Logistics warehousing environment adjustment method and system based on Internet of Things

Abstract

The present application relates to the field of environmental regulation technology, and discloses a logistics warehousing environment regulation method and system based on the Internet of Things. The method includes: obtaining the types of items in the warehouse, and setting the maximum value of the item's quality attribute as a reference attribute; searching the item environment mapping table according to the item type and reference attribute, and setting the environmental parameter corresponding to the maximum weight value as the target environmental parameter; periodically collecting external temperature data, calculating the internal and external temperature difference, and calculating the first regulation coefficient of the environmental control device based on the temperature difference, thereby calculating the first operating power and controlling its operation; periodically collecting the current environmental data in the warehouse, performing error compensation on the data therein, and generating corrected environmental data; searching the storage status mapping table based on the corrected environmental data, extracting the storage status corresponding to the maximum weight value, and judging whether the corrected environmental data is abnormal, and adjusting the environmental control device if abnormal. The present application improves the accuracy, flexibility, and intelligence level of storage environment regulation.

Description

Technical Field

[0001] The present application relates to the field of environmental regulation technology, and in particular to a logistics warehousing environment regulation method and system based on the Internet of Things. Background Art

[0002] In modern logistics and warehousing, regulating the storage environment is crucial for ensuring cargo quality and safety. Traditional methods for regulating the storage environment rely primarily on manual inspections and manually operated equipment, which suffer from low inspection efficiency and limited regulation accuracy. With the rapid development of the Internet of Things (IoT) technology, its application in the warehousing sector is gaining increasing attention. Leveraging sensor networks, data transmission, and intelligent control technologies, the IoT enables real-time monitoring and automated regulation of the storage environment.

[0003] Similar prior art includes a Chinese patent application with publication number CN118113089A, which discloses a warehouse environment control method, air conditioner and storage medium, which obtain preset temperature information and / or humidity information, temperature information and / or humidity information inside the warehouse, and temperature information and / or humidity information outside the warehouse; compare the temperature information and / or humidity information inside the warehouse, and the temperature information and / or humidity information outside the warehouse with the preset temperature information and / or humidity information, respectively, and obtain comparison results; and control the air conditioner to execute an air circulation plan inside the warehouse, or an air circulation plan inside and outside the warehouse based on the comparison results. There is also a Chinese patent application with publication number CN115291655A, which discloses a warehouse environment adjustment system, method and storage medium, including a monitoring system, an environment adjustment device and a background system; the monitoring system is used to collect environmental information of the warehouse, and send control instructions to the environment adjustment device according to the environmental information to control the operation of the environment adjustment device; if an alarm is required based on the environmental information, the alarm is processed and the alarm information is sent to the background system; the environment adjustment device is used to perform corresponding operations according to the control instructions to adjust the environment of the warehouse; the background system is used to provide alarm information query services to users based on the alarm information, and perform management and maintenance processing.

[0004] The above-mentioned existing technologies only consider environmental factors when adjusting environmental control equipment, and the adjustment method is single, the degree of intelligence is low, and there is a lack of in-depth processing and comprehensive analysis capabilities of environmental data. Therefore, it is an urgent problem to provide a logistics warehousing environment adjustment method and system based on the Internet of Things to improve the accuracy, flexibility and intelligence level of warehousing environment adjustment. Summary of the Invention

[0005] The present application provides a logistics warehousing environment adjustment method and system based on the Internet of Things, which is used to improve the efficiency, accuracy and responsiveness of logistics warehousing environment adjustment.

[0006] In a first aspect, the present application provides a method for adjusting a logistics warehousing environment based on the Internet of Things, the method comprising:

[0007] Step 1: Obtain the types of items in the warehouse, identify the quality attributes of all items, and set the maximum value of the quality attribute as the reference attribute;

[0008] Step 2: Search the item environment mapping table based on the item type and reference attribute, extract the maximum weight value corresponding to the reference attribute, define it as the first reference weight, and use the environment parameter corresponding to the first reference weight as the target environment parameter;

[0009] Step 3: Periodically collect external temperature data, calculate the temperature difference between the external temperature data and the target temperature data, execute a first predetermined algorithm on the temperature difference, obtain a first adjustment coefficient of the environmental control device, calculate a first operating power of the environmental control device based on the first adjustment coefficient, and control the environmental control device to operate at the first operating power;

[0010] Step 4: Periodically collect the current environmental data in the warehouse, extract any current environmental data, define it as the first data, determine whether the first data is within the corresponding preset range, and if so, perform error compensation on the first data to generate new environmental data. After traversing all current environmental data, generate the corrected environmental data;

[0011] Step 5: Search the storage status mapping table based on the corrected environmental data, extract the maximum weight value corresponding to the corrected environmental data, define it as the second reference weight, use the storage status corresponding to the second reference weight as the target storage status, and determine whether there is an abnormality in the corrected environmental data based on the target storage status. If so, adjust the environmental control equipment.

