Intelligent wax block management method and system based on Internet of Things, computer equipment and readable storage medium

Abstract

The invention relates to the field of intelligent medical treatment, in particular to an intelligent wax block management method and system based on the Internet of Things, computer equipment and a readable storage medium, and the method comprises the following steps: distributing a unique digital identifier for each wax block, and associating the digital identifier with physical storage position information of the wax block; wax block storage environment data are collected in real time through multiple modes; dynamically adjusting microenvironment parameters of the storage unit based on the environment data; and responding to a received external query instruction, and sending a positioning instruction and a target calling instruction by constructing a multi-modal position index. The full-life-cycle digital management of the wax blocks is realized, centralized and unified management through a computer is also facilitated, and the management efficiency is improved. A unique identifier is assigned to each wax block, so that the wax blocks can be digitally associated with storage position information of the wax blocks, then the storage environment is maintained through environment monitoring and intelligent regulation and control, and finally rapid and efficient calling is achieved through intelligent positioning and carrying of a computer.

Description

Technical Field

[0001] The present application relates to the technical field of smart medical care, and in particular to an intelligent wax block management method, system, computer device and readable storage medium based on the Internet of Things. Background Art

[0002] Medical paraffin block filing cabinets are core equipment for pathology departments, medical research institutions, and forensic laboratories. They are used for the long-term storage of paraffin-embedded tissue samples, known as wax blocks. With the advancement of precision medicine and digital pathology, the demand for wax block storage has surged. Traditional manual management methods face challenges such as low efficiency, prone to errors, and insufficient space.

[0003] Existing wax block filing cabinets primarily rely on physical sorting and manual retrieval. While some advanced technologies attempt to introduce automation and digitization solutions, these solutions still have limitations. One approach involves RFID-based smart filing cabinets, which track the location of wax blocks through radio frequency identification. However, this relies on close-range scanning, lacks global positioning, and the labels are susceptible to moisture damage. Another approach involves automated high-bay warehouses, which employ robotic arms to transport wax block boxes. This provides high storage density but is costly, complex to maintain, and difficult to adapt to specimens of varying sizes.

[0004] To summarize, the first solution has the problem of missed readings due to signal interference and cannot monitor environmental data such as temperature and humidity in real time; the second solution has poor flexibility, is incompatible with non-standard wax blocks, and has a high risk of system paralysis during power outages. The overall wax block reading accuracy is low.

[0005] To address these issues, a technical solution is needed that can improve the accuracy of wax block reading and has good compatibility. Summary of the Invention

[0006] An intelligent wax block management method based on the Internet of Things, characterized by comprising the following steps:

[0007] Assigning a unique digital identifier to each wax block and associating the digital identifier with the physical storage location information of the wax block;

[0008] Collect wax block storage environment data in real time through multi-modality;

[0009] Dynamically adjust storage unit microenvironment parameters based on environmental data;

[0010] In response to the received external query instructions, the positioning instructions and target retrieval instructions are issued by building a multimodal location index.

[0011] By implementing this technical solution, digital management of wax blocks throughout their lifecycle is achieved. This is far more accurate and reliable than traditional paper records, and also facilitates centralized, unified computer management, significantly improving management efficiency. The system works by assigning a unique identifier to each wax block, digitally linking it to its storage location. Environmental monitoring and intelligent control are then used to maintain the storage environment. Finally, intelligent computer positioning and handling enable fast and efficient retrieval, making the entire management process digital and intelligent.

[0012] Furthermore, the digital identifier is a quantum dot fluorescence code and / or a blockchain hash value;

[0013] And / or, the real-time multi-modal acquisition of the wax block storage environment data includes acquiring at least one of temperature and humidity data, formaldehyde concentration, cabinet vibration data, optical data of detecting the wax block and / or label, and wax block position data, or a combination of several of the above;

[0014] And / or, the storage unit microenvironment parameters include temperature, humidity, air pressure, vibration, formaldehyde concentration, oxygen content, microbial density and dust particle count.

