Intelligent warehouse management method and system based on adaptive environment

Abstract

The invention relates to an intelligent warehouse management method and system based on a self-adaptive environment, and the method comprises the steps: obtaining the environment monitoring data of a warehouse, carrying out the standardization processing of the environment monitoring data, removing the noise and errors in the environment monitoring data, and converting the environment monitoring data into the same magnitude. And analyzing the standardized environmental monitoring data through a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and judging whether to adjust the environmental condition of the warehouse based on the environmental prediction data. And when the environment condition of the warehouse needs to be adjusted, receiving an environment adjustment instruction containing an environment adaptive decision, and controlling an environment control device in the warehouse to adjust the environment condition according to the environment adaptive decision in response to the environment adjustment instruction. According to the method, the warehouse environment can be monitored in real time, the environment self-adaptive decision can be predicted and made in real time to adjust the warehouse environment condition, and the environment adaptability of the warehouse and the real-time performance of environment condition adjustment are further improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing technology, and in particular to an intelligent warehousing management method and system based on an adaptive environment. Background Art

[0002] In the context of the current global market, the development of intelligent warehousing technology is one of the key trends in the field of logistics and supply chain management. In addition, with the booming development of e-commerce and consumers' increasing expectations for instant gratification, traditional warehouse management systems are facing major challenges.

[0003] Currently, existing smart warehousing technologies primarily focus on automated and robotic systems. These systems, by integrating advanced Internet of Things (IoT) technologies, artificial intelligence (AI), machine learning, and big data analytics, have significantly improved the efficiency and accuracy of warehouse management. Robotic warehouse automation can automatically transport shelves to designated picking stations, reducing manual picking time and travel distance. This approach improves the efficiency of intelligent warehouse operations by optimizing picking routes and cargo storage strategies through algorithms. Similarly, the use of highly integrated logistics management software and automated equipment (including automated forklifts and conveyor belts) enables rapid sorting and packaging of goods, as well as efficient inventory management. Furthermore, some advanced warehouse management systems have begun implementing environmental monitoring, using sensors to monitor temperature and humidity within the warehouse and automatically adjust them to maintain optimal storage conditions. These systems can optimize warehouse environmental settings through real-time data analysis, ensuring the quality and storage safety of sensitive goods such as pharmaceuticals and food. However, the dynamic environmental monitoring and adaptive adjustment capabilities of these existing technologies need further improvement. Otherwise, they may struggle to adjust temperature, humidity, and light intensity within the warehouse to accommodate sudden changes in environmental conditions, potentially impacting the quality and safety of stored goods. Secondly, while the above-mentioned existing technologies use data analysis to optimize warehouse management, their comprehensive analysis of environmental data and inventory requirements needs to be further improved. Thirdly, the above-mentioned existing technologies require high system complexity, resulting in high training costs for professional technicians and equipment maintenance costs.

[0004] In summary, the existing intelligent warehousing technology is relatively expensive, and its environmental adaptability and real-time adjustment of environmental conditions need to be further improved. Summary of the Invention

[0005] Based on this, it is necessary to provide an intelligent warehousing management method and system based on an adaptive environment that can reduce costs and has high real-time environmental adaptability and environmental condition adjustment to address the above technical problems.

[0006] The present invention provides an intelligent warehouse management method based on an adaptive environment, the method comprising:

[0007] Acquire environmental monitoring data of the warehouse, and perform standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data to the same magnitude;

[0008] Analyzing the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determining whether to adjust the environmental conditions of the warehouse based on the environmental prediction data;

[0009] When the environmental conditions of the warehouse need to be adjusted, receiving an environmental adjustment instruction including an environmental adaptive decision, and controlling the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision in response to the environmental adjustment instruction;

[0010] Among them, the environmental monitoring data includes the temperature, humidity and light intensity of the warehouse, the environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse, and the environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

[0011] In one embodiment, obtaining environmental monitoring data of a warehouse and performing standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data to the same level include:

[0012] The temperature sensors, humidity sensors and light sensors installed at different locations in the warehouse are used to obtain temperature data, humidity data and light intensity at multiple locations in the warehouse at a first moment;

[0013] Based on the environmental monitoring data and weight coefficients measured by different sensors at the first moment, the environmental status index of the warehouse at the first moment is calculated by weighted summation, and the weight coefficient is determined according to the sensor type and the location of the sensor.

[0014] In one embodiment, the step of obtaining environmental monitoring data of a warehouse and performing standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data to the same level further includes:

[0015] Based on the environmental monitoring data of the warehouse at the first moment, obtaining a raw data vector of the environmental monitoring data at the first moment, and calculating a mean and a standard deviation of the raw data vector;

[0016] Calculating a standardized data vector of the original data vector at the first moment according to the mean and standard deviation of the original data vector at the first moment, so as to perform standardization processing on the environmental monitoring data;

[0017] The original data vector includes multi-dimensional data of temperature, humidity and light intensity in the warehouse, and the standardized data vector is the ratio of the difference between the original data vector and the mean to the standard deviation.

[0018] In one embodiment, analyzing the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determining whether to adjust the environmental conditions of the warehouse based on the environmental prediction data, includes:

[0019] Calling the machine learning algorithm to process the standardized environmental monitoring data to identify the change trend of each data in the environmental monitoring data;

[0020] The machine learning algorithm is trained based on the changing trend of the environmental monitoring data in combination with the historical environmental monitoring data of the warehouse to construct an environmental prediction model for predicting changes in warehouse environmental conditions within a first future time period.