[0012] In combination with the first aspect, in the first implementation method of the first aspect of the present application, step 3 also includes: when the warehouse door is open, obtaining the exposed area of ​​the warehouse, executing a second predetermined algorithm on the exposed area and the temperature difference, obtaining the second adjustment coefficient of the environmental control device, defining the sum of the first adjustment coefficient and the second adjustment coefficient as a third adjustment coefficient, calculating the second operating power of the environmental control device based on the third adjustment coefficient, and controlling the environmental control device to operate according to the second operating power.

[0013] In combination with the first aspect, in the second implementation method of the first aspect of the present application, the time interval for collecting environmental data in the warehouse is adjusted based on the environmental disturbance factors, including: real-time monitoring of the environmental disturbance factors in the warehouse, when the environmental disturbance factors change, calculating the disturbance intensity within a first preset time, and adjusting the time interval according to preset rules based on the disturbance intensity.

[0014] In combination with the first aspect, in a third implementation of the first aspect of the present application, when the disturbance intensity increases, the time interval is reduced according to a first preset ratio;

[0015] When the disturbance intensity decreases, the time interval is increased according to a second preset ratio.

[0016] In combination with the first aspect, in a fourth implementation of the first aspect of the present application, in step 4, performing error compensation on the first data to generate new environmental data includes:

[0017] Step 41: extract historical environmental data corresponding to the first data within a second preset time, combine the historical environmental data with the first data to generate an environmental data sequence, and convert the environmental data sequence into a preset numerical type to obtain a first data sequence;

[0018] Step 42: Perform a spatial transformation operation on the first data sequence through the first transformation module to generate linear transformation data. At the same time, perform a third predetermined algorithm on the first data sequence through the second transformation module to obtain a prediction value corresponding to the first data.

[0019] Step 43: Execute a preset periodic function calculation on the transformed data and the predicted value to obtain an adjustment value, feed the adjustment value back to the first transformation module and the second transformation module, and return to step 42, repeating steps 42 to 43 until the transformed data and the predicted value converge;

[0020] Step 44: Correct the first data based on the transformed data and the predicted value to generate new environmental data.

[0021] In combination with the first aspect, in a fifth implementation of the first aspect of the present application, in step 42, performing a spatial transformation operation on the first data sequence by the first transformation module to generate linear transformation data includes:

[0022] A nonlinear function of the environmental monitoring device corresponding to the first data is obtained, a first-order partial derivative matrix is ​​constructed based on the nonlinear function, and the first data sequence is multiplied by the first-order partial derivative matrix to generate transformed data.

[0023] In combination with the first aspect, in a sixth implementation of the first aspect of the present application, adjusting the device operating parameters of the environment control device based on the moving object includes:

[0024] Real-time monitoring of whether there are moving objects in the warehouse. If there are, the area where the moving objects are located is identified and defined as the target area;

[0025] Extract the maximum temperature and minimum humidity in the target environmental parameters, adjust the equipment operating parameters of the first environmental control device based on the maximum temperature and minimum humidity, and adjust the blowing mode of the first environmental control device, wherein the first environmental control device is the environmental control device corresponding to the target area.

[0026] In combination with the first aspect, in a seventh implementation of the first aspect of the present application, adjusting the blowing mode of the first environmental control device includes:

[0027] Obtaining the positions of the first environmental control device, the mobile object, and all items in the target area, identifying a reference plane perpendicular to the ground on which the first environmental control device and the mobile object are located, and obtaining items that are not located on the reference plane, defining them as first items;

[0028] Surface temperatures of all first objects are obtained, objects corresponding to maximum surface temperatures are defined as selected objects, and the first environmental control device is controlled to blow air toward the selected objects.

[0029] In a second aspect, the present application provides a logistics warehousing environment adjustment system based on the Internet of Things, the system comprising: a data acquisition module, a parameter setting module, an equipment control module, a data acquisition module and an environment adjustment module;

[0030] The data acquisition module is used to obtain the types of items in the warehouse, identify the quality attributes of all items, and set the maximum value of the quality attribute as the reference attribute;

[0031] A parameter setting module is used to search the item environment mapping table according to the item type and reference attribute, extract the maximum weight value corresponding to the reference attribute, define it as the first reference weight, and use the environment parameter corresponding to the first reference weight as the target environment parameter;

[0032] a device control module, configured to periodically collect external temperature data, calculate a temperature difference between the external temperature data and target temperature data, execute a first predetermined algorithm on the temperature difference, obtain a first adjustment coefficient of the environmental control device, calculate a first operating power of the environmental control device based on the first adjustment coefficient, and control the environmental control device to operate at the first operating power;

[0033] The data acquisition module is used to periodically collect the current environmental data in the warehouse, extract any current environmental data, define it as the first data, determine whether the first data is within the corresponding preset range, and if so, perform error compensation on the first data to generate new environmental data. After traversing all the current environmental data, the corrected environmental data is generated;

[0034] The environmental adjustment module is used to search the storage status mapping table based on the corrected environmental data, extract the maximum weight value corresponding to the corrected environmental data, define it as the second reference weight, and use the storage status corresponding to the second reference weight as the target storage status. Based on the target storage status, it is determined whether there is an abnormality in the corrected environmental data. If so, the environmental control equipment is adjusted.

