Textile intelligent warehouse management system and method based on cloud computing

Abstract

The invention discloses a textile intelligent warehouse management system and method based on cloud computing, and relates to the technical field of inventory management, and the textile intelligent warehouse management system based on cloud computing comprises a warehouse monitoring area division module, a warehouse textile storage monitoring module, a monitoring data analysis module, and a textile warehouse management module. According to the invention, the fine management layout of the warehouse space can be realized, the storage state and quality level of each area can be clearly quantified, the abnormal warehouse sub-areas can be accurately screened out according to the monitoring characterization value and timely risk early warning management can be carried out, and the in-service sensing devices of each warehouse sub-area can be intelligently managed and adjusted, so that the management efficiency is improved. Accuracy and timeliness of data acquisition are guaranteed, and scientificity and intelligent level of textile warehouse management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory management, and in particular to a textile intelligent warehouse management system and method based on cloud computing. Background Art

[0002] With the continuous development of the textile industry and the growing market demand, textile warehouse management is facing more and more challenges. Traditional warehouse management systems can usually only record basic inventory quantity information, which can no longer meet the current high-level management needs for textiles and can easily affect the efficiency of the entire warehouse operation.

[0003] Prior art, such as the invention patent with publication number: CN111784231A, is a textile warehouse inventory information management system, which discloses a textile warehouse inventory information management system, including a central server, the central server is connected to a number of regional servers, the several regional servers correspond to different regions, the regional servers are connected to the warehouse center management system of the corresponding region, the warehouse center management system includes basic management information, warehousing management information, outbound management information and inventory management information, the central server manages and integrates regional servers in different regions, the warehouse management system in the corresponding region is connected to the central server through the regional server, the warehouse management system includes basic management information, warehousing management information, outbound management information and inventory management information, which are respectively used for personnel information management, goods in and out of the warehouse information management and inventory management, in addition, remote user terminals access the central server through a communication network, and local users can access the servers in the corresponding area, reducing server load and improving information management effects.

[0004] From the above scheme, it can be seen that the current warehouse management usually only monitors and manages the items in the warehouse. When the items are textiles, due to the special requirements for storage conditions, the demand for warehouse management equipment is also high. At this time, if only the textiles are monitored independently, the overall operation and maintenance management quality and safety of the warehouse cannot be fully guaranteed. Therefore, the current warehouse management system is difficult to meet the more sophisticated textile storage needs, and the overall intelligence needs to be improved. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a textile intelligent warehouse management system and method based on cloud computing, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: On the one hand, the present invention provides a textile intelligent warehouse management system based on cloud computing, including: a warehouse monitoring area division module, which is used to count a number of integrated sensor devices installed in the textile warehouse, recorded as each in-service sensor device, and count the area of ​​the sensor area belonging to each in-service sensor device to divide the textile warehouse into each warehouse sub-area.

[0007] The warehouse textile storage monitoring module is used to count the stored textiles in each warehouse sub-area for monitoring and obtain the storage textile monitoring data of each warehouse sub-area.

[0008] The monitoring data analysis module is used to store textile monitoring data of each warehouse sub-area and input it into the cloud computing center to analyze and obtain the textile monitoring characterization value of each warehouse sub-area.

[0009] The textile storage management module is used to screen abnormal warehouse sub-areas for risk warning management based on the textile monitoring characterization values ​​of each warehouse sub-area, and to intelligently manage and adjust the in-service sensor devices in each warehouse sub-area.

[0010] As an optimal technical solution, the stored textiles in each warehouse sub-area are monitored by statistics to obtain the stored textile monitoring data of each warehouse sub-area. The specific process is as follows: within a preset monitoring period, the stored textiles in each warehouse sub-area are monitored in real time to obtain the stored textile monitoring data of each warehouse sub-area.

[0011] The storage textile monitoring data of each warehouse sub-area includes first textile data and second warehouse environment data.

[0012] The first textile data includes the average daily storage number of textiles in each warehouse sub-area, the average daily storage density of textiles, the cumulative number of types of stored textiles, and the average daily use frequency of textiles.

[0013] The second warehouse environment data includes the daily average dust concentration, the maximum microbial content, the daily average temperature difference and the daily average humidity difference of each warehouse sub-area.

[0014] As a preferred technical solution, the analysis obtains the textile monitoring characterization value of each warehouse sub-area, and the specific analysis process is as follows: based on the first textile data analysis and processing, the textile storage characteristic value of each warehouse sub-area is obtained.

[0015] Based on the analysis and processing of the second warehouse environmental data, the environmental characteristic value of each warehouse sub-area is obtained.

[0016] According to the textile storage characteristic value of each warehouse sub-area and the environmental characteristic value of each warehouse sub-area, the textile monitoring characterization value of each warehouse sub-area is obtained through comprehensive analysis and processing.