[0015] By employing the above technical solution, we achieve the combined use of multiple encoding methods and sensor data, making the management system more comprehensive. Quantum dot encoding provides anti-counterfeiting capabilities, while blockchain provides tamper-proof and traceability. Combining the two generates a unique and stable digital identity. Collecting multiple sensor data allows for monitoring multiple aspects of environmental conditions, enabling comprehensive control. The operating principle is to select and combine various encoding methods and sensor data types based on their respective strengths to build a robust and reliable management system.

[0016] Furthermore, the multimodal location index is constructed in the following manner:

[0017] Obtain the spatial location information of the wax block based on UWB ultra-wideband positioning technology;

[0018] Location verification via the digital identifier and / or spatial hash value of the blockchain record;

[0019] Combined with image record information to assist positioning.

[0020] By employing this technical solution, the physical location of the wax block can be quickly and accurately determined. UWB provides high-precision positioning, blockchain records serve as verification, and image data enhances robustness. Its operating principle is that multi-source heterogeneous positioning information complements each other, and different methods work together to ensure accurate and reliable positioning results.

[0021] Furthermore, the method for dynamically adjusting the microenvironment parameters of the storage unit based on environmental data includes:

[0022] Perform local filtering, denoising and normalization on the collected microenvironmental parameters to generate current environmental status information;

[0023] Construct a multi-parameter fusion decision tree model to generate dynamic control instructions based on the microenvironment parameter control algorithm and current environmental status information;

[0024] The dynamic control instruction is executed, and the real-time feedback of environmental data is used to verify whether the current environmental status information reaches the target storage unit microenvironment parameter. If not, continuous iterative control is performed.

[0025] By employing this technical solution, we achieve intelligent closed-loop control of environmental parameters. Data is first refined, then control instructions are generated using a decision tree algorithm, and finally, feedback is verified. This system operates by building an intelligent decision-making closed-loop control process that dynamically adjusts the environment to maintain optimal conditions and avoid impacting samples.

[0026] Furthermore, the method of issuing the target call instruction in the query response step includes:

[0027] The target wax block is retrieved by a magnetically levitated AGV robot or a storage cabinet ejection device, and the path planning is optimized based on the wax block call frequency;

[0028] And / or, the target position is indicated by projection or simulation display of AR glasses, combined with visual algorithms to achieve rapid positioning of the target wax block.

[0029] By employing this technical solution, query commands can be quickly converted into actual wax block handling operations. The AGV robot achieves automated handling, while the AR device implements interactive navigation. The system operates by receiving external query commands and rapidly locating and handling the corresponding wax blocks based on the command targets.

[0030] Furthermore, the input method of the external query instruction includes:

[0031] Semantic input, query targets are input through natural language instructions;

[0032] and / or, visual input, by taking a wax block or label image to query;

[0033] And / or, biometric input, verifying query permissions through fingerprint, iris or face recognition.

[0034] By adopting the above technical solutions, a variety of different query methods are supported. Voice, image, and biometric recognition provide flexible query methods. The working principle is that the system opens multiple query input interfaces, and users can choose the most appropriate method to query based on the scenario.

[0035] An intelligent wax block management system based on the Internet of Things, comprising:

[0036] an identification association subsystem for assigning a unique digital identification to each wax block and associating the digital identification with the physical storage location information of the wax block;

[0037] Multimodal data acquisition subsystem, used to collect wax block storage environment data in real time through multimodal methods;

[0038] An environmental control subsystem, used to dynamically adjust the storage unit microenvironmental parameters based on environmental data;

[0039] The command response subsystem is used to respond to received external query commands and issue positioning commands and call target commands by building a multi-modal location index.

[0040] By implementing the above technical solution, a complete end-to-end intelligent management system has been constructed. It consists of subsystems such as encoding, monitoring, control, and query. Each component has clear functions and works together to achieve fully automated intelligent management. Its operating principle is to organically combine the subsystems through digital linkage, ultimately achieving intelligent closed-loop control.

[0041] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flowchart of an intelligent wax block management method based on the Internet of Things in an embodiment.

[0044] Figure 2 The figure is a schematic structural diagram of an intelligent wax block management device based on the Internet of Things in one embodiment.