[0035] Compared with the prior art, the beneficial effects of the technical solution of this application are at least as follows:

[0036] 1. By identifying the types of items and reference attributes, the target environmental parameters corresponding to the item attributes are accurately set to ensure that the storage environment meets the storage requirements of the items, while extending the shelf life of the items and reducing energy consumption.

[0037] 2. Periodically collect external and internal environmental data, and calculate the temperature difference between the external temperature data and the target temperature data. Based on the temperature difference, dynamically adjust the operating power of the environmental control equipment to improve the flexibility of warehouse environment adjustment, achieve energy saving and consumption reduction, and reduce operating costs.

[0038] 3. Periodically collect current environmental data in the warehouse, perform error compensation and correction on the environmental data to improve data accuracy, judge abnormal conditions based on the corrected environmental data, and adjust environmental control equipment in a timely manner to quickly respond to environmental changes, avoid environmental control errors caused by data deviations, and maintain the stability of the storage environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 This is a schematic diagram of an embodiment of a method for adjusting a logistics warehousing environment based on the Internet of Things in an embodiment of the present application;

[0041] Figure 2 This is a schematic diagram of an embodiment of a new method for generating environmental data in an embodiment of the present application;

[0042] Figure 3 This is a schematic diagram of an embodiment of a logistics warehousing environment adjustment system based on the Internet of Things in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The embodiments of the present application provide a method and system for regulating a logistics warehousing environment based on the Internet of Things. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0044] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the method for adjusting the logistics warehousing environment based on the Internet of Things includes:

[0045] Step 1: Obtain the types of items in the warehouse, identify the quality attributes of all items, and set the maximum value of the quality attribute as the reference attribute.

[0046] Specifically, item information is obtained through automatic identification using IoT technologies (such as RFID, barcode scanning, and sensors) or input by management personnel. This information can include the name, type, and quantity of the item. Items include vegetables, seafood, meat products, medicines, and electronic products, and different types of items require different storage environments.

[0047] Quality attributes represent the freshness of an item (e.g., categorized as high, medium, and low). These attributes can be captured through image recognition technology or input into the system by human resources. Perishable items, such as vegetables, seafood, and meat, experience varying degrees of irreparable damage during transportation, resulting in changes in the item's freshness. Different quality attributes correspond to different storage conditions. For example, items with the highest quality attributes require stricter storage conditions to maintain long-term freshness, while items with medium quality attributes require standard storage conditions to maintain their current freshness. Stricter storage conditions (e.g., temperature of 2°C and humidity of 70%) require more energy from environmental control devices, while standard storage conditions (e.g., temperature of 4°C and humidity of 65%) consume fewer resources and costs. Providing storage conditions tailored to the item's quality attributes can reduce energy consumption while ensuring quality.

[0048] Step 2: Search the item environment mapping table based on the item type and reference attribute, extract the maximum weight value corresponding to the reference attribute, define it as the first reference weight, and use the environment parameter corresponding to the first reference weight as the target environment parameter.

[0049] Specifically, environmental parameters include temperature, humidity, light, air quality, etc. The item environment mapping table is a mapping table between item types, quality attributes, and optimal storage environments. The weight value between each environmental parameter and the item type and reference attribute is obtained through training based on historical data, and the strength of the relationship between the environmental parameter and the item type and reference attribute (i.e., the weight value) can be directly quantified. For example, the item type is vegetable A, the reference attribute is high, and the environmental parameter B1 has a probability of 0.8 to keep the quality attribute of vegetable A high, and the environmental parameter B2 has a probability of 0.95 to keep the quality attribute of vegetable A high, then the environmental parameter B2 corresponding to the maximum weight value of 0.95 is selected as the target environmental parameter.

[0050] Step 3: Periodically collect external temperature data, calculate the temperature difference between the external temperature data and the target temperature data, execute a first predetermined algorithm on the temperature difference, obtain a first adjustment coefficient of the environmental control device, calculate a first operating power of the environmental control device based on the first adjustment coefficient, and control the environmental control device to operate according to the first operating power.

[0051] Specifically, environmental control equipment includes air conditioners and cooling devices. Warehouse walls are exposed to the outside world. When the temperature inside the warehouse is lower than the outside temperature, heat loss occurs due to heat dissipation. Large temperature differences between the inside and outside of the warehouse result in greater heat loss. To maintain the target temperature inside the warehouse, the environmental control equipment requires higher operating power. When the temperature difference between the inside and outside of the warehouse is smaller, heat loss is smaller, and the environmental control equipment requires less operating power to maintain the target temperature inside the warehouse.

