Intelligent vertical warehouse monitoring system

Abstract

The application discloses an intelligent vertical warehouse monitoring system, and relates to the technical field of intelligent warehouse automation.The system comprises an early warning response module for generating early warning signals of different levels according to a safety index of a vertical warehouse and triggering corresponding control instructions; and a control execution module for receiving the control instructions sent by the early warning response module and adjusting the operation parameters of environmental regulation equipment and logistics equipment in the vertical warehouse.The specific calculation formulas of an environmental risk coefficient, an equipment health coefficient and an inventory anomaly coefficient are designed, and a safety evaluation model of the vertical warehouse is established based on the three factors, so that an abstract safety state of the vertical warehouse is converted into quantifiable and comparable numerical values, a traditional subjective judgment mode is replaced, and objective and accurate evaluation of the safety level of the vertical warehouse is realized, so that potential safety hazards can be quickly and accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent warehouse automation, and particularly relates to an intelligent vertical warehouse monitoring system. BACKGROUND

[0002] With the rapid upgrading of modern logistics and intelligent manufacturing industry, as the core infrastructure of high-density storage and efficient turnover of goods, the application scale of vertical warehouses in the fields of e-commerce retail, automobile parts, and pharmaceutical cold chain continues to expand. The operation efficiency, storage safety, and operation stability of vertical warehouses have become the key to affecting the efficiency of supply chain circulation. Especially in the scenario of centralized management of multi-category goods and large flow access, vertical warehouses need to bear higher storage density, meet the strict requirements of medicine and food on temperature and humidity, and harmful gases, and cope with the reliability requirements of high-load operation of stacker, conveyor, and elevator. The traditional manual inspection and single-point monitoring mode has been difficult to adapt to the real-time and accurate management and control of intelligent vertical warehouses, and the construction of a multi-dimensional intelligent monitoring system has become inevitable in the industry.

[0003] At present, the monitoring technology of vertical warehouses has developed from manual management to the initial stage of automation. In the early stage, traditional vertical warehouses relied on manual inspection to record temperature and humidity, observed equipment status with the naked eye, and manually counted inventory, which had problems such as low data frequency, poor real-time performance, and large errors. In recent years, some vertical warehouses have introduced sensors to achieve local automation, such as using temperature and humidity sensors to collect environmental data, using vibration sensors to monitor the status of stackers, and using bar code scanning to count inventory. However, such technologies are mostly limited to a single dimension or device, lack system integration, environmental monitoring ignores the impact of volatile organic compounds on special goods, equipment monitoring only has simple threshold alarms without multi-parameter health assessment, and inventory monitoring lacks the ability to identify packaging damage and surface contamination. Moreover, data is stored in a scattered manner, which cannot form a linked analysis and is difficult to support overall safety judgment of the vertical warehouse.

[0004] From the actual application, there are still many problems to be solved in the existing monitoring technology. Firstly, the environmental monitoring dimension is incomplete and the early warning is lagging. Most systems do not include volatile organic compound monitoring, and it is difficult to capture the temperature and humidity change rate in real time, which can easily cause goods damage due to parameter mutation. Secondly, the equipment monitoring has no prediction ability and only relies on vibration and noise threshold alarms without building a health coefficient model, making it difficult to identify potential faults in advance, such as slow vertical vibration exceeding the standard of stackers and vibration changes caused by conveyor belt wear. Maintenance is mostly after-fault maintenance, which increases costs and affects operation. Thirdly, the inventory monitoring has insufficient accuracy and dimension. Manual or bar code counting is prone to errors, lacks packaging damage degree and pollution area identification means, and has no real-time comparison mechanism between actual and expected inventory, which can easily cause problems such as backlog, shortage, or untreated goods damage. Fourthly, multi-dimensional data is fragmented and there is no comprehensive evaluation. There is no unified safety model integration and analysis, making it difficult to output the overall safety index of the vertical warehouse, the early warning and control linkage is poor, and it is difficult to take timely control measures, which exacerbates the potential safety hazards.

[0005] Therefore, it is urgent to invent an intelligent vertical warehouse monitoring system to solve the above problems. SUMMARY

[0006] The present application aims to provide an intelligent vertical warehouse monitoring system to solve the problems raised in the background art.