[0021] In one embodiment, the analyzing the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determining whether to adjust the environmental conditions of the warehouse based on the environmental prediction data, further includes:

[0022] Using the standardized current environmental monitoring data as input to the environmental prediction model to output environmental prediction data for a first future time period corresponding to the current environmental monitoring data;

[0023] When any of the environmental prediction data is not within a preset range, the environmental adjustment instruction including the environmental adaptive decision is received to adjust the environment of the warehouse;

[0024] The environmental prediction data includes the temperature, humidity and light intensity within a first future time period of the current environmental monitoring data.

[0025] In one embodiment, when the environmental conditions of the warehouse need to be adjusted, receiving an environmental adjustment instruction including an environmental adaptive decision, and controlling the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision in response to the environmental adjustment instruction, includes:

[0026] When the temperature, humidity, or light intensity of the current environmental monitoring data is not within a preset range within a first future time period, obtaining a corresponding environmental adjustment instruction based on the environmental adaptive decision, and executing the environmental adaptive decision in response to the environmental adjustment instruction;

[0027] The air conditioning device or humidity control device or light intensity control device is controlled according to the environmental adaptive decision to adjust the environmental conditions in the warehouse to meet the storage conditions of the items stored in the warehouse.

[0028] In one embodiment, the method further comprises:

[0029] Obtaining the current inventory status and order requirements in the warehouse, and planning the location of stored items based on the current inventory status and order requirements combined with the warehouse's inventory layout influence coefficient;

[0030] Based on the location planning results of the stored items, the speed parameters of the storage item locations changing over time are obtained, and the location information of the storage items after changing over time is calculated according to the current location of the stored items, order requirements, inventory layout influence coefficients and speed parameters.

[0031] The present invention also provides an intelligent warehouse management system based on an adaptive environment, the system comprising:

[0032] A data preprocessing module is used to obtain environmental monitoring data of the warehouse and perform standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same magnitude;

[0033] An environmental data prediction module is configured to analyze the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determine whether to adjust the environmental conditions of the warehouse based on the environmental prediction data;

[0034] An environmental adaptive adjustment module is configured to receive an environmental adjustment instruction including an environmental adaptive decision when the environmental conditions of the warehouse need to be adjusted, and control the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision in response to the environmental adjustment instruction;

[0035] Among them, the environmental monitoring data includes the temperature, humidity and light intensity of the warehouse, the environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse, and the environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

[0036] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements any of the above-described intelligent warehouse management methods based on an adaptive environment.

[0037] The present invention also provides a computer storage medium storing a computer program, wherein when the computer program is executed by a processor, the computer storage medium implements any of the above-described intelligent warehouse management methods based on an adaptive environment.

[0038] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described intelligent warehouse management methods based on an adaptive environment.

[0039] The above-mentioned adaptive environment-based intelligent warehouse management method and system obtains warehouse environmental monitoring data and standardizes it to remove noise and errors from the data and convert it to a uniform level. Subsequently, the standardized environmental monitoring data is analyzed using a machine learning algorithm, outputting environmental prediction data based on the environmental monitoring data. Based on the prediction data, a decision is made whether to adjust the warehouse's environmental conditions. When the warehouse's environmental conditions need to be adjusted, an environmental adjustment instruction containing an adaptive environmental decision is received. In response to the environmental adjustment instruction, the environmental control equipment within the warehouse is controlled to adjust the warehouse's environmental conditions according to the adaptive environmental decision, so that the warehouse's environmental conditions meet the storage requirements of the stored items and ensure the quality and safety of the stored items. This method optimizes warehouse storage environment management by integrating warehouse environmental monitoring, data processing, adaptive environmental decision-making, and environmental adjustment. It also incorporates a machine learning algorithm to analyze the processed monitoring data. This method has a high level of intelligence and avoids complex system setup and equipment requirements, reducing operation and maintenance costs and labor costs to a certain extent. Furthermore, this method can monitor the warehouse environment in real time and predict and formulate adaptive environmental decisions to adjust the warehouse's environmental conditions in real time, further improving the warehouse's environmental adaptability and the real-time nature of environmental condition adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. 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 paying any creative work.

[0041] Figure 1 This is a flow chart of the intelligent warehouse management method based on the adaptive environment provided by the present invention;

[0042] Figure 2 A schematic diagram of an intelligent warehouse environment management process of an intelligent warehouse management method based on an adaptive environment in a specific embodiment provided by the present invention;

[0043] Figure 3 The second flowchart of the intelligent warehouse management method based on the adaptive environment provided by the present invention;

[0044] Figure 4 The third flow chart of the intelligent warehouse management method based on the adaptive environment provided by the present invention;

[0045] Figure 5 This is a fourth flow chart of the intelligent warehouse management method based on the adaptive environment provided by the present invention;

[0046] Figure 6 This is a fifth flow chart of the intelligent warehouse management method based on the adaptive environment provided by the present invention;

[0047] Figure 7 Flowchart 6 of the intelligent warehouse management method based on adaptive environment provided by the present invention;

[0048] Figure 8 Flowchart 7 of the intelligent warehouse management method based on adaptive environment provided by the present invention;

[0049] Figure 9 A schematic diagram of the structure of the intelligent warehouse management system based on the adaptive environment provided by the present invention;

[0050] Figure 10 This is a diagram of the internal structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] The following combination Figures 1 to 10 The present invention describes an intelligent warehouse management method and system based on an adaptive environment.