[0052] To achieve the above target temperature, the operating power of the environmental control equipment is PW, and the heat load generated is HL1, HL1=W×C1, where W is the adjustment coefficient and C1 is the cooling capacity provided by the environmental control equipment at the operating power PW; when the warehouse door is closed, the first heat loss caused by heat exchange between the inside and outside of the warehouse is HL2, HL2=T D ×W H ×S W , where T D is the above temperature difference, W H is the heat loss coefficient of the warehouse exterior wall, S W is the area of ​​the warehouse outer wall. To maintain the temperature in the warehouse, HL1 needs to be greater than or equal to HL2. The first adjustment coefficient W1 is further derived as the ratio of the heat loss HL2 to C1.

[0053] The operating power of the environmental control equipment in the entire warehouse is controlled based on the external temperature, which achieves precise control of equipment operation, improves the accuracy of environmental regulation, and avoids unnecessary energy waste.

[0054] Preferably, the environmental equipment mapping table is searched based on the inventory and the target environmental parameters, the maximum weight value corresponding to the inventory is extracted and defined as the third reference weight, the equipment operating parameters corresponding to the third reference weight are used as the target operating parameters, and the operating power of the environmental control equipment required to reach the above-mentioned target temperature is calculated based on the target operating parameters.

[0055] Specifically, the above-mentioned target operating parameters include (current, voltage, vibration frequency, etc.).

[0056] Step 4: Periodically collect the current environmental data in the warehouse, extract any current environmental data, define it as the first data, and determine whether the first data is within the corresponding preset range. If so, perform error compensation on the first data to generate new environmental data. After traversing all current environmental data, generate corrected environmental data.

[0057] Specifically, due to the physical characteristics and operating principles of environmental monitoring equipment, it exhibits nonlinear characteristics within certain measurement ranges. Nonlinearity means that the relationship between the sensor's output and input is not a simple linear relationship, resulting in errors between the detected data and the actual value. When the environmental data monitored by the environmental monitoring equipment falls within its corresponding preset range, correction can reduce these errors and improve data accuracy.

[0058] Step 5: Search the storage status mapping table based on the corrected environmental data, extract the maximum weight value corresponding to the corrected environmental data, define it as the second reference weight, use the storage status corresponding to the second reference weight as the target storage status, and determine whether there is an abnormality in the corrected environmental data based on the target storage status. If so, adjust the environmental control equipment.

[0059] Specifically, storage states include normal, high temperature, high humidity, high temperature and humidity, etc. The internal storage environment is relatively complex, and changes in a certain environmental parameter may also be caused by other environmental parameters. Machine learning of historical data (for example, through a neural network model) can quantify the relationship between environmental data and storage states through weights. For example, the environmental parameter is B3, which corresponds to two storage states C1 and C2. The weight value of C1 is 0.9 and the weight value of C2 is 0.3. Then C1 is defined as the target storage state. If the above-mentioned corrected environmental data does not exist in the storage state mapping table, the target storage state is obtained based on the historical environmental data with the greatest similarity to the corrected environmental data.

[0060] Obtaining the warehouse status through the warehouse status mapping table can comprehensively consider multiple factors and reduce misjudgments. It is also highly interpretable and can determine whether there are any abnormalities in the warehouse environment even if you lack experience.

[0061] Specifically, when the corrected environmental data indicates that there is an abnormality in the storage environment, the operating power, wind force or wind direction, etc. of the environmental control equipment are adjusted according to the corrected environmental data.

[0062] In a specific embodiment, step 3 also includes: when the warehouse door is open, obtaining the exposed area of ​​the warehouse, executing a second predetermined algorithm on the exposed area and the temperature difference, obtaining the second adjustment coefficient of the environmental control device, defining the sum of the first adjustment coefficient and the second adjustment coefficient as a third adjustment coefficient, calculating the second operating power of the environmental control device based on the third adjustment coefficient, and controlling the environmental control device to operate according to the second operating power.

[0063] Specifically, opening the warehouse door will also cause heat exchange between the inside and outside of the warehouse, resulting in heat loss inside the warehouse. The second heat loss caused is HL3, HL3=TD×SE, where SE is the exposed area of ​​the warehouse and the second adjustment coefficient is the ratio of the heat loss HL3 to C1.

[0064] The technical solution of the present invention not only takes into account external temperature changes, but also takes into account the impact of opening the warehouse door on the environment, making environmental regulation more comprehensive and accurate; it dynamically adjusts the operating power of the environmental control equipment according to actual conditions, avoids temperature fluctuations caused by opening the warehouse door, and improves the stability and reliability of environmental regulation.

[0065] In a specific embodiment, the time interval for collecting environmental data in the warehouse is adjusted based on environmental disturbance factors, including: real-time monitoring of the environmental disturbance factors in the warehouse, when the environmental disturbance factors change, calculating the disturbance intensity within a first preset time, and adjusting the time interval according to preset rules based on the disturbance intensity.

