources, and the same processing rules can be used to process two types of data at the same time, improving resource utilization), and perform preprocessing, including data cleaning, deduplication and format unification.

[0057] It should also be noted that data cleaning refers to removing invalid or erroneous data records to ensure that the data in the database is accurate. For example, if a temperature and humidity sensor occasionally records an impossible temperature value (such as -500°C), this record needs to be deleted or corrected to avoid interference with subsequent data analysis.

[0058] Deduplication refers to removing duplicate data records to ensure that each data entry is unique. For example, if the temperature and humidity data at the same time point are recorded multiple times, one record needs to be retained and the remaining duplicate records removed to avoid data redundancy and statistical bias.

[0059] Format unification means converting all data into a consistent format to ensure data consistency and comparability. For example, ensure that all date and time records are in a consistent format.

[0060] S3. Based on environmental parameters and chemical information, analyze whether the chemical storage environment meets safety standards and dynamically adjust the chemical storage environment according to the analysis results.

[0061] Furthermore, based on the safety standards for chemical storage, a safe range of environmental parameters is set; for example, the safe range of temperature and humidity is 18°C-25°C, the relative humidity is 40%-60%, and the gas concentration does not exceed the safety limit.

[0062] The central control system receives environmental data from environmental monitoring equipment in real time, and at the same time, judges the received environmental parameters according to the safety range.

[0063] If the environmental parameters are within the safety range, the current chemical storage environment is considered to meet the safety standards. If the environmental parameters are beyond the safety range, the current chemical storage environment is considered to not meet the safety standards. The judgment process is as follows: Figure 2 shown.

[0064] The current chemical storage environment meets the safety standards and no parameters need to be adjusted. The current chemical storage environment does not meet the safety standards. Environmental parameters should be adjusted and the PID controller should be used to calculate the optimal control intensity of the environmental parameter adjustment device. The expression is:

[0065]

[0066] Where u(t) represents the optimal control intensity of the environmental parameter adjustment device, K p It indicates the direct influence of the environmental parameter error value e(t) on the control quantity u(t). e(t) indicates the error value between the environmental parameter and the safety range. K r Indicates the degree of influence of the error value e(t) accumulated over time, Indicates the error change rate, K d Indicates the degree of influence of the error change rate on the control quantity.

[0067] The environmental parameter adjustment device dynamically adjusts the chemical storage environment according to the optimal control intensity u(t) of the environmental parameter adjustment device calculated by the PID controller. For example, when the chemical storage environment does not meet the safety standards, the system will automatically initiate corresponding adjustment measures, such as turning on the air conditioner to cool down or starting the ventilation system to reduce the gas concentration to restore the safety state.

[0068] It should also be noted that the significance of this step is to ensure that the chemical storage environment always meets safety standards through real-time monitoring and automatic adjustment mechanisms, thereby improving the safety and reliability of chemical storage. Specifically, by setting the safety range of environmental parameters and dynamically adjusting the environmental parameters using a PID controller, it is possible to automatically start adjustment measures when environmental parameters exceed the safety range, such as adjusting the cooling intensity of the air conditioner or starting the ventilation system to reduce the gas concentration, thereby quickly restoring the safety state and effectively avoiding safety hazards caused by abnormal environmental parameters. This mechanism not only improves the level of automated management of chemical storage, but also significantly reduces the need for manual intervention and enhances the stability and response speed of the system.

[0069] S4. Use historical data to train a demand forecasting model to predict the demand rate of chemicals in the adjusted chemical storage environment.

[0070] Going a step further, historical data is collected from chemical usage records, including the frequency of chemical use, storage time, and consumption rate.

[0071] The LSTM neural network is selected as the basic model, and the historical data is used to train the LSTM neural network into a demand forecasting model that can predict the demand rate of chemicals. The LSTM neural network is selected as the basic model because it can handle nonlinear relationships in time series data and has a memory function.

[0072] The mean square error is chosen as the loss function to measure the difference between the predicted value and the true value. The specific process is: first calculate the difference between the model predicted value and the actual observed value, and then average the squares of these differences. The purpose of this is to amplify the impact of the prediction error, so that the model pays more attention to reducing the prediction error.