[0007] To achieve the above object, the present application provides the following technical solution: an intelligent vertical warehouse monitoring system comprising the following modules:

[0008] The environmental monitoring module collects environmental state data through temperature and humidity sensors and gas concentration sensors deployed in the vertical warehouse to form an environmental monitoring dataset; the environmental monitoring dataset includes temperature distribution data, humidity distribution data and gas concentration data;

[0009] The equipment operation monitoring module collects mechanical vibration data through vibration sensors and noise sensors installed on the stacker, conveyor and elevator to form an equipment operation monitoring dataset; the equipment operation monitoring dataset includes stacker vibration data, conveyor vibration data and elevator noise data;

[0010] The inventory state monitoring module collects goods storage data through RFID readers and image recognition devices to form an inventory state monitoring dataset; the inventory state monitoring dataset includes goods quantity data and goods appearance data;

[0011] The data analysis module is used to analyze the environmental monitoring dataset, equipment operation monitoring dataset and inventory state monitoring dataset to obtain an environmental risk coefficient, an equipment health coefficient and an inventory anomaly coefficient, and input the environmental risk coefficient, equipment health coefficient and inventory anomaly coefficient into a vertical warehouse safety evaluation model to output a vertical warehouse safety index;

[0012] The calculation formula of the environmental risk coefficient is:

[0013] ,

[0014] Where, ΔT is the temperature change rate, T max is the maximum allowable temperature change rate, ΔH is the humidity change rate, H max is the maximum allowable humidity change rate, G voc is the volatile organic compound concentration, G std is the volatile organic compound concentration threshold, e is the natural constant, and α, β, γ are weight factors and satisfy α+β+γ=1;

[0015] The calculation formula of the equipment health coefficient is:

[0016] ,

[0017] wherein V s is the vertical vibration amplitude of the stacker, V s，max is the maximum vertical vibration amplitude allowed by the stacker, V c is the vibration amplitude of the conveyor belt, V c，max is the maximum vibration amplitude of the conveyor belt allowed, N d is the operating noise decibel value of the hoist, N d，max is the maximum noise decibel value allowed by the hoist;

[0018] The calculation formula of the inventory anomaly coefficient is:

[0019] ,

[0020] wherein Q a is the actual inventory quantity, Q e is the expected inventory quantity, D p is the degree of packaging damage, D p，max is the maximum allowed degree of packaging damage, S d is the surface contamination area ratio, S d，max is the maximum allowed surface contamination area ratio, max is the maximum value function;

[0021] The calculation formula of the three-dimensional warehouse safety evaluation model is:

[0022] ,

[0023] wherein I s is the three-dimensional warehouse safety index, C e is the environmental risk coefficient, C d is the equipment health coefficient, C i is the inventory anomaly coefficient;

[0024] The early warning response module is used to generate early warning signals of different levels according to the three-dimensional warehouse safety index, and trigger corresponding control instructions;

[0025] The control execution module is used to receive the control instructions issued by the early warning response module, and adjust the operating parameters of the environmental regulation equipment and the logistics equipment in the three-dimensional warehouse;

[0026] The data storage and management module is used to store the environmental monitoring data set, the equipment operation monitoring data set, the inventory state monitoring data set, the three-dimensional warehouse safety index and the historical early warning records, and provide a data query and analysis interface.

[0027] Preferably, the temperature distribution data includes temperature change rate; the humidity distribution data includes humidity change rate; and the gas concentration data includes volatile organic compound concentration.

[0028] Preferably, the stacker vibration data comprises a stacker vertical vibration amplitude; the conveyor vibration data comprises a conveyor belt vibration amplitude; and the hoist noise data comprises a hoist operation noise decibel value.

[0029] Preferably, the cargo quantity data comprises an actual inventory quantity and an expected inventory quantity; and the cargo appearance data comprises a package damage degree and a surface contamination area proportion.

[0030] Preferably, the early warning signal comprises a first-level early warning signal, a second-level early warning signal and a third-level early warning signal.