[0045] Figure 3 Schematic diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] Example 1

[0047] like Figure 1 As shown, this embodiment provides an intelligent wax block management method based on the Internet of Things, including the following steps:

[0048] The identification and association step assigns a unique digital identifier to each paraffin block and associates this digital identifier with its physical storage location. As each paraffin block enters storage, a unique digitally coded identifier is printed on a carrier such as an embedding cassette, blister film, or self-adhesive label using a printer or blister machine. This identifier may include information such as the sample library number, tissue type, and staining batch. This identifier is then associated with data such as the storage batch, storage time, and location to create an electronic archive.

[0049] In one embodiment, the digital identifier can be generated using quantum dot fluorescence encoding technology. Quantum dots offer advantages such as stable luminescence, resistance to photobleaching, and the ability to encode multiple types, with a coding capacity of up to billions of possible types. The identifier association step 1 is to print the quantum dot identifier on a wax block embedding cassette or label, which is then scanned and identified by an optical reader.

[0050] In another embodiment, the digital identification may also use other encoding technologies, such as RFID electronic tags, QR codes, bar codes, etc.

[0051] Furthermore, blockchain technology can be used to generate a unique hash value for each wax block. Blockchain technology, with its decentralized, tamper-resistant, and traceable characteristics, provides additional security for the wax blocks. The hash value and quantum dot code together form a dual digital identifier, improving recognition accuracy.

[0052] The multimodal data collection step collects multimodal data about the wax block storage environment in real time. During storage, environmental parameters such as temperature, humidity, and gas levels are collected daily, forming a continuous time-series data stream. If data exceeding limits is detected, the system automatically issues an alert and invokes cloud-based diagnostic services to analyze the cause and impact. In conjunction with the equipment management system, the system monitors the operating status of equipment such as air conditioners and dehumidifiers to determine whether they require repair or replacement. Data anomalies in each storage substep are also monitored to identify localized faults.

[0053] In one embodiment, the types of data collected may include: temperature and humidity data collected by temperature and humidity sensors, formaldehyde and other harmful substance concentration data collected by gas sensors, filing cabinet vibration data collected by acceleration sensors, wax block appearance and identification image data collected by CCD cameras, and wax block location data obtained by UWB positioning base stations. These various sensors are deployed at different locations in the storage sub-step to comprehensively monitor the microenvironmental state. Multimodal fusion is used to improve data reliability and accuracy.

[0054] In other embodiments, the type and quantity of sensors can be adjusted according to actual needs, such as adding a light intensity sensor, an air pressure sensor, etc.

[0055] The environmental control step dynamically adjusts the microenvironmental parameters of the storage substep based on collected environmental data to ensure the storage quality of the wax blocks. Parameters such as the air conditioning temperature and dehumidifier power in the warehouse are adjusted in real time according to preset thresholds. Adaptive fuzzy control and model-free control methods are used to overcome the time-varying characteristics of the system caused by dynamic changes in storage capacity and suppress fluctuations caused by external interference.

[0056] In one embodiment, the environmental control step includes a data preprocessing step, a multi-parameter fusion decision model, and a parameter control execution step.

[0057] The data preprocessing step performs filtering, denoising, and normalization on the multimodal data to improve data quality.

[0058] The multi-parameter fusion decision model comprehensively evaluates the impact of various parameters on wax block storage and generates optimal target environmental parameters. In one embodiment, this model can be constructed based on a fuzzy decision tree algorithm, weighing the importance of each parameter and outputting target values for temperature, humidity, air pressure, vibration, etc.

[0059] The control execution step controls the air conditioner, dehumidifier, pressure balance instrument, shock absorber, and other equipment according to the target parameters to achieve precise parameter adjustment, and the effect is verified through sensor feedback data. In other embodiments, the control model can also use intelligent optimization methods such as neural networks and genetic algorithms.

[0060] Furthermore, this embodiment also illustrates the working principle of the environmental control step. In one embodiment, the environmental control further comprises four steps: a threshold alarm, a parameter adjustment step, a predictive control step, and a manual intervention step.