[0073] The back-propagation algorithm is used to calculate the gradient of the loss function with respect to the model parameters, and the parameters are updated based on the gradient. The specific process is: First, the prediction value is calculated through forward propagation and the loss function (such as mean square error) is calculated. Next, back-propagation is performed layer by layer starting from the output layer to calculate the gradient of the loss function of each layer with respect to the model parameters. This process involves the application of the chain rule to pass the gradient information from the previous layer to the next layer. Finally, based on the calculated gradient, an optimization algorithm (such as gradient descent) is used to update the parameters of the model to minimize the loss function.

[0074] The chemical information is input into the trained demand forecasting model to predict the demand rate of the chemical in the adjusted chemical storage environment. The expression is:

[0075] Y(t)=ReLU(exp(-γ∥h(t-1)-x(t)∥ 2 )+b)

[0076] Among them, Y(t) represents the chemical demand rate at time t, γ is the width parameter of the Gaussian kernel, x(t) is the chemical information at time t, h(t-1) represents the model's memory state of chemical information at time (t-1), and b is the bias term.

[0077] It should also be noted that by collecting historical data from the usage records of chemicals and using the LSTM neural network as the basic model for training, a demand forecasting model that can predict the demand trend of chemicals can be established. The LSTM neural network was chosen because it can effectively handle nonlinear relationships in time series data and has a memory function that helps capture long-term dependencies. By selecting the mean square error as the loss function and using the back propagation algorithm to calculate the gradient to update the model parameters, the predictive performance of the model can be continuously optimized to more accurately predict the demand rate of chemicals. Ultimately, by inputting chemical information into the trained model, the demand rate of chemicals in the adjusted chemical storage environment can be predicted in real time, thereby helping the inventory management system to better plan replenishment plans and ensure that inventory levels can meet demand without excess, thereby improving the efficiency and safety of chemical management.

[0078] S5. Calculate the total demand for chemicals in the future based on the demand rate of chemicals and generate a chemical replenishment plan.

[0079] Furthermore, a safety stock level S is set based on historical data and business needs. The specific process is: first, analyze the frequency of chemical use, storage time, and consumption rate in historical data to determine the fluctuation range of chemical demand; second, combine business needs, such as production plans, supply chain stability requirements, and potential supply disruption risks, to set a reasonable safety stock level S to ensure that there is enough inventory to cope with demand peaks or supply chain fluctuations. Business needs specifically refer to the goals set by the company based on its own operating conditions, including but not limited to production plan requirements, expected delivery time of customer orders, lead time in supply chain management, and risk management strategies, to ensure that inventory can meet daily operating needs and respond to emergencies.

[0080] Based on the demand rate and current inventory level of chemicals, the total demand for chemicals in the future is calculated as follows:

[0081]

[0082] Among them, Q is the total amount of chemicals that need to be replenished in the predicted future time period T, I(t) represents the inventory level at the current time t, Y(t+i) represents the chemical demand rate at time t+1, and T represents the length of the predicted future time period; max represents the maximum value so that the chemical compensation amount cannot be negative. The chemical replenishment plan includes the type and quantity of the chemicals to be replenished and the time point of replenishment.

[0083] It should also be noted that by calculating the total amount Q of chemicals that need to be replenished in the future time period T, the inventory management system can automatically generate a detailed replenishment plan, clearly specifying the types and quantities of chemicals to be replenished and the time points for replenishment, thereby improving the efficiency and accuracy of inventory management and enhancing the stability of the company's supply chain.

[0084] S6. Execute replenishment plan and update the inventory status of chemicals.

[0085] Furthermore, the warehouse manager replenishes chemicals according to the chemical replenishment plan. After each replenishment, the inventory status of the chemicals is updated in real time through the RFID system, and the updated inventory status of the chemicals is recorded as the basis for subsequent environmental monitoring and chemical demand forecasting.