[0017] The textile monitoring characterization value of each warehouse sub-area is a numerical result of quantifying the textile storage characteristic value of each warehouse sub-area and the environmental characteristic value of each warehouse sub-area, and is used to characterize the monitoring risk level of textiles in each warehouse sub-area.

[0018] As a preferred technical solution, the specific analysis process of the textile storage characteristic values ​​of each warehouse sub-area is as follows: extract the textile reference data stored in the database, and the textile reference data includes the reference average daily storage number of textiles, the reference average daily storage density of textiles, the reference cumulative number of stored textile types, and the reference average daily usage frequency of textiles.

[0019] According to the first textile data and the textile reference data, the textile storage characteristic value of each warehouse sub-area is obtained through comprehensive analysis and processing.

[0020] The textile storage characteristic value of each warehouse sub-area is a numerical result of quantifying the first textile data and the textile reference data, and is used to characterize the textile storage load degree of each warehouse sub-area.

[0021] As a preferred technical solution, the environmental characteristic values ​​of each warehouse sub-area are specifically analyzed as follows: the warehouse reference environmental data stored in the database is extracted, and the warehouse reference environmental data includes the warehouse's defined dust concentration, defined microbial content, defined temperature difference, and defined humidity difference.

[0022] Based on the second warehouse environment data and the warehouse reference environment data, the environmental characteristic value of each warehouse sub-area is obtained through comprehensive analysis and processing.

[0023] The environmental characteristic value of each warehouse sub-area is a numerical result of quantifying the second warehouse environmental data and the warehouse reference environmental data, and is used to characterize the complexity of environmental changes in each warehouse sub-area.

[0024] As an optimal technical solution, the abnormal warehouse sub-areas are screened for risk warning management, and the in-service sensor devices in each warehouse sub-area are intelligently managed and adjusted. The specific process is as follows: the textile monitoring risk threshold of the warehouse sub-area stored in the database is extracted.

[0025] When the textile monitoring characterization value of a warehouse sub-area is greater than the textile monitoring risk threshold of the warehouse sub-area, the warehouse sub-area is marked as an abnormal warehouse sub-area and a risk warning is issued. Conversely, when the textile monitoring characterization value of a warehouse sub-area is less than or equal to the textile monitoring risk threshold of the warehouse sub-area, it is recorded as a steady-state warehouse sub-area, and each abnormal warehouse sub-area and each steady-state warehouse sub-area are counted in turn.

[0026] The default execution and collection frequency of the in-service sensor devices in each warehouse sub-area is obtained, and the in-service sensor devices in each steady-state warehouse sub-area are controlled and managed with the corresponding default execution and collection frequency.

[0027] The difference between the textile monitoring characterization value and the textile monitoring risk threshold of each abnormal warehouse sub-area is extracted and recorded as the device adjustment reference value of each abnormal warehouse sub-area, and matched with the acquisition frequency supplement value corresponding to each device adjustment reference value interval stored in the database to obtain the acquisition frequency supplement value corresponding to each abnormal warehouse sub-area, and accumulated with the default execution acquisition frequency of the in-service sensor device in each abnormal warehouse sub-area to obtain the designated acquisition frequency of the in-service sensor device in each abnormal warehouse sub-area for control and management, thereby completing the intelligent management and adjustment of the in-service sensor devices in each warehouse sub-area.

[0028] As a preferred technical solution, the cloud computing-based intelligent warehousing management system for textiles also includes intelligent outbound management of textiles. The specific process is as follows: obtaining outbound instructions and counting the types of textiles in the outbound instructions, thereby obtaining a number of stored textiles in the warehouse that are consistent with the types of textiles in the outbound instructions, and marking them as textiles ready for outbound delivery.

[0029] Statistics are collected on the warehouse sub-areas to which each textile to be shipped belongs, and then sensor log data corresponding to each textile to be shipped belongs is obtained, which is recorded as the shipping impact data of each textile to be shipped. The shipping impact data includes the temperature, humidity and light intensity collected by the corresponding in-service sensor device each time for each textile to be shipped in the existing cycle.

[0030] According to the types of textiles in the outbound instruction, the outbound reference data sets of each type of textiles in the database are matched to obtain the outbound reference data set corresponding to the type of textiles in the outbound instruction, which is recorded as the target outbound reference data set. The target outbound reference data set includes reference temperature, reference humidity, and reference light intensity.

[0031] According to the outbound impact data of each textile to be shipped and the target outbound reference data set, a comprehensive analysis and processing is performed to obtain the outbound priority index of each textile to be shipped.

[0032] The outbound priority index of each textile to be shipped out is a numerical result of quantifying the outbound impact data of each textile to be shipped out and the target outbound reference data set, and is used to characterize the outbound priority of textiles in each warehouse sub-area.