[0031] Preferably, the adjustment process of the control execution module is as follows:

[0032] receiving a control instruction issued by the early warning response module;

[0033] if the instruction corresponds to the second-level early warning signal, adjusting the operation parameters of the environment adjusting device, comprising: starting the ventilation system of the specified area, setting the target temperature as T target , and the target humidity as H target ; and reducing the operation speed of the associated logistics equipment to δ times of the rated speed, wherein 0 < δ < 1.

[0034] if the instruction corresponds to the third-level early warning signal, adjusting the operation parameters of the environment adjusting device and the logistics equipment, comprising: starting the emergency ventilation and air purification system of the whole warehouse area; forcibly stopping the equipment operation of which the health coefficient C d is lower than the threshold value D th ; and switching the operation mode of the equipment of which the health coefficient C d is higher than the threshold value D th to a low-speed safety mode, and limiting the operation speed to ε times of the rated speed, wherein 0 < ε < δ.

[0035] Technical effects and advantages of the present application:

[0036] 1. The present application breaks the limitation of traditional single-dimensional monitoring by deploying an environment monitoring module, an equipment operation monitoring module and an inventory state monitoring module, multi-dimensionally collecting the environment, equipment and inventory data of the stereoscopic warehouse and forming corresponding data sets, and realizes all-around, dead-angle-free control of the overall operation state of the stereoscopic warehouse, laying a comprehensive data foundation for subsequent safety evaluation.

[0037] 2. The present application realizes objective and accurate evaluation of the safety level of the stereoscopic warehouse by designing specific calculation formulas of the environment risk coefficient, the equipment health coefficient and the inventory abnormality coefficient, and constructing a stereoscopic warehouse safety evaluation model based on the three, and converts the abstract stereoscopic warehouse safety state into quantifiable and comparable numerical values, replaces the traditional subjective judgment mode, and realizes objective and accurate evaluation of the safety level of the stereoscopic warehouse, which can quickly and accurately identify potential safety hazards.

[0038] 3、The application realizes hierarchical early warning and accurate regulation by dividing the early warning signal into first, second and third levels, and matching differentiated control strategies for different early warning levels, such as starting ventilation in a specified area, reducing the speed of logistics equipment, starting emergency ventilation in the whole warehouse area, and forcibly stopping high-risk equipment or switching to low-speed mode for the third level early warning, which can not only avoid excessive intervention affecting operation efficiency under low risk, but also prevent loss expansion caused by insufficient response under high risk. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The system framework diagram of the application.

[0040] Figure 2 The module execution flowchart of the application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0042] The application provides an intelligent vertical warehouse monitoring system as shown in Figure 1 which comprises the following modules: an environment monitoring module, a device operation monitoring module, a inventory state monitoring module, a data analysis module, an early warning response module, a control execution module and a data storage and management module.

[0043] The application provides a module execution flowchart as shown in Figure 2 which specifically comprises:

[0044] The environment monitoring module collects environment state data through temperature and humidity sensors and gas concentration sensors deployed in the vertical warehouse to form an environment monitoring data set; the environment monitoring data set comprises temperature distribution data, humidity distribution data and gas concentration data.

[0045] It should be noted that the temperature and humidity sensor is an integrated temperature and humidity composite sensor that outputs digital signals, and the measurement range covers a temperature range of-40℃ to 85℃ and a relative humidity range of 0% to 100%, with a temperature accuracy of ±0.5℃ and a humidity accuracy of ±3%RH.

[0046] Further, in the above technical solution, the temperature distribution data comprises a temperature change rate; the humidity distribution data comprises a humidity change rate; and the gas concentration data comprises volatile organic compound concentration.