[0066] Specifically, the aforementioned environmental disturbance factors include access control status and / or inventory levels. When a warehouse door is opened, heat loss occurs within the warehouse, causing changes in the warehouse environment. Physical or chemical changes in items within the warehouse also affect the warehouse environment, and the amount of inventory directly affects the magnitude of this impact. For example, when a warehouse door is opened, the number of times the door is opened within a first preset time period (e.g., 2 hours) is calculated and used as the disturbance intensity value. When goods are deposited or withdrawn, the amount of goods in the warehouse after a first preset time period (e.g., 10 minutes) is calculated and used as the disturbance intensity value.

[0067] Dynamically adjusting the data collection time interval based on environmental disturbance factors can improve the system's response speed and adaptability, ensure the safe storage of items, and enhance warehouse management.

[0068] In a specific embodiment, when the disturbance intensity increases, the time interval is reduced according to a first preset ratio; when the disturbance intensity decreases, the time interval is increased according to a second preset ratio.

[0069] Specifically, when the intensity of environmental disturbance is low, the time interval for data collection is appropriately increased according to a preset ratio, and unnecessary data collection and processing are reduced, thereby saving computing resources and energy consumption; when the intensity of environmental disturbance is high, the time interval for data collection is appropriately reduced according to a preset ratio, so that the environmental monitoring equipment can measure changes in environmental conditions without omission.

[0070] In a specific embodiment, the remaining power of all environmental monitoring devices is obtained, and the time interval for data collection is adjusted based on the disturbance intensity and the remaining power of the environmental monitoring devices.

[0071] For example, when the environmental disturbance factor is the access control status and the disturbance intensity is 5 times and the remaining power of the environmental monitoring device is 90%, the time interval is set to 10 minutes; when the remaining power of the environmental monitoring device is 70%, the time interval is set to 15 minutes.

[0072] The technical solution of the present invention accurately controls the data acquisition frequency, so that the system can respond quickly to environmental changes and reduce unnecessary data acquisition when the environment is stable, thereby improving the overall stability of the system and extending the life of the environmental monitoring equipment.

[0073] In a specific embodiment, performing error compensation on the first data to generate new environmental data in step 4 includes:

[0074] Step 41: extract historical environmental data corresponding to the first data within a second preset time, combine the historical environmental data and the first data to generate an environmental data sequence, and convert the environmental data sequence into a preset numerical type to obtain a first data sequence.

[0075] Step 42: Perform a spatial transformation operation on the first data sequence through the first transformation module to generate linear transformation data. At the same time, execute a third predetermined algorithm on the first data sequence through the second transformation module to obtain a prediction value corresponding to the first data.

[0076] Step 43: Perform preset periodic function calculation on the transformed data and the predicted value to obtain the adjustment value, feed the adjustment value back to the first transformation module and the second transformation module, and return to step 42, repeating steps 42 to 43 until the transformed data and the predicted value converge.

[0077] Step 44: Correct the first data based on the transformed data and the predicted value to generate new environmental data.

[0078] The flowchart of the new environmental data generation method is as follows Figure 2 shown.

[0079] Specifically, the preset data type is floating-point data to facilitate subsequent mathematical operations. In the second transformation module, a predicted value corresponding to the first data is estimated using a least squares method, a moving average filter, an Euler integral, or a Kalman filter. The preset periodic function is a sine function, a cosine function, a tangent function, a cotangent function, a secant function, or a cosecant function.

[0080] In a specific embodiment, performing a spatial transformation operation on the first data sequence by the first transformation module in step 42 to generate linear transformation data includes:

[0081] A nonlinear function of the environmental monitoring device corresponding to the first data is obtained, a first-order partial derivative matrix is ​​constructed based on the nonlinear function, and the first data sequence is multiplied by the first-order partial derivative matrix to generate transformed data.

[0082] Specifically, the nonlinear function of the environmental monitoring equipment is constructed based on its performance and characteristics to describe the relationship between the detected data and the actual physical quantity. A first-order partial derivative matrix is ​​constructed based on the nonlinear function, and the nonlinear system relationship is linearized through differential operations. The first-order partial derivative matrix is ​​a Jacobian matrix, and the order of the matrix is ​​consistent with the number of data in the first data sequence. For example, if the first data sequence contains three values, the first-order partial derivative matrix is ​​a 3-order matrix.

[0083] For example, taking a temperature sensor as an example, the nonlinear function may be a polynomial function: , find the first-order partial derivative of the nonlinear function, and the constructed first-order partial derivative matrix is Assume that a is 0.1, b is 0.2, and the first data sequence is 22, 22.5, and 24, where 24 is the first data. The transformed data obtained by the first transformation module is , the predicted value corresponding to the first data obtained by the second transformation module is 23. A sine function calculation is then performed on the transformed data and the predicted value (using the transformed data and the predicted value as input values ​​of the sine function, respectively). The results of the sine function calculation are multiplied to obtain a first adjustment value (in this case, the first adjustment value is a matrix). The average of the values ​​on the diagonal of the first adjustment value is used as the adjustment value, and this value is fed back to the first transformation module and the second transformation module.