[0033] As a preferred technical solution, the textile monitoring characterization value of each warehouse sub-area is specifically formulated as follows:

[0034] Among them, C iis the textile monitoring characterization value of the i-th warehouse sub-area, A i is the textile storage characteristic value of the i-th warehouse sub-area, B i is the environmental characteristic value of the i-th warehouse sub-area, Store eigenvalue influencing factors for textiles, is the influencing factor of the environmental characteristic value, i is the number of each warehouse sub-area, i=1,2,...,n, n is the number of warehouse sub-areas, and e is a natural constant.

[0035] As a preferred technical solution, the specific formula of the priority index of each textile product to be shipped out is as follows:

[0036] Among them, D j is the priority index of the jth textile product to be shipped out, a jk is the temperature collected by the in-service sensor device for the kth time in the existing cycle for the jth textile to be shipped out, a0 is the reference temperature, b jk is the humidity collected by the in-service sensor device for the kth time in the existing cycle for the jth textile to be shipped out, b0 is the reference humidity, c jk is the light intensity collected by the in-service sensor device for the kth time for the jth textile to be shipped out in the existing cycle, c0 is the reference light intensity, t j is the existing cycle duration of the jth textile to be prepared for shipment, z1 is the temperature weight, z2 is the humidity weight, z3 is the light intensity weight, z4 is the existing cycle duration weight, j is the number of each textile to be prepared for shipment, j=1,2,...,m, m is the number of textiles to be prepared for shipment, k is the number of each collection, k=1,2,...,s, s is the number of collections, and e is a natural constant.

[0037] The second aspect of the present invention provides a textile intelligent warehousing management method based on cloud computing, comprising the following steps: S1, counting a number of integrated sensor devices installed in the textile warehouse, recorded as each in-service sensor device, and counting the area of ​​the sensor area belonging to each in-service sensor device to divide the textile warehouse into each warehouse sub-area.

[0038] S2, counting and monitoring the stored textiles in each warehouse sub-area, and obtaining monitoring data of the stored textiles in each warehouse sub-area.

[0039] S3, based on the stored textile monitoring data of each warehouse sub-area, and inputting it into the cloud computing center, analyzing and obtaining the textile monitoring characterization value of each warehouse sub-area.

[0040] S4, based on the textile monitoring characterization values ​​of each warehouse sub-area, screen abnormal warehouse sub-areas for risk warning management, and intelligently manage and adjust the in-service sensor devices in each warehouse sub-area.

[0041] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0042] (1) The present invention realizes dynamic optimization and efficient operation of the warehouse monitoring system by providing a textile intelligent warehouse management system and method based on cloud computing. It can automatically adjust the working collection frequency of the sensor device according to the actual needs and environmental changes of different regions, thereby improving the intelligence level and sustainability of the entire warehouse management system.

[0043] (2) The present invention monitors the storage data of textiles in each warehouse sub-area, and can grasp the dynamic data in the warehouse in real time, and conduct comprehensive monitoring and analysis from multiple aspects such as the storage quantity and type of textiles to environmental parameters, thereby effectively reducing management costs and operational risks, and promoting the development of the textile warehousing industry towards intelligence and efficiency.

[0044] (3) The present invention obtains the textile monitoring characterization value of each warehouse sub-area by analysis, and can clearly quantify and evaluate the storage status and quality of each area. This allows managers to intuitively understand the advantages and disadvantages of different areas and carry out targeted resource allocation and optimization management. At the same time, it also provides key data basis for realizing refined warehouse management.

[0045] (4) The present invention can detect potential problems and risks in advance by screening abnormal warehouse sub-areas for risk warning management, which is conducive to taking timely measures to prevent and solve them, avoiding the quality decline or loss of textiles due to environmental abnormalities, improper storage, etc., and improving the reliability and stability of warehouse management.

[0046] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0048] Figure 2 It is a schematic diagram of the method flow of the present invention.

[0049] Figure 3 It is a scatter diagram of the relationship between the textile storage characteristic value of the warehouse sub-area and the textile monitoring characteristic value of the warehouse sub-area involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 As shown, the first aspect of an embodiment of the present invention provides a textile intelligent warehouse management system based on cloud computing, including: a warehouse monitoring area division module, which is used to count a number of integrated sensor devices set in the textile warehouse, recorded as each in-service sensor device, and count the area of ​​the sensor area belonging to each in-service sensor device to divide the textile warehouse into each warehouse sub-area.

[0052] It should be noted that the statistics of several integrated sensor devices installed in the textile warehouse refer to a comprehensive inventory and record of the sensor equipment used to monitor various key data in the warehouse. Through accurate statistics of each in-service sensor device, the distribution of monitoring resources currently owned by the warehouse can be clearly understood.

[0053] It should be noted that each of the in-service sensing devices is integrated from a plurality of sensors, including not only dust sensors, microbial monitoring sensors, temperature sensors and humidity sensors.