[0047] It should be noted that the temperature change rate is acquired in real time by integrated temperature and humidity composite sensors with digital signal output deployed in various functional areas of the automated warehouse. The sensors continuously measure the ambient temperature value at a fixed sampling interval Δt = 10 seconds, and record the temperature measurement values ​​T1, T2, ..., T in time series in the data storage and management module. n The instantaneous rate of change is calculated using the differential calculation module: obtaining the adjacent sampling time t. k With t k+1 Temperature value T k and T k+1 According to the formula ΔT=∣T k+1 -T k | / Δt calculates the temperature change per unit time, where Δt = t k+1 -t k =10 seconds, and the final output is a data stream of temperature change rate with timestamps;

[0048] The humidity change rate is measured using a digital temperature and humidity composite sensor integrated with temperature monitoring. Sampling is performed within the relative humidity range of 0% to 100% with an accuracy of ±3%RH. Humidity measurements H1, H2, ..., H are acquired at intervals of Δt = 10 seconds. n The real-time data processing unit performs time-series difference operations on the continuously sampled values ​​to extract adjacent time points t. m Humidity value H m With t m+1 Humidity value H m+1 According to the preset algorithm ΔH=|H m+1 -H m | / Δt calculates the rate of change of humidity per unit time, where the time step Δt is fixed at 10 seconds, and the calculation results are synchronously transmitted to the environmental monitoring dataset.

[0049] The concentration of volatile organic compounds (VOCs) is collected using semiconductor gas sensors distributed 1.5 meters above the ground in the shelving area. The sensors have a range of 0-1000 ppm and a detection resolution of 0.1 ppm. A gas sampling cycle is performed every 5 minutes: the sensor probe actively draws in air samples from the storage area, and the built-in catalytic combustion unit converts the VOC components into quantifiable electrical signals. The A / D conversion module outputs a standard 4-20mA analog signal, which is then converted into a digital concentration value G with 16-bit precision by the data acquisition card. voc Finally, the time series data of volatile organic compound concentrations are stored according to the region number.

[0050] The equipment operation monitoring module collects mechanical vibration data through vibration and noise sensors installed on stacker cranes, conveyors, and elevators, forming an equipment operation monitoring dataset; the equipment operation monitoring dataset includes stacker crane vibration data, conveyor vibration data, and elevator noise data;

[0051] It should be known that the vibration sensor includes a piezoelectric acceleration sensor and a capacitive micro-vibration sensor, a frequency response range is not less than 0.5 Hz to 2000 Hz, and a dynamic range is not less than 80 dB.

[0052] Further, in the technical scheme, the stacker vibration data includes a stacker vertical vibration amplitude; the conveyor vibration data includes a conveyor belt vibration amplitude; and the hoist noise data includes a hoist operation noise decibel value.

[0053] It should be known that the stacker vertical vibration amplitude is collected by installing a three-axis piezoelectric acceleration sensor on a stacker drive motor base, a sensor range is ±20 g, a frequency response is 0.5 Hz-2000 Hz, a sampling rate is set to 1 kHz; a sensor Z-axis, that is, a vertical direction output signal is filtered by a 4 kHz low-pass filter, and a double integral operation is performed by an integral processor: a time domain acceleration signal a z (t) is converted into a displacement amount , and a displacement peak value V s =max|d z (t) | is extracted in a 10 ms time window as the vertical vibration amplitude, and a measurement resolution reaches 0.01 mm.

[0054] The conveyor belt vibration amplitude is collected by a non-contact laser vibration measuring instrument, the vibration measuring instrument is installed at a position 0.5 m away from a belt surface of a conveyor support, emits a 650 nm wavelength laser beam, and captures a belt surface displacement at a 1 MHz sampling rate; after an original displacement signal s(t) is filtered by a 100 Hz high-pass filter to eliminate belt sag interference, a vibration component is extracted by time domain analysis: a vibration peak-to-peak value V c =max[s(t)]-min[s(t)] is calculated in a 1 s period, a measurement range is 0-30 mm, and an accuracy is ±0.1% FS.

[0055] The hoist operation noise decibel value is collected by an explosion-proof sound pressure sensor, the sensor is arranged at a position 1 m away from a hoist drive unit, is equipped with a 1 / 2 inch pre-polarized microphone, and has a frequency range of 20 Hz-12.5 kHz; after a sound pressure signal is filtered by an A-weighting network, an effective value in a 200 ms time window is calculated by an RMS detector, and is finally converted into a decibel value, a dynamic range is 30 dB-130 dB.

[0056] The inventory state monitoring module collects goods storage data by an RFID reader and a image recognition device, and forms an inventory state monitoring data set; the inventory state monitoring data set includes goods quantity data and goods appearance data.