[0061] The threshold alarm process monitors the real-time values of various environmental parameters to determine whether they exceed preset thresholds. These thresholds are set based on relevant standards and experience. For example, GB / T34539-2017, "Code for the Design of Archives and Warehouse Buildings," stipulates that the temperature of paper archives should not exceed 24°C, and the relative humidity should be controlled between 45% and 60%. Thresholds are set in multiple levels, with each 1°C or 5% RH increase in temperature and humidity registering a violation. If a parameter exceeds the limit multiple times or for an extended period, the system automatically issues an alert, prompting staff to take timely action. Alerts include audible and visual alarms, text messages, emails, and voice calls.

[0062] The parameter adjustment process uses threshold comparison results, combined with seasonal variations and equipment energy consumption, to develop optimized equipment control strategies. For example, when temperatures are too high in the summer, the system prioritizes the activation of variable-frequency air conditioners to increase cooling capacity while simultaneously shutting down dehumidifiers to prevent condensation from increasing the humidity load. When humidity is too low in the winter, the system increases the amount of fresh air introduced and, if necessary, activates a humidifier. The adjustment process utilizes algorithms such as PID and fuzzy control to achieve smooth parameter convergence. Methods such as bang-bang control can effectively suppress overshoot and maintain stability near the target value.

[0063] The predictive control process, based on historical trends in environmental parameters, utilizes methods such as time series analysis and machine learning to predict future trends and formulate proactive control strategies. For example, the autoregressive moving average (ARMA) model, combined with factors such as diurnal temperature differences and weather forecasts, can predict temperature and humidity profiles over the next 24 hours and adjust air conditioner start and stop times and temperature setpoints. Deep learning models such as long-short-term memory (LSTM) networks can further exploit the spatiotemporal correlations in historical data, improving prediction accuracy.

[0064] In exceptional circumstances, the system supports manual intervention and priority control. Administrators can remotely control a device by forcing it on or off, or by setting custom target parameters, via the web or app. For regular inventory checks, wax box sorting, and disinfection and dust removal, automated control can be suspended during designated time periods. The system includes an emergency switch and on-site control panel, ensuring manual operation in the event of a power outage or malfunction, ensuring the safety of both equipment and samples.

[0065] The system responds to commands, receives and processes external user queries, and enables rapid location and automatic retrieval of wax blocks. It monitors temperature and humidity trends in the warehouse in real time. If thresholds are exceeded, it activates backup air conditioning or dehumidifiers or switches to emergency control mode. Emergency control utilizes a PID algorithm to automatically adjust actuator output based on deviations, ensuring the temperature and humidity quickly return to the target range. It also sends an alarm signal to the host computer, prompting staff to check the equipment's operating status.

[0066] In one embodiment, external commands can be implemented through human-computer interaction methods such as voice input, visual input, and biometric verification. Users can directly speak keywords related to the wax block or scan an image of the wax block label with their mobile phone. The intelligent terminal then converts the command into a structured query statement. After verifying the legitimacy of the command, it then uses a multimodal location index to issue a command to locate the target wax block.

[0067] Positioning is accomplished using a triplet of information: a digital identifier, spatial coordinates, and the physical storage location. The system prioritizes accessing a distributed database to match the digital identifier and obtain the logical location. If a match fails, the UWB indoor positioning system is activated to scan the target area, acquiring the wax block's real-time spatial coordinates and combining this with the image information for auxiliary positioning. High-confidence positioning results trigger a retrieval command, enabling the automated removal of the wax block via an AGV, robotic arm, or ejection device. For frequently requested wax blocks, the system memorizes their inbound and outbound routes to optimize scheduling efficiency.

[0068] Furthermore, in one embodiment, the instruction response step includes three execution sub-steps: a semantic recognition sub-step, a positioning and navigation sub-step, and a wax block extraction sub-step.

[0069] The semantic recognition substep uses natural language processing and speech recognition technologies to convert the user's spoken instructions into structured query statements that the computer can understand. For example, if a user says "Search for Zhang San's lung tissue slices," the system first converts the speech signal into text using a speech recognition engine. Then, using NLP technologies such as named entity recognition and syntactic analysis, it extracts key information and maps it into a structured SQL or JSON query. In one embodiment, deep learning models such as LSTM+CRF are used to build an end-to-end semantic analysis process, directly learning the mapping relationship between speech signals and query statements. Synthetic data and transfer learning methods can effectively alleviate the problem of insufficient training data.