[0054] It should also be noted that the area of ​​the sensing region to which each in-service sensing device belongs is counted, which is the specific area of ​​the warehouse that each sensing device is responsible for monitoring, and can be extracted from the background of the warehouse management system.

[0055] It should be understood that the specific area range of the warehouse refers to the maximum spatial range that each sensor device can effectively monitor under the established technical parameters and installation conditions, which is usually pre-set by the warehouse manager during installation based on the production design specifications of the sensor device.

[0056] It should also be noted that, in the embodiment, there may be an overlap between the areas of the subordinate sensing regions of the two in-service sensing devices. In this case, the overlapping area between the areas of the subordinate sensing regions of the two in-service sensing devices can be allocated with equal areas. The equal area allocation refers to evenly allocating the overlapping area covered by the two sensing devices to the two sensing devices on the principle of equally dividing the area. In the embodiment, the overlapping area can be divided and allocated to the corresponding sensors according to the principle of proximity. Through equal area allocation, there is no monitoring overlap in the areas of the subordinate sensing regions of the two in-service sensing devices when actual supervision is met.

[0057] In a specific embodiment, through the detailed statistics and division of the area of ​​each in-service sensor device and its affiliated sensor area, it is not only helpful to build a comprehensive and sophisticated warehouse monitoring system, but also can provide strong support for the subsequent accurate monitoring and management of textiles in each warehouse sub-area. It can reasonably allocate resources and formulate management strategies according to the characteristics and needs of different areas, improve the efficiency and scientificity of warehouse management, and also lay a solid foundation for realizing intelligent and precise management of textile warehousing.

[0058] The warehouse textile storage monitoring module is used to count the stored textiles in each warehouse sub-area for monitoring and obtain the storage textile monitoring data of each warehouse sub-area.

[0059] In this embodiment, the statistics of the stored textiles in each warehouse sub-area are monitored to obtain the stored textile monitoring data of each warehouse sub-area. The specific process is as follows: within a preset monitoring period, the stored textiles in each warehouse sub-area are monitored in real time to obtain the stored textile monitoring data of each warehouse sub-area.

[0060] In this embodiment, by monitoring the storage data of textiles in each warehouse sub-area, it is possible to grasp the dynamic data in the warehouse in real time, and conduct comprehensive monitoring and analysis from multiple aspects such as the storage quantity and type of textiles to environmental parameters, thereby effectively reducing management costs and operational risks, and promoting the development of the textile warehousing industry towards intelligence and efficiency.

[0061] The storage textile monitoring data of each warehouse sub-area includes first textile data and second warehouse environment data, wherein the first textile data can be directly monitored and extracted through the background management log records of the warehouse management system, and the second warehouse environment data can be monitored and obtained through in-service sensor devices.

[0062] The first textile data includes the average daily storage number of textiles in each warehouse sub-area, the average daily storage density of textiles, the cumulative number of types of stored textiles, and the average daily use frequency of textiles.

[0063] The second warehouse environment data includes the daily average dust concentration, the maximum microbial content, the daily average temperature difference and the daily average humidity difference of each warehouse sub-area.

[0064] It should be understood that the average daily storage density of textiles is the ratio of the number of textiles recorded in each warehouse sub-area to the area of ​​each warehouse sub-area during a preset monitoring period of textiles, and is obtained through averaging.

[0065] It should be understood that the types of textiles include but are not limited to cotton fabrics, linen fabrics, silk fabrics, polyester fiber textiles, nylon textiles, acrylic textiles, and the like.

[0066] It should be understood that the average daily usage frequency of textiles is the frequency of textile entry and exit recorded in each warehouse sub-area within a preset monitoring period, and is obtained through averaging.

[0067] It should be understood that the daily average temperature difference of each warehouse sub-area is the difference between the highest temperature and the lowest temperature recorded in each warehouse sub-area every day within the preset monitoring period, and is obtained through mean processing. By accurately monitoring and analyzing the daily average temperature difference, the temperature fluctuation in the warehouse sub-area can be clearly understood.

[0068] It should be understood that the daily average humidity difference of each warehouse sub-area is the difference between the highest humidity and the lowest humidity recorded in each warehouse sub-area every day within the preset monitoring period, and is obtained through mean processing. By accurately considering the daily average humidity difference, the fluctuation range and changes of humidity in the warehouse sub-area can be effectively reflected.

[0069] The monitoring data analysis module is used to store textile monitoring data of each warehouse sub-area and input it into the cloud computing center to analyze and obtain the textile monitoring characterization value of each warehouse sub-area.

[0070] It should be noted that the cloud computing center refers to a comprehensive service platform that integrates high-performance computing resources, large-scale data storage capabilities, and advanced data processing and analysis technologies. In a specific embodiment, virtualization technology can be used to encapsulate computing resources, storage resources, and network resources into an independent virtual environment to provide on-demand computing services, data storage services, and complex data analysis and processing services for enterprises or individual users.