[0057] It should be noted that the image recognition device includes a high-resolution industrial camera based on a CMOS image sensor and an infrared thermal imaging camera, with a resolution of not less than 1920×1080 pixels, a frame rate of not less than 30fps, and equipped with an adaptive optical zoom lens.

[0058] Furthermore, in the above technical solution, the quantity data of goods includes the actual inventory quantity and the expected inventory quantity; the appearance data of goods includes the degree of packaging damage and the percentage of surface contamination area.

[0059] It's important to know that the actual inventory quantity is collected through a fixed RFID reader array deployed in the rack aisles. The readers operate at a frequency of 902-928MHz with a transmission power of 30dBm, and the location tags are activated simultaneously during stacker crane operations. The system uses a time-slotted ALOHA anti-collision algorithm to identify valid tags in real time, count the quantity of goods per location, and finally summarize the total actual inventory Q for the warehouse area. a ;

[0060] The expected inventory quantity is obtained in real time from the warehouse management system via the OPC UA protocol. The system polls the data table of the warehouse management system database every 30 seconds, extracts the planned storage quantity of each storage location, and calculates the total expected inventory Qe by storage area after the data verification module verifies the consistency of the timestamp. The data interface transmission delay is ≤100ms.

[0061] The extent of packaging damage was assessed using a high-resolution industrial camera equipped with a 20-megapixel CMOS sensor. The camera was mounted at a 45-degree angle 1.2 meters above the conveyor, using a 200W LED array light source. Image acquisition was triggered when goods passed through the detection area. The packaging outline was extracted using the Mask R-CNN instance segmentation algorithm, and the pixel area A of the damaged region was calculated. d With total packaging area A t Degree of damage D p =A d / A t The detection resolution reaches 0.1mm. 2 / pixel, detection speed ≤1s / piece;

[0062] The percentage of surface contamination area was acquired using a multispectral imaging system, which includes a visible light camera (400-700nm) and a near-infrared camera (900-1700nm), combined with a laser 3D scanner with 0.1mm precision. Spectral reflectance analysis was performed on the cargo surface to identify the pixel coordinate set Ω of the contaminated area. c The actual polluted surface area was calculated by reconstructing the three-dimensional point cloud.

[0063] The data analysis module is configured to analyze the environment monitoring dataset, the equipment operation monitoring dataset and the inventory status monitoring dataset to obtain an environment risk coefficient, an equipment health coefficient and an inventory abnormality coefficient, and input the environment risk coefficient, the equipment health coefficient and the inventory abnormality coefficient into the stereoscopic warehouse safety evaluation model to output a stereoscopic warehouse safety index.

[0064] Further, in the above technical solution, the calculation formula of the environment risk coefficient is:

[0065] ,

[0066] wherein, ΔT is a temperature change rate, T max is a maximum allowable temperature change rate, ΔH is a humidity change rate, H max is a maximum allowable humidity change rate, G voc is a volatile organic compound concentration, G std is a volatile organic compound concentration threshold, e is a natural constant, and α, β and γ are weight factors and satisfy α+β+γ=1.

[0067] It should be noted that the weight factors α, β and γ are set as follows: based on statistical analysis of N groups of effective samples from a historical environment abnormality event database, the occurrence frequencies f T , f H , and f G of temperature mutation events, humidity mutation events and VOC exceeding events are extracted, and initial weights are calculated according to the formula , , Under the constraint conditions 0.3≤α, β and γ≤0.5 and α+β+γ=1, the initial weights are manually fine-tuned with a precision of 0.01 through a management interface, wherein N≥1000.

[0068] The temperature mutation event is determined as a temperature mutation event when the temperature change rate ΔT satisfies ≥0.9 in consecutive 3 sampling periods Δt, and the cumulative temperature change amplitude ≥5℃, wherein Δt=10 seconds.

[0069] The humidity mutation event is determined as a humidity mutation event when the humidity change rate ΔH satisfies ≥0.85 in consecutive 6 sampling periods Δt, and the cumulative humidity change amount ≥15%RH, wherein Δt=10 seconds.