[0084] For example, the transformed data after convergence is , the predicted value is 23. Calculate the trace of the transformed data (14.3) and the difference between the predicted value and the first data (1), use the ratio of the difference to the trace (1 / 14.3) as the error weight, and use the sum of the predicted value and the error weight (23.1) as the new environmental data.

[0085] The technical solution of the present invention linearizes nonlinear data in a local area through a first transformation module, calculates the predicted value at the current moment through a second transformation module, estimates the dynamic change trend of the data, and then dynamically adjusts parameters according to the state (such as temperature) through a preset periodic function to compensate for nonlinear characteristics. Finally, the data, predicted value and detected value are comprehensively transformed to eliminate nonlinear errors and output accurate correction results. This can effectively solve the complexity of nonlinear data correction and improve the accuracy and reliability of data.

[0086] In a specific embodiment, adjusting operating parameters of an environmental control device based on a moving object includes:

[0087] (1) Real-time monitoring of whether there are moving objects in the warehouse. If there are, identify the area where the moving objects are located and define it as the target area.

[0088] (2) extracting the maximum temperature and minimum humidity values ​​from the target environmental parameters, adjusting the equipment operating parameters of the first environmental control device based on the maximum temperature and minimum humidity values, and adjusting the blowing mode of the first environmental control device, wherein the first environmental control device is the environmental control device corresponding to the target area.

[0089] Specifically, sensors (such as cameras, infrared sensors, and lidar) monitor the warehouse in real time for the presence of moving objects (such as people or transport machinery). If a moving object is detected, the area it is located in is identified and defined as a target area. These moving objects can be people or transport machinery. When the moving object is a person, low temperatures and humidity in the warehouse can cause discomfort. When the moving object is transport machinery, excessive humidity can cause equipment corrosion and performance degradation. When a moving object enters the warehouse, the operating parameters of the environmental control equipment in its area are adjusted based on the target environmental parameters. This ensures that the environmental conditions in the target area are always optimal, improving the comfort and safety of the moving object.

[0090] In a specific embodiment, adjusting the blowing mode of the first environmental control device includes:

[0091] (1) Obtain the positions of the first environmental control device, the mobile object, and all items in the target area, identify the reference plane perpendicular to the ground where the first environmental control device and the mobile object are located, and obtain items that are not located on the reference plane, which are defined as first items.

[0092] (2) Obtaining the surface temperatures of all first objects, defining the object corresponding to the maximum surface temperature as a selected object, and controlling the first environmental control device to blow air toward the selected object.

[0093] Specifically, the objects on the reference plane are all located on the horizontal line connecting the first environmental control device and the moving object. If air is blown towards these objects, the moving object will be affected (the human body will feel cold and the moisture exposed to mechanical equipment will increase). Blowing air towards the selected objects corresponding to the maximum surface temperature can quickly reduce the temperature of the selected objects while increasing the comfort and safety of the moving objects.

[0094] The following verifies the beneficial effects of the technical solution of the present invention from five aspects: environmental adjustment accuracy, energy efficiency, abnormal response speed, data accuracy and comprehensive benefits.

[0095] (1) Environmental adjustment accuracy experiment

[0096] Experimental design: a. Three warehouses of the same size (1000 m³) were selected and monitored using the traditional method, the existing IoT method, and the proposed method, respectively. b. Stored items: fresh fruits and vegetables (strictest storage conditions: temperature 2±0.5°C, humidity 70±5%). c. The monitoring period was 7 days (168 hours).

[0097] The result data is as follows:

[0098]

[0099] Conclusion: Through error compensation and target parameter optimization, the proposed method can improve the environmental parameter compliance rate by 12.7-30.1 percentage points and reduce the average deviation by 40-75%.

[0100] (2) Energy efficiency comparison experiment

[0101] Experimental design: a. Same external conditions (average daily temperature difference of 10°C); b. The warehouse door was opened eight times a day, each time for 5 minutes; c. The energy consumption of the environmental control equipment (air conditioning) was monitored.

[0102] The result data is as follows:

[0103]

[0104] Conclusion: The dynamic power regulation and exposed area compensation algorithm of this application can save an additional 20.1% of energy compared with the existing IoT method and 37.1% of energy compared with the traditional method.

[0105] (3) Abnormal response speed test

[0106] Experimental design: a. Simulate abnormal opening of the warehouse door (30 minutes); b. Monitor system response time and environmental recovery time.

[0107] The result data is as follows:

[0108]

[0109] Conclusion: The disturbance intensity monitoring and adaptive sampling interval of this application increased the response speed by 67-87%, significantly reducing the impact of anomalies.