[0086] It should also be noted that when the replenishment plan is generated, the warehouse manager performs replenishment operations according to the type, quantity and time of the planned chemicals. After each replenishment is completed, the inventory status of the chemicals is updated in real time through the RFID system. The RFID system reads the information on the RFID tag, confirms the location and quantity of the chemicals, and transmits this data to the central control system. The system automatically records the time, quantity and other information of the replenishment, and updates the inventory status of the chemicals. The updated inventory status information is recorded as the basis for subsequent environmental monitoring and chemical demand forecasting.

[0087] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0089] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0090] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0091] Example 2, reference Figure 3 , which is an embodiment of the present invention, provides an intelligent warehouse management system for hazardous chemicals in a hydropower station based on RFID.

[0092] Data collection module: Environmental monitoring equipment is deployed at key locations in chemical warehouses to collect environmental parameters such as temperature, humidity, and gas concentration in real time, and transmit the data to the central control system through wireless communication technologies (such as Wi-Fi and Zigbee). At the same time, the location information, storage status, usage frequency, and consumption rate of chemicals are collected through RFID readers.

[0093] Data integration and preprocessing module: The central control system receives environmental parameters transmitted by environmental monitoring equipment and chemical information collected by RFID readers, integrates these data into a unified database, and preprocesses them through data cleaning, deduplication and format unification to ensure data integrity and consistency.

[0094] Intelligent analysis and dynamic adjustment module: Based on the safety standards for chemical storage, the safety range of environmental parameters is set. The central control system receives environmental data in real time and determines whether the environmental parameters exceed the safety range based on the safety range. If the environmental parameters exceed the safety range, the PID controller is used to calculate the optimal control intensity of the environmental parameter adjustment equipment and dynamically adjust the chemical storage environment.

[0095] Demand forecasting module: Collect historical data from the usage records of chemicals, select LSTM neural network as the basic model, and use historical data to train the demand forecasting model. Select mean square error as the loss function and use the back propagation algorithm to update the model parameters. Input the chemical information into the trained model to predict the demand rate of chemicals in the adjusted chemical storage environment.

[0096] Inventory management module: Set safety inventory levels based on historical data and business needs. Calculate the total demand for chemicals in the future based on the demand rate and current inventory level of chemicals, and generate a chemical replenishment plan, including the type, quantity and time of replenishment of chemicals.

[0097] Inventory status update module: The warehouse manager replenishes chemicals according to the chemical replenishment plan. After each replenishment, the inventory status of the chemicals is updated in real time through the RFID system, and the updated inventory status is recorded as the basis for subsequent environmental monitoring and chemical demand forecasting.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent storage management method for hazardous chemicals in a hydropower station based on RFID, characterized in that: include: Deploy environmental monitoring equipment to collect environmental parameters in real time and collect chemical information through RFID readers; Integrate environmental parameters and chemical information into a unified database and perform pre-processing; Based on environmental parameters and chemical information, analyze whether the chemical storage environment meets safety standards and dynamically adjust the chemical storage environment according to the analysis results; Using historical data to train a demand forecasting model to predict the demand rate of chemicals in a regulated chemical storage environment; Calculate the total demand for chemicals in the future based on the demand rate of chemicals and generate a chemical replenishment plan; Execute replenishment schedule and update inventory status of chemicals.

2. The RFID-based intelligent storage management method for hazardous chemicals in a hydropower station according to claim 1, characterized in that: The environmental parameters include temperature, humidity and gas concentration; The chemical information includes location information, storage status, usage frequency and consumption rate of the chemical.

3. The RFID-based intelligent storage management method for hazardous chemicals in a hydropower station according to claim 2, characterized in that: The preprocessing includes using the same processing rules for the two types of data in the database to perform data cleaning, deduplication and format unification.

4. The RFID-based intelligent storage management method for hazardous chemicals in a hydropower station as claimed in claim 3 is characterized by: The analyzing whether the chemical storage environment meets the safety standards includes: if the current chemical storage environment meets the safety standards, there is no need to adjust the parameters; If the current chemical storage environment does not meet the safety standards, the environmental parameters are adjusted, and the PID controller is used to calculate the optimal control intensity of the environmental parameter adjustment equipment. The expression is: Where u(t) represents the optimal control intensity of the environmental parameter adjustment device, K p It indicates the direct influence of the environmental parameter error value e(t) on the control quantity u(t). e(t) indicates the error value between the environmental parameter and the safety range. K r Indicates the degree of influence of the error value e(t) accumulated over time, Indicates the error change rate, K d Indicates the degree of influence of the error change rate on the control quantity; The environmental parameter regulating device dynamically adjusts the chemical storage environment according to the optimal control intensity u(t) of the environmental parameter regulating device calculated by the PID controller.