[0053] like Figure 1 As shown, in one embodiment, an intelligent warehouse management method based on an adaptive environment includes the following steps:

[0054] Step S110 , obtaining environmental monitoring data of the warehouse, and performing standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same magnitude.

[0055] Among them, environmental monitoring data includes but is not limited to warehouse temperature, humidity and light intensity.

[0056] Specifically, the intelligent warehousing system obtains the environmental monitoring data of the warehouse, namely the temperature, humidity and light intensity measured by the temperature sensors, humidity sensors and light sensors in the warehouse, and standardizes the obtained environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data to the same level.

[0057] Combine Figure 2 As shown, in a specific embodiment, the present invention provides an intelligent warehousing management method based on an adaptive environment. In an intelligent warehousing system, environmental monitoring is the key to ensuring that storage conditions are most suitable for cargo storage needs. The core of the method is to use a high-precision sensor network to collect warehouse environmental data in real time, such as temperature, humidity, light intensity, etc., and use these data to adjust the warehouse's operating environment to ensure cargo quality and safety.

[0058] In this embodiment, environmental monitoring is mainly achieved by deploying a multi-point sensor network. These sensors include but are not limited to temperature sensors, humidity sensors, light sensors, etc. The data collected by each sensor will be transmitted to the central processing system for analysis and decision support.

[0059] The expression for the integration and processing of environmental monitoring data is:

[0060] E_t=a1*S1_t+a2*S2_t+...+an*Sn_t.

[0061] Where E_t represents the comprehensive environmental status index at time t, S1_t, S2_t, ..., Sn_t represent the readings of different sensors at time t, and a1, a2, ..., an are weight coefficients adjusted according to the sensor type and location. The setting of these weight coefficients depends on the contribution and sensitivity of each sensor to the environmental monitoring quality.

[0062] It's important to note that E_t is a weighted sum representing the integrated state of all environmental factors at a specific point in time. This allows system administrators to adjust weights based on the storage requirements of different goods, giving more attention to specific environmental factors. For example, for pharmaceuticals requiring low-temperature storage, the temperature sensor's weight would be set higher to ensure that temperature changes can be detected and adjusted immediately.

[0063] In practical applications, the environmental monitoring process described above allows warehouse management systems to respond to environmental changes in real time, automatically adjusting the warehouse's air conditioners, humidifiers, dehumidifiers, and other environmental control equipment to maintain the most suitable storage environment. This not only improves the storage quality and safety of goods, but also optimizes warehouse operating costs by reducing goods loss and extending their storage life. Furthermore, integrated environmental monitoring data can be used to generate environmental status reports, providing decision support for warehouse management and providing a data basis for complying with regulatory requirements. For example, in the food and pharmaceutical industries, strict environmental control is a quality control requirement. Using this type of intelligent warehousing not only enables basic storage functions but also improves the efficiency and automation of warehouse management, greatly enhancing the efficiency and reliability of logistics and supply chain management.

[0064] In this embodiment, data processing and analysis are key processes for ensuring efficient management and optimized decision-making. This process involves cleaning, standardizing, and preprocessing the raw data collected from various sensors to facilitate further analysis and application. First, after being collected from environmental monitoring sensors, raw data typically contains various noise and errors, which may be caused by sensor errors, external interference, or data transmission errors. Therefore, a series of data processing steps must be performed to improve the data's quality and usability.

[0065] The expression for data standardization is:

[0066] D_t'=(D_t-μ) / σ.

[0067] Where D_t represents the raw data vector collected at time t, including data in multiple dimensions such as temperature, humidity, and light intensity. μ represents the mean of the raw data vector, which is calculated as the sum of all data points divided by the number of data points. σ represents the standard deviation of the data vector, which is used to measure the degree of dispersion of the data points. D_t' represents the standardized data vector. After this standardization process, the environmental monitoring data will have zero mean and unit variance, which is helpful for subsequent data analysis and machine learning model training.

[0068] It's important to note that normalization is a common technique in data preprocessing. Its primary purpose is to eliminate the impact of different magnitudes and dimensions on data analysis, making the data comparable for comparisons and calculations. For example, in environmental monitoring, temperature might be measured in degrees Celsius, while humidity is expressed as a percentage. Directly comparing these data can lead to misunderstandings and errors. Normalization converts the data to the same magnitude, facilitating effective comparison and analysis.

[0069] Furthermore, data standardization is particularly important in intelligent warehousing systems because it supports efficient data analysis. For example, cleaned and standardized data can be used directly to monitor changes in the warehouse environment and identify trends or anomalies. It also supports the application of machine learning algorithms, as almost all machine learning algorithms require preprocessed data to improve model performance and accuracy. Standardized data can reduce the computational complexity of machine learning algorithm training and accelerate convergence. Finally, there is decision support. Standardized data can provide warehouse management systems with consistent and accurate environmental monitoring information, supporting warehouse management decisions such as environmental adjustments and inventory allocation.

[0070] Step S120: Analyze the standardized environmental monitoring data through a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determine whether to adjust the environmental conditions of the warehouse based on the environmental prediction data.