[0070] The VOC exceeding event is determined as a VOC exceeding event when the volatile organic compound concentration G voc satisfies G voc >0.95×G std in two consecutive samplings, or the single sampling value Gvoc ≥ 1.2 x G std , the VOC is determined to be over-standard, wherein the sampling interval is 5 minutes;

[0071] The maximum allowable temperature change rate T max = 3℃ / min;

[0072] The maximum allowable humidity change rate H max = 20%RH / min;

[0073] The volatile organic compound concentration threshold G std = 100ppm.

[0074] Further, in the above technical solution, the calculation formula of the device health coefficient is:

[0075] ,

[0076] Wherein, V s is the vertical vibration amplitude of the stacker, V s，max is the maximum allowable vertical vibration amplitude of the stacker, V c is the belt vibration amplitude of the conveyor, V c，max is the maximum allowable belt vibration amplitude of the conveyor, N d is the operating noise decibel value of the elevator, N d，max is the maximum allowable noise decibel value of the elevator.

[0077] It should be noted that the maximum allowable vertical vibration amplitude V s，max of the stacker, the maximum allowable belt vibration amplitude V c，max of the conveyor, and the maximum allowable noise decibel value N d，max of the elevator are determined according to the manufacturer's technical documents of the stacker, the conveyor, and the elevator, such as the maximum allowable vertical vibration amplitude V s，max of the stacker, which is set according to the device model and the installation foundation, such as a concrete structure, and the typical value is between 0.5mm and 2mm, and the specific value needs to be checked in the device manual; the maximum allowable belt vibration amplitude V c，max of the conveyor depends on the type and load of the belt, and the typical value is in the range of 1mm to 5mm; and the maximum allowable noise decibel value N d，max of the elevator is typically between 75dB and 85dB.

[0078] Further, in the above technical solution, the calculation formula of the inventory abnormality coefficient is:

[0079] ,

[0080] Wherein, Q a is the actual inventory quantity, Q eD is the expected inventory quantity p D is the degree of package damage p，max S is the maximum allowable degree of package damage d S is the surface contamination area ratio d，max max is the maximum function.

[0081] It should be noted that the values of the maximum allowable degree of package damage D p，max and the maximum allowable surface contamination area ratio S d，max are set to D p，max = 5% and S d，max = 1%, wherein the value of the maximum allowable degree of package damage D p，max is set to 5% to meet the definition of "acceptable quality level" in general quality standards such as ISO9001 and ASTM D4169, allowing the existence of slight scratches or dents on the goods without affecting the core function, while adapting to the high-precision image detection capability of the patent system 0.1mm² / pixel, ensuring that small-scale damage can be accurately identified; the value of the maximum allowable surface contamination area ratio S d，max is set to 1% to meet the hygiene requirements of food and pharmaceutical storage in GMP and HACCP, and to realize high-sensitivity monitoring of the contaminated area through a multispectral imaging system to avoid cross-contamination risks.

[0082] Further, in the above technical solution, the calculation formula of the three-dimensional warehouse safety evaluation model is:

[0083]

[0084] wherein I s is the three-dimensional warehouse safety index, C e is the environmental risk coefficient, C d is the equipment health coefficient, and C i is the inventory anomaly coefficient.

[0085] The early warning response module is used to generate early warning signals of different levels according to the three-dimensional warehouse safety index, and trigger corresponding control instructions;

[0086] Further, in the above technical solution, the early warning signals include a first-level early warning signal, a second-level early warning signal, and a third-level early warning signal;

[0087] It should be noted that when the three-dimensional warehouse safety index satisfies the condition I s ≥ 0.8, a first-level early warning signal is generated, which is a green signal indicating that the system state is normal, and only a normal state prompt information is sent to the monitoring center;

[0088] When the three-dimensional warehouse safety index satisfies the condition 0.6 ≤ I s ​When <0.8, a secondary early warning signal is generated, which is a yellow signal, indicating that the system is mildly abnormal, sending a warning notice to the monitoring center and equipment maintenance personnel, prompting equipment inspection and environmental parameter adjustment;

[0089] When the safety index of the stereoscopic warehouse meets condition I s When <0.6, a tertiary early warning signal is generated, which is a red signal, indicating that the system has a serious risk, sending an emergency alert to the monitoring center, equipment maintenance personnel, and safety management responsible person, and automatically triggering the equipment degradation operation mode and the environment forced adjustment program.