[0070] The positioning and navigation substep is responsible for quickly retrieving the exact location of the target wax block. This process consists of two steps: logical positioning and physical positioning. Logical positioning uses a hash table query to map the wax block number to the corresponding storage sub-storage number. Physical positioning uses multi-sensor fusion to obtain the spatial coordinates of the wax block within the substep. Indoor positioning technologies such as UWB, RFID, and inertial navigation can collaborate and complement each other. The positioning engine integrates all location evidence and uses confidence-weighted analysis to estimate the optimal position.

[0071] The wax block extraction sub-step receives the positioning results, plans the outbound route, and controls the extraction equipment to execute the outbound delivery. In one embodiment, an AGV cart with a laser radar is used to autonomously navigate to the target storage sub-step, and accurately grab the wax block through a robotic arm or suction cup. Path planning uses heuristic search algorithms such as A* and RRT, combined with real-time obstacle avoidance. Frequently used wax blocks are dynamically deployed near the entrances and exits to reduce transportation costs. In another embodiment, the storage sub-step is equipped with an electric lock and a spring ejection mechanism. The lock is opened by an electromagnet, and the servo motor controls the ejector rod to eject the shelf and stop it precisely at the outlet.

[0072] This embodiment further illustrates the method for collecting and fusing multimodal data in detail.

[0073] In one embodiment, the temperature and humidity sensor uses an SHT31 digital temperature and humidity sensor with a measurement range of -40 to 100% RH, a typical accuracy of ±2% RH and ±0.3° C., and outputs data once per second through an I2C interface.

[0074] The gas sensor adopts SP3SAQ2 type intelligent gas step, which can effectively detect the concentration of VOCs such as formaldehyde.

[0075] The CCD camera is a 2-megapixel industrial camera equipped with a 25mm fixed-focus lens, a frame rate of 60fps, a field of view of 52°, and a minimum working distance of 10cm.

[0076] The UWB positioning base station adopts the DecaWave DWM1000 procedure, with a bandwidth of 500MHz, an indoor positioning accuracy of 10cm, a maximum communication distance of 35m, a refresh frequency of 100Hz, and a power consumption of 160mW.

[0077] In another embodiment, the parameter settings of each sensor can be flexibly adjusted according to the storage environment and accuracy requirements. For example, the temperature and humidity sensor can use the higher-resolution Sensirion SHT85, which improves humidity accuracy to 1.5% RH and temperature accuracy to 0.1°C.

[0078] CCD cameras can use CMOS sensors with higher pixels to improve image quality.

[0079] UWB positioning can increase the number of base stations and shorten the ranging blind area.

[0080] Various sensors are networked through wireless communication protocols such as ZigBee and Wi-Fi to achieve low-power real-time transmission of data.

[0081] The data collection process aggregates heterogeneous, multi-source data to an edge gateway for initial screening using threshold rules and anomaly detection. High-quality data is synchronized to a cloud-based big data platform, using Kafka distributed message queues and the HDFS file system for data caching and persistence. The cloud platform uses parallel computing frameworks such as MapReduce and Spark to clean, extract features, and perform semantic mapping on multimodal data. Subsequently, machine learning algorithms, such as multivariate linear regression and support vector machines, are used to construct environmental assessment models, enabling quantitative characterization of storage parameters and trend prediction.

[0082] Example 2

[0083] Reference Figure 2 In this embodiment, an intelligent wax block management device based on the Internet of Things is provided. The intelligent wax block management device based on the Internet of Things corresponds one-to-one to the intelligent wax block management method based on the Internet of Things in the above embodiment.

[0084] An intelligent wax block management method based on the Internet of Things includes the following steps:

[0085] an identification assignment module that assigns a unique digital identification to each wax block and associates the digital identification with the physical storage location information of the wax block;

[0086] Environmental data acquisition module, which collects wax block and stores environmental data in real time through multi-modal methods;

[0087] Environmental control module, dynamically adjusts storage unit microenvironment parameters based on environmental data;

[0088] Query the corresponding module, respond to the received external query instructions, and issue positioning instructions and call target instructions by building a multimodal location index.