[0071] Specifically, the intelligent warehousing system uses machine learning algorithms to analyze and predict standardized environmental monitoring data to output environmental prediction data for a period of time in the future based on the environmental monitoring data, and determines whether the environmental conditions of the warehouse need to be adjusted based on the environmental prediction data.

[0072] Combine Figure 2 As shown in the specific embodiment, the present invention provides an adaptive environment-based intelligent warehouse management method. In the intelligent warehouse system, the adaptive environment decision-making process uses machine learning algorithms to analyze warehouse environmental monitoring data to predict future environmental change trends and, based on this, determine whether to adjust the warehouse's environmental conditions. This process aims to ensure that the storage environment within the warehouse is always suitable for the stored goods, especially those sensitive to temperature and humidity.

[0073] In this embodiment, the environmental adaptive decision-making process relies on advanced data analysis and pattern recognition technology, and is mainly implemented through the following steps:

[0074] Data analytics: Use machine learning algorithms to process standardized environmental data to identify trends and patterns in the data.

[0075] Predictive modeling: Build a predictive model based on historical data to estimate changes in environmental parameters over a period of time in the future.

[0076] Decision making: Based on the prediction results, decide whether the environmental control system needs to be adjusted to adapt to the predicted environmental changes.

[0077] Among them, the expression of the decision of environmental adjustment is:

[0078] A_t=k*log(1+exp(R_t*D_t')).

[0079] Where A_t is the intensity of the adjustment action at time t, indicating the degree of adjustment required; R_t is the output of the risk assessment model at time t, whose value indicates the predicted risk or the urgency of the response; D_t' is the standardized environmental data, representing the data input to the risk assessment model after preprocessing; and k is the adjustment coefficient, which is used to scale the intensity of the adjustment action to ensure the appropriateness of the response.

[0080] In practical applications, this decision-making process enables real-time monitoring of environmental conditions within the warehouse, such as elevated temperatures or humidity levels exceeding preset ranges. By analyzing real-time data with risk assessment models, the intelligent warehousing system can predict whether changing environmental conditions will impact stored goods. If the model predicts an adverse environmental change, it calculates the necessary adjustments and automatically adjusts relevant environmental control devices, such as heating, cooling, or humidification, to maintain optimal storage conditions. For example, if the predictive model predicts that the warehouse temperature will continue to rise within the next few hours based on recent temperature trends, the system might proactively lower the air conditioning setpoint to prevent damage to temperature-sensitive goods. This prediction and response mechanism enables warehouses to effectively manage environmental risks, thereby improving warehouse management efficiency and ensuring product quality. By leveraging the aforementioned data analysis and machine learning technologies, environmentally adaptive decision-making enables intelligent warehousing systems to adapt to complex and changing environmental conditions while maintaining efficient operations. This not only enhances the intelligence of the warehousing system but also ensures the quality and safety of goods.

[0081] Step S130: When the environmental conditions of the warehouse need to be adjusted, an environmental adjustment instruction including an environmental adaptive decision is received, and in response to the environmental adjustment instruction, the environmental control equipment in the warehouse is controlled to adjust the environmental conditions according to the environmental adaptive decision.

[0082] The environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse, and the environmental control equipment includes but is not limited to air conditioning equipment, humidity control equipment, and light intensity control equipment.

[0083] Specifically, when the intelligent warehousing system needs to adjust the environmental conditions of the warehouse, it will receive an environmental adjustment instruction containing an environmental adaptive decision, and in response to the environmental adjustment instruction, control the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision, so that the warehouse environment is in an environmental condition suitable for storing goods.

[0084] Combine Figure 2As shown in the specific embodiment, the present invention provides an adaptive environment-based intelligent warehouse management method. In an intelligent warehouse system, environmental adjustments are a key step in ensuring that storage conditions consistently meet cargo requirements. Adjustment instructions derived from adaptive environmental decisions automatically adjust warehouse environmental control equipment, such as air conditioners and humidifiers, to achieve an ideal storage environment. This process not only responds quickly but also adjusts the environment based on real-time data to ensure the quality and safety of stored goods.

[0085] The expression for execution environment adjustment is:

[0086] adjust(E_t)=E_t+η*A_t。

[0087] Where adjust(E_t) is the adjusted environmental state, which is a real-time update of the current environmental state. E_t represents the current environmental state, including but not limited to parameters such as temperature and humidity. η is the adjustment efficiency parameter, which is a coefficient reflecting the system response speed and adjustment strength. A_t is the adjustment action strength provided by the environmental adaptive decision-making process, which is determined by the magnitude of the environmental change required for storage.

[0088] In practical applications, this environmental adjustment process allows warehouse management systems to quickly respond to environmental changes. For example, if a predictive model indicates that the temperature will rise significantly in the coming hours, and the current warehouse temperature is approaching the upper temperature limit for storing sensitive pharmaceuticals, the system will automatically adjust the air conditioning temperature setting downward. At this point, A_t will be a negative value, indicating that a cooling effect is needed, and η determines the rate and magnitude of the temperature adjustment. The complexity of performing environmental adjustments lies in the need to precisely control and coordinate the operation of various devices to achieve rapid and accurate environmental adjustments. Furthermore, each type of cargo may have different storage requirements, so the system must be able to process diverse data from different sensors and respond optimally. Therefore, the setting of the adjustment efficiency parameter η is particularly critical, as it requires fine-tuning based on the actual responsiveness of the equipment and the expected rate of environmental change. By automatically performing environmental adjustments, intelligent warehousing systems can ensure that the storage environment within the warehouse is always maintained at an optimal state despite changing external environmental conditions. This process improves storage efficiency and reduces cargo loss. Automated environmental adjustments also reduce reliance on manual operations, further reducing operating costs and error rates.