[0090] The control execution module is configured to receive the control instruction issued by the early warning response module, and adjust the operation parameters of the environmental adjustment equipment and the logistics equipment in the stereoscopic warehouse.

[0091] Further, in the above technical solution, the adjustment process of the control execution module is:

[0092] Receiving the control instruction issued by the early warning response module;

[0093] If the instruction corresponds to a secondary early warning signal, the operation parameters of the environmental adjustment equipment are adjusted, including: starting the ventilation system of the specified area, setting the target temperature to T target , and the target humidity to H target ; reducing the operation speed of the associated logistics equipment to δ times of the rated speed, where 0<δ<1;

[0094] If the instruction corresponds to a tertiary early warning signal, the operation parameters of the environmental adjustment equipment and the logistics equipment are adjusted, including: starting the emergency ventilation and air purification system of the whole warehouse area; forcibly stopping the equipment operation of the health coefficient C d below the threshold value D th ; for the equipment with the health coefficient C d higher than the threshold value D th , switch its operation mode to a low-speed safety mode, and limit the operation speed to ε times of the rated speed, where 0<ε<δ.

[0095] It should be noted that the typical value of δ is set to 0.8, and the typical value of ε is set to 0.5;

[0096] In one embodiment, when the early warning response module generates a secondary early warning signal, i.e. a yellow alert and the comprehensive index is between 0.6 and 0.8, the control execution module will start the hierarchical regulation program, which first adjusts the environment, starts the intelligent ventilation system of the abnormal area, sets the target temperature to 22 degrees Celsius, allows a floating of plus or minus 0.5 degrees Celsius, which is a 3-degree Celsius rollback from the current temperature, and controls the target humidity to 45% relative humidity, allowing a floating of plus or minus 3%; secondly, the speed of the device is limited, and the running speed of the associated logistics equipment such as the stacker and the conveyor is reduced to 0.8 times the rated speed, i.e. a 20% reduction. For example, the horizontal running speed of the stacker will be reduced from 180 meters per minute to 144 meters per minute, and the conveyor belt speed will be reduced from 60 meters per minute to 48 meters per minute;

[0097] When the early warning is upgraded to a tertiary early warning signal, i.e. a red alert and the comprehensive index is less than 0.6, the system will execute the intensive intervention strategy. First, start the global environment control, turn on the emergency ventilation and air purification system of the entire warehouse area, and force the volatile organic compound concentration to be reduced to below 100 ppm within 15 minutes. Secondly, implement device hierarchical control, immediately execute the shutdown operation for devices with a health coefficient below 0.7, such as a stacker with excessive vibration; for devices with a health coefficient of 0.7 and above, switch to a low-speed safety mode, limiting their running speed to half of the rated speed, for example, the lifting speed of the elevator will be limited to 15 meters per minute from 30 meters per minute, and the conveyor belt speed will be further reduced to 24 meters per minute. The speed limit of this low-speed safety mode is always lower than the speed limit standard in the secondary early warning.

[0098] The data storage and management module is used to store the environment monitoring data set, the device running monitoring data set, the inventory status monitoring data set, the three-dimensional warehouse safety index and the historical early warning record, and provides a data query and analysis interface.