[0071] In this embodiment, the cloud computing center enables warehouse managers to efficiently collect, analyze and use textile monitoring data, thereby optimizing warehouse management, improving operational efficiency and ensuring the quality and safety of textiles.

[0072] In this embodiment, the analysis obtains the textile monitoring characterization value of each warehouse sub-area, and the specific analysis process is as follows: based on the first textile data analysis and processing, the textile storage characteristic value of each warehouse sub-area is obtained.

[0073] In this embodiment, the specific analysis process of the textile storage characteristic values ​​of each warehouse sub-area is as follows: extract the textile reference data stored in the database, and the textile reference data includes the reference average daily storage number of textiles, the reference average daily storage density of textiles, the reference cumulative number of stored textile types, and the reference average daily usage frequency of textiles.

[0074] It should be noted that the database is used to store reference indicator data, and the reference indicator data includes first reference indicator data, second reference indicator data and third reference indicator data.

[0075] The first reference indicator data includes textile reference data and warehouse reference environment data, and also includes the textile monitoring risk threshold of the warehouse sub-area, the default execution collection frequency and the collection frequency supplementary value corresponding to the adjustment benchmark value interval of each device, and also includes the target outbound reference data set.

[0076] The second reference indicator data includes the average daily storage number weight, the average daily storage density weight, the cumulative textile type weight and the average daily use frequency weight, as well as the dust concentration weight, the microbial content weight, the temperature difference weight and the humidity difference weight, as well as the temperature weight, the humidity weight, the light intensity weight and the existing cycle duration weight.

[0077] The third reference index data includes a textile storage characteristic value influencing factor and an environmental characteristic value influencing factor.

[0078] According to the first textile data and the textile reference data, the textile storage characteristic value of each warehouse sub-area is obtained through comprehensive analysis and processing.

[0079] The textile storage characteristic value of each warehouse sub-area is a numerical result of quantifying the first textile data and the textile reference data, and is used to characterize the textile storage load degree of each warehouse sub-area.

[0080] In a specific embodiment, the textile storage characteristic value of each warehouse sub-area is specifically expressed as follows:

[0081]

[0082] Among them, A i is the textile storage feature value of the i-th warehouse sub-area, α i is the average daily storage number of textiles in the i-th warehouse sub-area, α0 is the reference average daily storage number of textiles, β i is the average daily storage density of textiles in the i-th warehouse sub-area, β0 is the reference average daily storage density of textiles, γ i is the cumulative number of textile types stored in the i-th warehouse sub-area, γ0 is the reference cumulative number of textile types stored, δ i is the average daily frequency of textile use in the i-th warehouse sub-area, δ0 is the reference average daily frequency of textile use, x1 is the weight of the average daily storage number, x2 is the weight of the average daily storage density, x3 is the cumulative weight of the number of textile types, x4 is the weight of the average daily frequency of use, i is the number of each warehouse sub-area, i = 1, 2, ..., n, n is the number of warehouse sub-areas, and e is a natural constant.

[0083] It should be noted that the average daily storage number weight has a value range between 0 and 1. In this embodiment, the average daily storage number weight is the average daily storage number weight preset in the database, which represents the numerical value of the influence of the average daily storage number on the storage characteristic value of the textile. When used, the preset average daily storage number weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the average daily storage number and the average daily storage number weight preset in the database form a mapping set, and the real-time average daily storage number is input into the mapping set to obtain the average daily storage number weight, wherein the mapping relationship is one-to-one correspondence.

[0084] It should be noted that the value range of the textile daily storage density weight is between 0 and 1. In this embodiment, the textile daily storage density weight is the textile daily storage density weight preset in the database, which represents the numerical value of the influence of the textile daily storage density on the textile storage characteristic value. When used, the preset textile daily storage density weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the textile daily storage density and the textile daily storage density weight preset in the database form a mapping set, and the real-time textile daily storage density is input into the mapping set to obtain the textile daily storage density weight, wherein the mapping relationship is one-to-one correspondence.

[0085] It should be noted that the cumulative textile type weight has a value range between 0 and 1. In this embodiment, the cumulative textile type weight is a cumulative textile type weight preset in the database, which represents a numerical value of the degree of influence of the cumulative textile type on the textile storage characteristic value. When used, the preset cumulative textile type weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the cumulative textile types and the cumulative textile type weights preset in the database form a mapping set, and the real-time cumulative textile types are input into the mapping set to obtain the cumulative textile type weights, wherein the mapping relationship is one-to-one correspondence.

[0086] It should be noted that the average daily usage frequency weight has a value range between 0 and 1. In this embodiment, the average daily usage frequency weight is a preset average daily usage frequency weight in the database, which represents a numerical value of the influence of the average daily usage frequency on the storage characteristic value of the textile. When used, the preset average daily usage frequency weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the average daily usage frequency and the preset average daily usage frequency weight in the database form a mapping set, and the real-time average daily usage frequency is input into the mapping set to obtain the average daily usage frequency weight, wherein the mapping relationship is one-to-one correspondence.