[0089] The above-mentioned intelligent warehouse management method based on adaptive environment obtains warehouse environmental monitoring data and standardizes it to remove noise and errors from the environmental monitoring data and convert it to the same magnitude. Subsequently, the standardized environmental monitoring data is analyzed using a machine learning algorithm, outputting environmental prediction data based on the environmental monitoring data. Based on the environmental prediction data, a decision is made whether to adjust the warehouse's environmental conditions. When the warehouse's environmental conditions need to be adjusted, an environmental adjustment instruction containing an adaptive environmental decision is received. In response to the environmental adjustment instruction, the environmental control equipment within the warehouse is controlled to adjust the warehouse's environmental conditions according to the adaptive environmental decision, so that the warehouse's environmental conditions meet the storage requirements of the stored items and ensure the quality and safety of the stored items. This method optimizes warehouse storage environment management by integrating warehouse environmental monitoring, data processing, adaptive environmental decision-making, and environmental adjustment. It also incorporates machine learning algorithms to analyze the processed monitoring data. This method has a high level of intelligence and avoids complex system setup and equipment requirements, reducing operation and maintenance costs and labor costs to a certain extent. Furthermore, this method can monitor the warehouse environment in real time and predict and formulate adaptive environmental decisions to adjust the warehouse's environmental conditions in real time, further improving the warehouse's environmental adaptability and the real-time nature of environmental condition adjustment.

[0090] like Figure 3 As shown, in one embodiment, the intelligent warehouse management method based on adaptive environment provided by the present invention obtains environmental monitoring data of the warehouse and standardizes the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same level, specifically comprising the following steps:

[0091] Step S112 , obtaining temperature data, humidity data, and light intensity at multiple locations in the warehouse at a first moment through temperature sensors, humidity sensors, and light sensors installed at multiple locations in the warehouse.

[0092] Step S114 , based on the environmental monitoring data measured by different sensors at the first moment and the weight coefficient, the environmental status index of the warehouse at the first moment is calculated by weighted summation, where the weight coefficient is determined according to the sensor type and the location of the sensor.

[0093] The first moment is any moment t.

[0094] like Figure 4 As shown, in one embodiment, the intelligent warehouse management method based on adaptive environment provided by the present invention obtains environmental monitoring data of the warehouse and performs standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same level, and specifically further includes the following steps:

[0095] Step S116 , based on the environmental monitoring data of the warehouse at the first moment, obtaining the original data vector of the environmental monitoring data at the first moment, and calculating the mean and standard deviation of the original data vector.

[0096] Step S118 , calculating a standardized data vector of the original data vector at the first moment according to the mean and standard deviation of the original data vector at the first moment, so as to perform standardization processing on the environmental monitoring data.

[0097] The original data vector includes multi-dimensional data of temperature, humidity, and light intensity in the warehouse, and the standardized data vector is the ratio of the difference between the original data vector and the mean to the standard deviation.

[0098] like Figure 5 As shown, in one embodiment, the present invention provides an intelligent warehouse management method based on an adaptive environment, which analyzes standardized environmental monitoring data through a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determines whether to adjust the environmental conditions of the warehouse based on the environmental prediction data. The method specifically includes the following steps:

[0099] Step S122: Call a machine learning algorithm to process the standardized environmental monitoring data to identify the change trend of each data in the environmental monitoring data.

[0100] Step S124 , training a machine learning algorithm based on the changing trend of the environmental monitoring data in combination with the historical environmental monitoring data of the warehouse, so as to construct an environmental prediction model for predicting changes in warehouse environmental conditions within a first future time period.

[0101] The first time period is any future period that can be predicted by the environmental prediction model.

[0102] like Figure 6 As shown, in one embodiment, the intelligent warehouse management method based on adaptive environment provided by the present invention analyzes the standardized environmental monitoring data through a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determines whether to adjust the environmental conditions of the warehouse based on the environmental prediction data. Specifically, the method further includes the following steps:

[0103] Step S126 , using the standardized current environmental monitoring data as input to the environmental prediction model to output environmental prediction data for a first future time period corresponding to the current environmental monitoring data.

[0104] Step S128: When any of the environmental prediction data is not within the preset range, an environmental adjustment instruction including an environmental adaptive decision is received to adjust the environment of the warehouse.

[0105] Among them, the environmental prediction data includes the temperature, humidity and light intensity in the first time period in the future of the current environmental monitoring data.

[0106] like Figure 7 As shown, in one embodiment, the intelligent warehouse management method based on adaptive environment provided by the present invention receives an environment adjustment instruction including an environment adaptive decision when the warehouse environment conditions need to be adjusted, and controls the environment control equipment in the warehouse to adjust the environment conditions according to the environment adaptive decision in response to the environment adjustment instruction, specifically including the following steps:

[0107] Step S132: When the temperature, humidity or light intensity of the current environmental monitoring data in the first future time period is not within the preset range, a corresponding environmental adjustment instruction is obtained based on the environmental adaptive decision, and the environmental adaptive decision is executed in response to the environmental adjustment instruction.