[0110] (4) Data accuracy verification

[0111] Experimental design: a. Under a known standard environment (25.0℃, 50.0%); b. Comparison of sensor data correction effects using different methods.

[0112] The result data is as follows:

[0113]

[0114] Conclusion: The nonlinear correction algorithm proposed in this application improves data accuracy by about 80%, which is significantly better than other methods.

[0115] (5) Comprehensive benefit analysis

[0116] Long-term operating data (6 months):

[0117]

[0118] Conclusion: The technology in this application shows significant advantages in terms of item preservation, equipment maintenance and operating costs, and the overall energy efficiency ratio is improved by 35-83%.

[0119] The above experimental data confirm that the logistics warehousing environment adjustment method based on the Internet of Things described in this application has significant advantages in adjustment accuracy, energy efficiency, response speed and data accuracy, and can realize intelligent and precise warehousing environment management.

[0120] The above describes the logistics warehousing environment adjustment method based on the Internet of Things in the embodiment of the present application. The following describes the logistics warehousing environment adjustment system based on the Internet of Things in the embodiment of the present application. Figure 3 In an embodiment of the present application, an embodiment of the logistics warehousing environment adjustment system based on the Internet of Things includes: a data acquisition module 10, a parameter setting module 20, an equipment control module 30, a data acquisition module 40 and an environment adjustment module 50.

[0121] The data acquisition module 10 is used to acquire the types of items in the warehouse, identify the quality attributes of all items, and set the maximum value of the quality attribute as the reference attribute.

[0122] The parameter setting module 20 is used to search the item environment mapping table according to the item type and reference attribute, extract the maximum weight value corresponding to the reference attribute, define it as the first reference weight, and use the environment parameter corresponding to the first reference weight as the target environment parameter.

[0123] The device control module 30 is used to periodically collect external temperature data, calculate the temperature difference between the external temperature data and the target temperature data, execute a first predetermined algorithm on the temperature difference, obtain a first adjustment coefficient of the environmental control device, calculate the first operating power of the environmental control device based on the first adjustment coefficient, and control the environmental control device to operate according to the first operating power.

[0124] The data acquisition module 40 is used to periodically collect the current environmental data in the warehouse, extract any current environmental data, define it as the first data, and determine whether the first data is within the corresponding preset range. If so, the error compensation is performed on the first data to generate new environmental data. After traversing all the current environmental data, the corrected environmental data is generated.

[0125] The environmental adjustment module 50 is used to search the storage status mapping table according to the corrected environmental data, extract the maximum weight value corresponding to the corrected environmental data, define it as the second reference weight, and use the storage status corresponding to the second reference weight as the target storage status. Based on the target storage status, it is determined whether there is an abnormality in the corrected environmental data. If so, the environmental control equipment is adjusted.

[0126] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0128] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A logistics warehousing environment adjustment method based on the Internet of Things, characterized in that: The method comprises: Step 1: Obtain the types of items in the warehouse, identify the quality attributes of all items, and set the maximum value of the quality attribute as the reference attribute, where the quality attribute is the freshness of the item; Step 2: Searching an item environment mapping table based on the item type and the reference attribute, extracting the maximum weight value corresponding to the reference attribute, defining it as a first reference weight, and using the environment parameter corresponding to the first reference weight as the target environment parameter; Step 3: Periodically collect external temperature data, calculate the temperature difference between the external temperature data and the target temperature data, execute a first predetermined algorithm on the temperature difference to obtain a first adjustment coefficient of the environmental control device, calculate a first operating power of the environmental control device based on the first adjustment coefficient, and control the environmental control device to operate at the first operating power; Step 4: Periodically collect the current environmental data in the warehouse, extract any current environmental data, define it as first data, determine whether the first data is within the corresponding preset range, and if so, perform error compensation on the first data to generate new environmental data. After traversing all current environmental data, generate corrected environmental data; Step 5: Searching a storage state mapping table based on the corrected environmental data, extracting the maximum weight value corresponding to the corrected environmental data, defining it as a second reference weight, taking the storage state corresponding to the second reference weight as a target storage state, and determining whether the corrected environmental data is abnormal based on the target storage state. If so, adjusting the environmental control device to adjust the operating power, wind force, or wind direction of the environmental control device according to the corrected environmental data; After step 3, it also includes: when the warehouse door is opened, obtaining the exposed area of ​​the warehouse, executing a second predetermined algorithm on the exposed area and the temperature difference, obtaining the second adjustment coefficient of the environmental control device, defining the sum of the first adjustment coefficient and the second adjustment coefficient as a third adjustment coefficient, calculating the second operating power of the environmental control device based on the third adjustment coefficient, and controlling the environmental control device to operate according to the second operating power.

2. The method for adjusting the logistics warehousing environment based on the Internet of Things according to claim 1, characterized in that: Adjusting the time interval for collecting environmental data in the warehouse based on environmental disturbance factors includes: real-time monitoring of the environmental disturbance factors in the warehouse, calculating the disturbance intensity within a first preset time when the environmental disturbance factors change, and adjusting the time interval according to preset rules based on the disturbance intensity.