[0099] Finally, it should be noted that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some technical features, as long as they are within the spirit and principles of the present application. Any modification, equivalent substitution, improvement, etc. made within the scope of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent storage monitoring system, characterized by, Comprise the following modules: The environment monitoring module collects environment state data through temperature and humidity sensors and gas concentration sensors deployed in the stereoscopic warehouse, forming an environment monitoring dataset; the environment monitoring dataset includes temperature distribution data, humidity distribution data, and gas concentration data; The equipment operation monitoring module collects mechanical vibration data through vibration sensors and noise sensors installed on the stacker, conveyor, and elevator, forming an equipment operation monitoring dataset; the equipment operation monitoring dataset includes stacker vibration data, conveyor vibration data, and elevator noise data; The inventory status monitoring module collects goods storage data through RFID readers and image recognition devices, forming an inventory status monitoring dataset; the inventory status monitoring dataset includes goods quantity data and goods appearance data; The data analysis module analyzes the environment monitoring dataset, equipment operation monitoring dataset, and inventory status monitoring dataset to obtain environment risk coefficients, equipment health coefficients, and inventory anomaly coefficients, and inputs the environment risk coefficients, equipment health coefficients, and inventory anomaly coefficients into a stereoscopic warehouse safety evaluation model to output a stereoscopic warehouse safety index; The calculation formula of the environment risk coefficient is: ， where ΔT is the temperature change rate, T max is the maximum temperature change rate allowed, ΔH is the humidity change rate, H max is the maximum humidity change rate allowed, G voc is the volatile organic compound concentration, G std is the volatile organic compound concentration threshold, e is the natural constant, and α, β, γ are weight factors and satisfy α + β + γ = 1. The calculation formula of the equipment health coefficient is: ， wherein V s is the vertical vibration amplitude of the stacker, V s，max is the maximum vertical vibration amplitude allowed for the stacker, V c is the vibration amplitude of the conveyor belt, V c，max is the maximum vibration amplitude allowed for the conveyor belt, N d is the operating noise decibel value of the elevator, N d，max is the maximum noise decibel value allowed for the elevator; The calculation formula of the inventory anomaly coefficient is: ， wherein Q a is the actual inventory quantity, Q e is the expected inventory quantity, D p is the degree of packaging damage, D p，max is the maximum allowable degree of packaging damage, S d is the surface contamination area ratio, S d，max is the maximum allowable surface contamination area ratio, max is the maximum function; The calculation formula of the stereoscopic warehouse safety evaluation model is: ， Wherein, I s is a stereoscopic warehouse safety index, C e is an environmental risk coefficient, C d is a device health coefficient, C i is a stock anomaly coefficient; The early warning response module generates early warning signals of different levels according to the stereoscopic warehouse safety index and triggers corresponding control instructions; The control execution module receives the control instructions issued by the early warning response module and adjusts the operating parameters of the environmental regulation equipment and logistics equipment in the stereoscopic warehouse; The data storage and management module stores the environment monitoring dataset, equipment operation monitoring dataset, inventory status monitoring dataset, stereoscopic warehouse safety index, and historical warning records, and provides data query and analysis interfaces.

2. The intelligent storage monitoring system of claim 1, wherein, The temperature distribution data includes temperature change rate; the humidity distribution data includes humidity change rate; the gas concentration data includes volatile organic compound concentration.

3. The intelligent storage monitoring system of claim 1, wherein, The stacker vibration data includes stacker vertical vibration amplitude; the conveyor vibration data includes conveyor belt vibration amplitude; the elevator noise data includes elevator operating noise decibel value.

4. The intelligent storage monitoring system of claim 1, wherein, The goods quantity data includes actual inventory quantity and expected inventory quantity; the goods appearance data includes packaging damage degree and surface contamination area proportion.

5. The intelligent storage monitoring system of claim 1, wherein, The early warning signal includes a first-level early warning signal, a second-level early warning signal, and a third-level early warning signal.

6. The intelligent storage monitoring system of claim 5, wherein, The adjustment process of the control execution module is: Receive the control instructions issued by the early warning response module; If the instruction corresponds to a secondary warning signal, adjusting the operating parameters of the environmental conditioning device, including: starting the ventilation system of the designated area, setting the target temperature T target , the target humidity H target ; reducing the operating speed of the associated logistics equipment to δ times the rated speed, where 0 < δ < 1; If the instruction corresponds to a three-level warning signal, adjust the operating parameters of the environmental regulation equipment and the logistics equipment, including: start the emergency ventilation and air purification system of the whole warehouse area; forcibly stop the health coefficient C d below the threshold value D th of the equipment operation; for the equipment with health coefficient C d higher than the threshold value D th , switch its operation mode to a low-speed safety mode, and limit the running speed to ε times of the rated speed, where 0<ε<δ.

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