[0108] Step S134: Control the air conditioning device or the humidity control device or the light intensity control device according to the environmental adaptive decision to adjust the environmental conditions in the warehouse to meet the storage conditions of the items stored in the warehouse.

[0109] like Figure 8 As shown, in one embodiment, the intelligent warehouse management method based on the adaptive environment provided by the present invention further includes the following steps:

[0110] Step S810 , obtaining the current inventory status and order requirements in the warehouse, and planning the location of the stored items based on the current inventory status and order requirements combined with the inventory layout influence coefficient of the warehouse.

[0111] Step S820, based on the location planning result of the stored items, obtain the speed parameter of the storage item location changing over time, and calculate the location information of the stored items after changing over time according to the current location of the stored items, order requirements, inventory layout influence coefficient and speed parameter.

[0112] Combine Figure 2 As shown, in a specific embodiment, the present invention provides an intelligent warehousing management method based on an adaptive environment. In an intelligent warehousing system, dynamic logistics optimization automatically optimizes the storage location and logistics path of goods through advanced algorithms to respond to real-time changes in inventory and order demand. The purpose of this process is to reduce the time and cost of goods handling and improve the operational efficiency of the warehouse.

[0113] The core of dynamic logistics optimization lies in predicting and adjusting the location of goods in real time. The process expression is:

[0114] P_t+1=P_t+v*sum(O_t*L_t).

[0115] In the formula, P_t and P_t+1 represent the cargo locations at time t and t+1, respectively, and are used to characterize the change of cargo location over time. O_t represents the predicted order demand, reflecting the demand for specific goods at a specific time point. L_t is the inventory layout impact coefficient, which is a coefficient that considers the impact of the specific storage location of goods on logistics efficiency. v is the speed parameter, which is used to adjust the rate of location change to ensure the efficiency and timeliness of cargo movement.

[0116] It's important to note that dynamic logistics optimization uses advanced algorithms to analyze and predict order demand (O_t). Combined with the current inventory status (P_t), this algorithm then calculates the optimal route and location for goods movement. The inventory layout impact factor (L_t) accounts for the impact of different inventory areas on operational efficiency, such as areas that may be closer to loading and unloading areas or shelves that are more easily accessible. The speed parameter (v) adjusts the rate of goods movement to accommodate varying operational needs and time windows.

[0117] In practice, if demand for a particular product is predicted to increase in the coming period, the system can automatically move these products to a more accessible location. This optimization not only reduces the distance workers must travel within the warehouse but also reduces the time wasted searching for products, significantly improving warehouse efficiency and responsiveness. Furthermore, dynamic logistics optimization can address situations such as holiday sales peaks and promotional events. By flexibly adjusting the location of goods to cope with sudden changes in order demand, it ensures that the warehouse can quickly respond to market demand and reduce potential inventory backlogs. By implementing dynamic logistics optimization, intelligent warehousing systems not only improve logistics efficiency, but also significantly reduce operating costs and optimize resource allocation.

[0118] The following describes the intelligent warehouse management system based on the adaptive environment provided by the present invention. The intelligent warehouse management system based on the adaptive environment described below and the intelligent warehouse management method based on the adaptive environment described above can refer to each other.

[0119] like Figure 9 As shown, in one embodiment, an intelligent warehouse management system based on an adaptive environment includes a data preprocessing module 910 , an environmental data prediction module 920 and an environmental adaptive adjustment module 930 .

[0120] The data preprocessing module 910 is used to obtain the environmental monitoring data of the warehouse and perform standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same magnitude.

[0121] The environmental data prediction module 920 is used to analyze the standardized environmental monitoring data through a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and to determine whether to adjust the environmental conditions of the warehouse based on the environmental prediction data.

[0122] The environment adaptive adjustment module 930 is used to receive an environment adjustment instruction containing an environment adaptive decision when the environment conditions of the warehouse need to be adjusted, and control the environment control equipment in the warehouse to adjust the environment conditions according to the environment adaptive decision in response to the environment adjustment instruction.

[0123] Among them, environmental monitoring data includes the temperature, humidity and light intensity of the warehouse. The environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse. The environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

[0124] In this embodiment, the data preprocessing module 910 of the intelligent warehouse management system based on the adaptive environment provided by the present invention is specifically used to:

[0125] Temperature data, humidity data and light intensity at multiple locations in the warehouse at a first moment are obtained through temperature sensors, humidity sensors and light sensors set at multiple different locations in the warehouse.

[0126] Based on the environmental monitoring data measured by different sensors at the first moment and the weight coefficients, the environmental status index of the warehouse at the first moment is calculated by weighted summation. The weight coefficients are determined according to the sensor type and the location of the sensor.

[0127] In this embodiment, the data preprocessing module 910 of the intelligent warehouse management system based on the adaptive environment provided by the present invention is further configured to:

[0128] Based on the environmental monitoring data of the warehouse at the first moment, the original data vector of the environmental monitoring data at the first moment is obtained, and the mean and standard deviation of the original data vector are calculated.

[0129] According to the mean and standard deviation of the original data vector at the first moment, a standardized data vector of the original data vector at the first moment is calculated to perform standardization processing on the environmental monitoring data.