[0087] It should also be noted that in this embodiment, the textile storage characteristic value of each warehouse sub-area is obtained by processing the average daily storage number of textiles, the average daily storage density of textiles, the cumulative number of stored textile types and the average daily use frequency of textiles. This takes into account the mutual influence between these parameters. For example, a higher average daily storage number of textiles usually means that the storage business in this warehouse sub-area is more frequent, but if the average daily storage density of textiles is too large at the same time, this may lead to crowded space, which is not conducive to the classification and sorting and retrieval and placement operations of textiles. When the cumulative number of stored textile types is large, the complexity of management will be increased. If the average daily use frequency of textiles is also high at this time, confusion and errors may easily occur during the frequent storage and retrieval process, increasing the difficulty of finding the right products. The time cost and error probability of finding specific textiles, and the textile storage characteristic values ​​obtained by comprehensively analyzing the average daily storage number of textiles, the average daily storage density of textiles, the cumulative number of stored textile types, and the average daily use frequency of textiles can accurately reflect the comprehensive status and characteristics of textile storage in each warehouse sub-area, and provide key basis for subsequent warehouse management decisions. If the combination of these parameters in a certain warehouse sub-area shows a more special situation, even if there seems to be no problem from a single parameter, a careful and in-depth analysis is required to find the most suitable management optimization plan for the area to ensure that the storage and use of textiles can be carried out efficiently and orderly, and to avoid problems such as inefficiency, waste of resources, and even damage to textiles due to improper management.

[0088] Taking the four different warehouse sub-areas involved in this embodiment as an example, when the average daily storage number of textiles in these four warehouse sub-areas is 120 pieces per day and the average daily storage density of textiles is 6 pieces per square meter, the reference average daily storage number of textiles is defined as 100 pieces per day, the reference average daily storage density of textiles is defined as 5 pieces per square meter, the reference cumulative number of types of stored textiles is defined as 50, the reference average daily use frequency of textiles is defined as 20 times per day, the average daily storage number weight is defined as 0.3, the average daily storage density weight is defined as 0.2, the cumulative textile type weight is defined as 0.15, and the average daily use frequency weight is defined as 0.35. Based on the different cumulative number of types of stored textiles and the average daily use frequency of textiles, the textile storage characteristic values ​​of the warehouse sub-areas obtained through processing are as follows:

[0089] Table 1 Textile storage characteristic values ​​of warehouse sub-areas

[0090]

[0091] Combined with the above table, it can be seen that the textile storage characteristic value of the fourth group of warehouse sub-areas ranks the highest, which indicates that compared with other groups, the textile storage pressure faced by the fourth group of warehouse sub-areas is the most significant. In specific examples, for warehouse sub-areas with a relatively high cumulative number of textile types stored and a relatively high average daily frequency of textile use, the complexity of their inventory management and the pressure on the warehouse entry and exit management system are relatively large. In other words, this warehouse sub-area is under great pressure in terms of textile storage, which means that the collection frequency of the sensor device needs to be increased to ensure that textiles in the warehouse can be safely stored.

[0092] In a specific embodiment, by analyzing the storage characteristic values ​​of textiles in each warehouse sub-area, the actual storage conditions and pressure of textiles in each warehouse sub-area can be accurately grasped, and timely measures can be taken to optimize and adjust.

[0093] Based on the analysis and processing of the second warehouse environmental data, the environmental characteristic value of each warehouse sub-area is obtained.

[0094] In this embodiment, the environmental characteristic values ​​of each warehouse sub-area are specifically analyzed as follows: the warehouse reference environment data stored in the database is extracted, and the warehouse reference environment data includes the warehouse's defined dust concentration, defined microbial content, defined temperature difference, and defined humidity difference.

[0095] Based on the second warehouse environment data and the warehouse reference environment data, the environmental characteristic value of each warehouse sub-area is obtained through comprehensive analysis and processing.

[0096] The environmental characteristic value of each warehouse sub-area is a numerical result of quantifying the second warehouse environmental data and the warehouse reference environmental data, and is used to characterize the complexity of environmental changes in each warehouse sub-area.

[0097] In a specific embodiment, the environmental characteristic value of each warehouse sub-area is specifically expressed as follows:

[0098]

[0099] Among them, B i is the environmental characteristic value of the i-th warehouse sub-area, θ i is the average daily dust concentration of the i-th warehouse sub-area, θ0 is the defined dust concentration of the warehouse, μ i is the highest microbial content of the i-th warehouse sub-area, μ0 is the defined microbial content of the warehouse, σ i is the daily average temperature difference of the i-th warehouse sub-area, σ0 is the defined temperature difference of the warehouse, τ iis the average daily humidity difference of the i-th warehouse sub-area, τ0 is the defined humidity difference of the warehouse, x5 is the dust concentration weight, x6 is the microbial content weight, x7 is the temperature difference weight, x8 is the humidity difference weight, i is the number of each warehouse sub-area, i = 1, 2, ..., n, n is the number of warehouse sub-areas, and e is a natural constant.