3. The method for adjusting the logistics warehousing environment based on the Internet of Things according to claim 2, characterized in that: When the disturbance intensity increases, reducing the time interval according to a first preset ratio; When the disturbance intensity decreases, the time interval is increased according to a second preset ratio.

4. The method for adjusting the logistics storage environment based on the Internet of Things according to claim 1, characterized in that: In step 4, performing error compensation on the first data to generate new environmental data includes: Step 41: extract historical environmental data corresponding to the first data within a second preset time period, combine the historical environmental data with the first data to generate an environmental data sequence, and convert the environmental data sequence into a preset numerical type to obtain a first data sequence; Step 42: Perform a spatial transformation operation on the first data sequence through a first transformation module to generate linear transformation data. Simultaneously, perform a third predetermined algorithm on the first data sequence through a second transformation module to obtain a prediction value corresponding to the first data. Step 43: Execute a preset periodic function calculation on the transformed data and the predicted value to obtain an adjustment value, feed the adjustment value back to the first transformation module and the second transformation module, and return to step 42, repeating steps 42 to 43 until the transformed data and the predicted value converge; Step 44: Correct the first data based on the transformed data and the predicted value to generate new environmental data.

5. The method for adjusting the logistics storage environment based on the Internet of Things according to claim 4 is characterized in that: In step 42, performing a spatial transformation operation on the first data sequence by a first transformation module to generate linear transformation data includes: Obtain a nonlinear function of the environmental monitoring equipment corresponding to the first data, construct a first-order partial derivative matrix based on the nonlinear function, multiply the first data sequence by the first-order partial derivative matrix to generate the transformed data.

6. The method for adjusting the logistics warehousing environment based on the Internet of Things according to claim 1, characterized in that: Adjusting the device operating parameters of the environmental control device based on the moving object includes: Real-time monitoring of whether there are moving objects in the warehouse. If there are, the area where the moving objects are located is identified and defined as the target area; Extract the maximum temperature and minimum humidity from the target environmental parameters, adjust the device operating parameters of the first environmental control device based on the maximum temperature and the minimum humidity, and adjust the blowing mode of the first environmental control device, wherein the first environmental control device is the environmental control device corresponding to the target area.

7. The method for adjusting the logistics warehousing environment based on the Internet of Things according to claim 6, characterized in that: The adjusting the blowing mode of the first environmental control device includes: Obtaining the positions of the first environmental control device, the mobile object, and all items in the target area, identifying a reference plane perpendicular to the ground on which the first environmental control device and the mobile object are located, and obtaining items not located on the reference plane, defining them as first items; Surface temperatures of all first objects are obtained, an object corresponding to a maximum surface temperature is defined as a selected object, and the first environmental control device is controlled to blow air toward the selected object.

8. The logistics warehousing environment adjustment system based on the Internet of Things is characterized by: The system includes: a data acquisition module, a parameter setting module, an equipment control module, a data acquisition module and an environment adjustment module; The data acquisition module is used to acquire the types of items in the warehouse, identify the quality attributes of all items, and set the maximum value of the quality attribute as a reference attribute, wherein the quality attribute is the freshness of the items; The parameter setting module is configured to search the item environment mapping table according to the item type and the reference attribute, extract the maximum weight value corresponding to the reference attribute, define it as a first reference weight, and use the environment parameter corresponding to the first reference weight as the target environment parameter; the device control module being configured to periodically collect external temperature data, calculate a temperature difference between the external temperature data and target temperature data, execute a first predetermined algorithm on the temperature difference, obtain a first adjustment coefficient of the environmental control device, calculate a first operating power of the environmental control device based on the first adjustment coefficient, and control the environmental control device to operate at the first operating power; The data acquisition module is used to periodically collect current environmental data in the warehouse, extract any current environmental data, define it as first data, determine whether the first data is within a corresponding preset range, and if so, perform error compensation on the first data to generate new environmental data. After traversing all current environmental data, corrected environmental data is generated; The environmental adjustment module is configured to search a storage state mapping table based on the corrected environmental data, extract a maximum weight value corresponding to the corrected environmental data, define the maximum weight value as a second reference weight, use the storage state corresponding to the second reference weight as a target storage state, and determine whether the corrected environmental data is abnormal based on the target storage state. If so, adjust the environmental control device; The equipment control module is also used to obtain the exposed area of ​​the warehouse when the warehouse door is open, execute a second predetermined algorithm on the exposed area and the temperature difference, obtain the second adjustment coefficient of the environmental control device, define the sum of the first adjustment coefficient and the second adjustment coefficient as a third adjustment coefficient, calculate the second operating power of the environmental control device based on the third adjustment coefficient, and control the environmental control device to operate according to the second operating power.

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