[0130] The original data vector includes multi-dimensional data of temperature, humidity, and light intensity in the warehouse, and the standardized data vector is the ratio of the difference between the original data vector and the mean to the standard deviation.

[0131] In this embodiment, the intelligent warehouse management system based on adaptive environment provided by the present invention, the environment data prediction module 920 is specifically used to:

[0132] The machine learning algorithm is called to process the standardized environmental monitoring data to identify the change trend of each data in the environmental monitoring data.

[0133] The machine learning algorithm is trained based on the changing trend of the environmental monitoring data and the historical environmental monitoring data of the warehouse to build an environmental prediction model for predicting changes in warehouse environmental conditions within the first time period in the future.

[0134] In this embodiment, the intelligent warehouse management system based on adaptive environment provided by the present invention, the environment data prediction module 920 is further used to:

[0135] The standardized current environmental monitoring data is used as the input of the environmental prediction model to output environmental prediction data for a first future time period corresponding to the current environmental monitoring data.

[0136] When any of the environmental prediction data is not within the preset range, an environmental adjustment instruction including an environmental adaptive decision is received to adjust the environment of the warehouse.

[0137] Among them, the environmental prediction data includes the temperature, humidity and light intensity in the first time period in the future of the current environmental monitoring data.

[0138] In this embodiment, the intelligent warehouse management system based on the adaptive environment provided by the present invention, the environment adaptive adjustment module 930 is specifically used to:

[0139] When the temperature, humidity or light intensity of the current environmental monitoring data is not within the preset range in the first time period in the future, the corresponding environmental adjustment instruction is obtained based on the environmental adaptive decision, and the environmental adaptive decision is executed in response to the environmental adjustment instruction.

[0140] The air conditioning equipment, humidity control equipment or light intensity control equipment is controlled according to the environmental adaptive decision to adjust the environmental conditions in the warehouse to meet the storage conditions of the items stored in the warehouse.

[0141] In this embodiment, the intelligent warehouse management system based on the adaptive environment provided by the present invention further includes a storage item management module for:

[0142] Obtain the current inventory status and order demand in the warehouse, and plan the location of stored items based on the current inventory status and order demand combined with the warehouse's inventory layout influence coefficient.

[0143] Based on the location planning results of the stored items, the speed parameters of the storage item locations changing over time are obtained, and the location information of the stored items after changing over time is calculated based on the current location of the stored items, order requirements, inventory layout influence coefficients, and speed parameters.

[0144] Figure 10 The following is a schematic diagram of the physical structure of an electronic device. The electronic device may be a smart terminal, and its internal structure diagram may be as follows: Figure 10 As shown. The electronic device includes a processor, an internal memory, and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an intelligent warehouse management method based on an adaptive environment is implemented, which method includes:

[0145] Obtain environmental monitoring data from the warehouse and perform standardization on the data to remove noise and errors and convert the data to the same level;

[0146] Analyze the standardized environmental monitoring data through machine learning algorithms to output environmental prediction data based on the environmental monitoring data, and determine whether to adjust the warehouse's environmental conditions based on the environmental prediction data;

[0147] When the environmental conditions of the warehouse need to be adjusted, an environmental adjustment instruction including an environmental adaptive decision is received, and in response to the environmental adjustment instruction, the environmental control equipment in the warehouse is controlled to adjust the environmental conditions according to the environmental adaptive decision;

[0148] Among them, environmental monitoring data includes the temperature, humidity and light intensity of the warehouse. The environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse. The environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

[0149] Those skilled in the art will understand that Figure 10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the electronic device to which the solution of the present invention is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0150] In another aspect, the present invention further provides a computer storage medium storing a computer program, wherein when the computer program is executed by a processor, an intelligent warehouse management method based on an adaptive environment is implemented, the method comprising:

[0151] Obtain environmental monitoring data from the warehouse and perform standardization on the data to remove noise and errors and convert the data to the same level;

[0152] Analyze the standardized environmental monitoring data through machine learning algorithms to output environmental prediction data based on the environmental monitoring data, and determine whether to adjust the warehouse's environmental conditions based on the environmental prediction data;

[0153] When the environmental conditions of the warehouse need to be adjusted, an environmental adjustment instruction including an environmental adaptive decision is received, and in response to the environmental adjustment instruction, the environmental control equipment in the warehouse is controlled to adjust the environmental conditions according to the environmental adaptive decision;

[0154] Among them, environmental monitoring data includes the temperature, humidity and light intensity of the warehouse. The environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse. The environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

[0155] In another aspect, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, implements an intelligent warehouse management method based on an adaptive environment, the method comprising:

[0156] Obtain environmental monitoring data from the warehouse and perform standardization on the data to remove noise and errors and convert the data to the same level;

[0157] Analyze the standardized environmental monitoring data through machine learning algorithms to output environmental prediction data based on the environmental monitoring data, and determine whether to adjust the warehouse's environmental conditions based on the environmental prediction data;

[0158] When the environmental conditions of the warehouse need to be adjusted, an environmental adjustment instruction including an environmental adaptive decision is received, and in response to the environmental adjustment instruction, the environmental control equipment in the warehouse is controlled to adjust the environmental conditions according to the environmental adaptive decision;

[0159] Among them, environmental monitoring data includes the temperature, humidity and light intensity of the warehouse. The environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse. The environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