[0100] It should be noted that the dust concentration weight has a value range between 0 and 1. In the present embodiment, the dust concentration weight is a dust concentration weight preset in a database, which represents a numerical value indicating the degree of influence of dust concentration on environmental characteristic values. When used, the preset dust concentration weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the dust concentration and the dust concentration weight preset in the database form a mapping set, and the real-time dust concentration is input into the mapping set to obtain the dust concentration weight, wherein the mapping relationship is one-to-one.

[0101] It should be noted that the microbial content weight has a value range between 0 and 1. In this embodiment, the microbial content weight is a microbial content weight preset in the database, which represents a numerical value of the influence of the microbial content on the environmental characteristic value. When used, the preset microbial content weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the microbial content and the microbial content weight preset in the database form a mapping set, and the real-time microbial content is input into the mapping set to obtain the microbial content weight, wherein the mapping relationship is one-to-one correspondence.

[0102] It should be noted that the temperature difference weight has a value range between 0 and 1. In the present embodiment, the temperature difference weight is a temperature difference weight preset in the database, which represents a numerical value indicating the degree of influence of the temperature difference on the environmental characteristic value. When used, the preset temperature difference weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the temperature difference and the temperature difference weight preset in the database form a mapping set, and the real-time temperature difference is input into the mapping set to obtain the temperature difference weight, wherein the mapping relationship is one-to-one.

[0103] It should be noted that the humidity difference weight has a value range between 0 and 1. In the present embodiment, the humidity difference weight is a humidity difference weight preset in the database, which represents a numerical value indicating the degree of influence of the humidity difference on the environmental characteristic value. When used, the preset humidity difference weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the humidity difference and the humidity difference weight preset in the database form a mapping set, and the real-time humidity difference is input into the mapping set to obtain the humidity difference weight, wherein the mapping relationship is one-to-one.

[0104] It should also be noted that in this embodiment, the environmental characteristic values ​​of each warehouse sub-area are obtained by processing the average daily dust concentration, the maximum microbial content, the average daily temperature difference and the average daily humidity difference, taking into account the mutual influence between these parameters. For example, a higher average daily dust concentration usually means that the air quality in the warehouse sub-area is poor, which may affect the cleanliness and quality of the textiles, especially for some delicate textiles, dust may adhere to their surface, affecting their appearance and performance. However, if the microbial content is low at the same time, the potential biological damage risk to the textiles may be reduced to a certain extent. However, under suitable conditions, microorganisms may use dust particles as carriers to reproduce and spread. When the average daily temperature difference is large, it will cause the warehouse sub-area to have a certain degree of microbial damage. The temperature fluctuates frequently, which will not only cause thermal expansion and contraction to the fiber structure of textiles, accelerating their aging and damage, but also may affect the stability of humidity. For example, when the temperature rises, the relative humidity may decrease, and when the temperature drops, the relative humidity may increase. This change in humidity will further affect the preservation state of textiles. If the average daily humidity difference is large, the textiles will face the risk of moisture or dryness. A humid environment may cause textiles to mold and deteriorate, while an overly dry environment may make textiles fragile, prone to static electricity and breakage. At this time, if the average daily dust concentration is also high, the dust may absorb more moisture, forming a humid microenvironment conducive to the growth of microorganisms, further aggravating the damage to textiles.

[0105] The environmental characteristic values ​​obtained by comprehensive analysis of the average daily dust concentration, maximum microbial content, average daily temperature difference and average daily humidity difference can accurately reflect the environmental quality and stability of each warehouse sub-area, which is crucial for evaluating the storage suitability of textiles in this area. If these parameters in a certain warehouse sub-area interact with each other and present a complex situation, even if a single parameter appears to be within an acceptable range, it is necessary to carefully evaluate its potential impact on the storage of textiles so as to take targeted measures to optimize and adjust the environment, ensure a good storage environment for textiles, and avoid quality degradation and damage of textiles due to environmental factors.

[0106] In a specific embodiment, by analyzing the environmental characteristic values ​​of each warehouse sub-area, the environmental conditions of each warehouse sub-area can be accurately quantified, and the performance of each area in key environmental parameters such as dust concentration, microbial content, temperature difference, and humidity difference can be clearly understood, providing warehouse managers with a scientific data basis to help them make more reasonable decisions in resource allocation.

[0107] According to the textile storage characteristic value of each warehouse sub-area and the environmental characteristic value of each warehouse sub-area, the textile monitoring characterization value of each warehouse sub-area is obtained through comprehensive analysis and processing.