[0089] The specific definition of an IoT-based intelligent wax block management device can be found in the definition of an IoT-based intelligent wax block method described above and will not be repeated here. Each module in the IoT-based intelligent wax block device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules can be embedded in or independent of a processor within the device in hardware form, or can be stored in a memory within the device in software form, so that the processor can call and execute operations corresponding to each of the modules.

[0090] Example 3

[0091] Reference Figure 3 In this embodiment, a computer device is provided. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database for storing all data involved in a method for optimizing wireless charger transmission efficiency based on data analysis. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other computer devices that have application software deployed. When the computer program is executed by the processor, a method for optimizing wireless charger transmission efficiency based on data analysis is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or can be a key, trackball, or touchpad provided on the computer device housing, or can be an external keyboard, touchpad, or mouse.

[0092] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0093] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of a method for optimizing wireless charger transmission efficiency based on data analysis described in any of the above embodiments are implemented.

[0094] Example 4

[0095] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for optimizing wireless charger transmission efficiency based on data analysis described in any of the above embodiments are implemented.

[0096] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0097] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. An intelligent wax block management method based on the Internet of Things, characterized in that: The following steps are involved: Assigning a unique digital identifier to each wax block and associating the digital identifier with the physical storage location information of the wax block; Collect wax block storage environment data in real time through multi-modality; Dynamically adjust storage unit microenvironment parameters based on environmental data; In response to the received external query instructions, the positioning instructions and target retrieval instructions are issued by building a multimodal location index.

2. The intelligent wax block management method based on the Internet of Things according to claim 1, characterized in that: The digital identifier is a quantum dot fluorescence code and / or a blockchain hash value; And / or, the real-time multi-modal acquisition of the wax block storage environment data includes acquiring at least one of temperature and humidity data, formaldehyde concentration, cabinet vibration data, optical data of detecting the wax block and / or label, and wax block position data, or a combination of several of the above; And / or, the storage unit microenvironment parameters include temperature, humidity, air pressure, vibration, formaldehyde concentration, oxygen content, microbial density and dust particle count.

3. The intelligent wax block management method based on the Internet of Things according to claim 1, characterized in that: The multimodal location index is constructed in the following manner: Obtain the spatial location information of the wax block based on UWB ultra-wideband positioning technology; Location verification via the digital identifier and / or spatial hash value of the blockchain record; Combined with image record information to assist positioning.

4. The intelligent wax block management method based on the Internet of Things according to claim 1, characterized in that: The method for dynamically adjusting the microenvironment parameters of the storage unit based on environmental data includes: Perform local filtering, denoising and normalization on the collected microenvironmental parameters to generate current environmental status information; Construct a multi-parameter fusion decision tree model to generate dynamic control instructions based on the microenvironment parameter control algorithm and current environmental status information; The dynamic control instruction is executed, and the real-time feedback of environmental data is used to verify whether the current environmental status information reaches the target storage unit microenvironment parameter. If not, continuous iterative control is performed.

5. The intelligent wax block management method based on the Internet of Things according to claim 1, characterized in that: The input method of the external query instruction includes: Semantic input, query targets are input through natural language instructions; and / or, visual input, by taking a wax block or label image to query; And / or, biometric input, verifying query permissions through fingerprint, iris or face recognition.

6. The intelligent wax block management method based on the Internet of Things according to claim 1, characterized in that: The method of issuing the target call instruction in the query response step includes: The target wax block is retrieved by a magnetically levitated AGV robot or a storage cabinet ejection device, and the path planning is optimized based on the wax block call frequency; And / or, the target position is indicated by projection or simulation display of AR glasses, combined with visual algorithms to achieve rapid positioning of the target wax block.

7. An intelligent wax block management device based on the Internet of Things, characterized in that: include: an identification associating device for assigning a unique digital identification to each wax block and associating the digital identification with the physical storage location information of the wax block; A multimodal data acquisition device for acquiring wax block storage environment data in real time through multimodal means; An environmental control device for dynamically adjusting the storage unit microenvironmental parameters based on environmental data; The command response device is used to respond to the received external query command and issue positioning command and call target command by constructing a multi-modal location index.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. 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 6 are implemented.

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