[0160] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0161] By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0162] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0163] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An intelligent warehouse management method based on adaptive environment, characterized in that: The method comprises: Acquire environmental monitoring data of the warehouse, and perform standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data to the same magnitude; Analyzing the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determining whether to adjust the environmental conditions of the warehouse based on the environmental prediction data; When the environmental conditions of the warehouse need to be adjusted, receiving an environmental adjustment instruction including an environmental adaptive decision, and controlling the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision in response to the environmental adjustment instruction; Among them, the environmental monitoring data includes the temperature, humidity and light intensity of the warehouse, the environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse, and the environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

2. The intelligent warehouse management method based on adaptive environment according to claim 1 is characterized in that: The step of obtaining environmental monitoring data of the warehouse and performing standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same magnitude includes: The temperature sensors, humidity sensors and light sensors installed at different locations in the warehouse are used to obtain temperature data, humidity data and light intensity at multiple locations in the warehouse at a first moment; Based on the environmental monitoring data and weight coefficients measured by different sensors at the first moment, the environmental status index of the warehouse at the first moment is calculated by weighted summation, and the weight coefficient is determined according to the sensor type and the location of the sensor.

3. The intelligent warehouse management method based on adaptive environment according to claim 2 is characterized in that: The step of obtaining environmental monitoring data of the warehouse and performing standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same magnitude further includes: Based on the environmental monitoring data of the warehouse at the first moment, obtaining a raw data vector of the environmental monitoring data at the first moment, and calculating a mean and a standard deviation of the raw data vector; Calculating a standardized data vector of the original data vector at the first moment according to the mean and standard deviation of the original data vector at the first moment, so as to perform standardization processing on the environmental monitoring data; The original data vector includes multi-dimensional data of temperature, humidity and light intensity in the warehouse, and the standardized data vector is the ratio of the difference between the original data vector and the mean to the standard deviation.

4. The intelligent warehouse management method based on adaptive environment according to claim 1 is characterized in that: The step of analyzing the standardized environmental monitoring data by a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determining whether to adjust the environmental conditions of the warehouse based on the environmental prediction data, includes: Calling the machine learning algorithm to process the standardized environmental monitoring data to identify the change trend of each data in the environmental monitoring data; The machine learning algorithm is trained based on the changing trend of the environmental monitoring data in combination with the historical environmental monitoring data of the warehouse to construct an environmental prediction model for predicting changes in warehouse environmental conditions within a first future time period.

5. The intelligent warehouse management method based on adaptive environment according to claim 4 is characterized in that: The method further includes analyzing the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determining whether to adjust the environmental conditions of the warehouse based on the environmental prediction data. Using the standardized current environmental monitoring data as input to the environmental prediction model to output environmental prediction data for a first future time period corresponding to the current environmental monitoring data; When any of the environmental prediction data is not within a preset range, the environmental adjustment instruction including the environmental adaptive decision is received to adjust the environment of the warehouse; The environmental prediction data includes the temperature, humidity and light intensity within a first future time period of the current environmental monitoring data.

6. The intelligent warehouse management method based on adaptive environment according to claim 5 is characterized in that: When the environmental conditions of the warehouse need to be adjusted, receiving an environmental adjustment instruction including an environmental adaptive decision, and controlling the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision in response to the environmental adjustment instruction, includes: When the temperature, humidity, or light intensity of the current environmental monitoring data is not within a preset range within a first future time period, obtaining a corresponding environmental adjustment instruction based on the environmental adaptive decision, and executing the environmental adaptive decision in response to the environmental adjustment instruction; The air conditioning device or humidity control device or light intensity control device is controlled according to the environmental adaptive decision to adjust the environmental conditions in the warehouse to meet the storage conditions of the items stored in the warehouse.

7. The intelligent warehouse management method based on adaptive environment according to any one of claims 1 to 6, characterized in that: The method further comprises: Obtaining the current inventory status and order requirements in the warehouse, and planning the location of stored items based on the current inventory status and order requirements combined with the warehouse's inventory layout influence coefficient; Based on the location planning results of the stored items, the speed parameters of the storage item locations changing over time are obtained, and the location information of the storage items after changing over time is calculated according to the current location of the stored items, order requirements, inventory layout influence coefficients and speed parameters.

8. An intelligent warehouse management system based on adaptive environment, characterized by: The system comprises: A data preprocessing module is used to obtain environmental monitoring data of the warehouse and perform standardization processing on the environmental monitoring data to remove noise and errors in the environmental monitoring data and convert the environmental monitoring data into the same magnitude; An environmental data prediction module is configured to analyze the standardized environmental monitoring data using a machine learning algorithm to output environmental prediction data based on the environmental monitoring data, and determine whether to adjust the environmental conditions of the warehouse based on the environmental prediction data; An environmental adaptive adjustment module is configured to receive an environmental adjustment instruction including an environmental adaptive decision when the environmental conditions of the warehouse need to be adjusted, and control the environmental control equipment in the warehouse to adjust the environmental conditions according to the environmental adaptive decision in response to the environmental adjustment instruction; Among them, the environmental monitoring data includes the temperature, humidity and light intensity of the warehouse, the environmental adaptive decision is preset based on the storage conditions of the items stored in the warehouse, and the environmental control equipment includes air conditioning equipment, humidity control equipment and light intensity control equipment.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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