5. The RFID-based intelligent storage management method for hazardous chemicals in a hydropower station as claimed in claim 4 is characterized by: The demand forecasting model includes collecting historical data from chemical usage records, including the frequency of use, storage time, and consumption rate of the chemical; Select mean square error as the loss function; Use the back-propagation algorithm to calculate the gradient of the loss function with respect to the model parameters and update the parameters based on the gradient; Calculate the predicted value and loss function through forward propagation, start from the output layer and back propagate layer by layer, calculate the gradient of the loss function of each layer relative to the model parameters, and pass the gradient information from the previous layer to the next layer; Based on the calculated gradients, an optimization algorithm is used to update the model’s parameters to minimize the loss function.

6. The RFID-based intelligent storage management method for hazardous chemicals in a hydropower station as claimed in claim 5, characterized in that: The demand rate includes inputting chemical information into a trained demand prediction model to predict the demand rate of the chemical in the adjusted chemical storage environment, and the expression is: Y(t)=ReLU(exp(-γ∥h(t-1)-x(t)∥ 2 )+b) Among them, Y(t) represents the chemical demand rate at time t, γ is the width parameter of the Gaussian kernel, x(t) is the chemical information at time t, h(t-1) represents the model's memory state of chemical information at time (t-1), and b is the bias term.

7. The RFID-based intelligent storage management method for hazardous chemicals in a hydropower station according to claim 6, characterized in that: Based on the demand rate and current inventory level of chemicals, the total demand for chemicals in the future is calculated as follows: Among them, Q is the total amount of chemicals that need to be replenished in the predicted future time period T, I(t) represents the inventory level at the current time t, Y(t+i) represents the chemical demand rate at time t+1, and T represents the length of the predicted future time period; max means taking the maximum value so that the chemical compensation amount cannot be negative; Based on the predicted total amount of chemicals Q that need to be replenished in the future time period T, a chemical replenishment plan is generated, including the types and quantities of chemicals to be replenished and the time points for replenishment.

8. An RFID-based intelligent storage management system for hazardous chemicals in a hydropower station using the method according to any one of claims 1 to 7, characterized in that: Data collection module: Environmental monitoring equipment is deployed at key locations in chemical warehouses to collect environmental parameters such as temperature, humidity, and gas concentration in real time, and transmit the data to the central control system through wireless communication technology. At the same time, RFID readers are used to collect location information, storage status, usage frequency, and consumption rate of chemicals; Data integration and preprocessing module: The central control system receives environmental parameters transmitted by environmental monitoring equipment and chemical information collected by RFID readers, integrates them into a unified database, and performs preprocessing through data cleaning, deduplication and format unification to ensure data integrity and consistency; Intelligent analysis and dynamic adjustment module: Based on the safety standards for chemical storage, the safety range of environmental parameters is set. The central control system receives environmental data in real time and determines whether the environmental parameters exceed the safety range based on the safety range. Demand forecasting module: collects historical data from the usage records of chemicals, selects LSTM neural network as the basic model, uses historical data to train the demand forecasting model, and predicts the demand rate of chemicals in the adjusted chemical storage environment; Inventory management module: Set safety inventory levels based on historical data and business needs, calculate the total demand for chemicals in the future based on the demand rate and current inventory level of chemicals, and generate a chemical replenishment plan, including the type, quantity and time of replenishment of chemicals; Inventory status update module: The warehouse manager replenishes chemicals according to the chemical replenishment plan. After each replenishment, the inventory status of the chemicals is updated in real time through the RFID system, and the updated inventory status is recorded as the basis for subsequent environmental monitoring and chemical demand forecasting.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any method as claimed in claim 1 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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