[0108] The textile monitoring characterization value of each warehouse sub-area is a numerical result of quantifying the textile storage characteristic value of each warehouse sub-area and the environmental characteristic value of each warehouse sub-area, and is used to characterize the monitoring risk level of textiles in each warehouse sub-area.

[0109] In a specific embodiment, the textile monitoring characterization value of each warehouse sub-area is specifically expressed by the following formula:

[0110] Among them, C i is the textile monitoring characterization value of the i-th warehouse sub-area, A i is the textile storage characteristic value of the i-th warehouse sub-area, B i is the environmental characteristic value of the i-th warehouse sub-area, Store eigenvalue influencing factors for textiles, is the influencing factor of the environmental characteristic value, i is the number of each warehouse sub-area, i=1,2,...,n, n is the number of warehouse sub-areas, and e is a natural constant.

[0111] It should be noted that the textile storage characteristic value influencing factor has a value range between 0 and 1. In this embodiment, the textile storage characteristic value influencing factor is a textile storage characteristic value influencing factor preset in the database, which represents the numerical value of the influence degree of the textile storage characteristic value on the textile monitoring characterization value. When used, the preset textile storage characteristic value influencing factor can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the textile storage characteristic value and the textile storage characteristic value influencing factor preset in the database form a mapping set, and the real-time textile storage characteristic value is input into the mapping set to obtain the textile storage characteristic value influencing factor, wherein the mapping relationship is one-to-one correspondence.

[0112] It should be noted that the environmental characteristic value influencing factor has a value range between 0 and 1. In this embodiment, the environmental characteristic value influencing factor is an environmental characteristic value influencing factor preset in the database, which represents the numerical value of the degree of influence of the environmental characteristic value on the textile monitoring characterization value. When used, the preset environmental characteristic value influencing factor can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the environmental characteristic value and the environmental characteristic value influencing factor preset in the database form a mapping set, and the real-time environmental characteristic value is input into the mapping set to obtain the environmental characteristic value influencing factor, wherein the mapping relationship is one-to-one correspondence.

[0113] It should also be noted that in this embodiment, the textile monitoring characterization value of each warehouse sub-area is obtained by processing according to the textile storage characteristic value and the environmental characteristic value, taking into account the mutual influence between these parameters. For example, when the textile storage characteristic value is high, it means that the storage quantity, type, density and usage frequency of the textiles in the warehouse sub-area are high, indicating that the storage monitoring demand of the textiles in the warehouse sub-area is high. However, if the environmental characteristic value is low at this time, that is, the environmental quality of the warehouse sub-area is good, the storage monitoring demand of the textiles in the warehouse sub-area will be reduced to a certain extent. If the environmental characteristic value is high, it means that the dust concentration in the warehouse sub-area is too high, the microbial content exceeds the standard, and the temperature difference and humidity difference are large, which will have a negative impact on the quality of the stored textiles. High-value textiles may be damaged due to the poor quality. environment and depreciate rapidly. For example, fine fiber materials may be worn in a high dust environment, or deformed or faded under unstable temperature and humidity conditions. The textile monitoring characterization value obtained by comprehensively considering the textile storage characteristic value and the environmental characteristic value can more comprehensively reflect the comprehensive situation of the warehouse sub-area. It not only reflects the storage situation of the textiles, but also combines the degree of influence of the environment on it. For example, when the monitoring characterization value is high, it may be the result of the interaction between storage and environmental factors. In-depth analysis and comprehensive improvement measures are needed, such as optimizing the storage layout and strengthening environmental control, to ensure that the textiles can be well stored and managed in the warehouse sub-area, improve the quality and efficiency of the entire warehouse operation, and avoid unnecessary losses and risks due to one-sided consideration of the problem.

[0114] See also Figure 3As shown, a scatter plot of the relationship between the textile storage characteristic value of the warehouse sub-area involved in the embodiment of the present invention and the textile monitoring characterization value of the warehouse sub-area is shown, wherein the horizontal axis is the textile storage characteristic value of the warehouse sub-area, and the vertical axis is the textile monitoring characterization value of the warehouse sub-area. The multiple scatter points in the figure are the textile monitoring characterization values ​​corresponding to the textile storage characteristic value of the warehouse sub-area involved in this embodiment. It can be clearly seen from the figure that with the increase of the textile storage characteristic value, the textile monitoring characterization value generally shows an upward trend, which indicates that when the textile storage characteristic value increases, the storage pressure of the textile warehouse increases, and the textile monitoring characterization value increases. However, the distribution of the scatter points does not show a strict linear relationship, but has a certain discreteness, which reflects that in the actual warehouse environment, in addition to the factor of the textile storage characteristic value, there are many other factors that will affect the textile monitoring characterization value. When the textile storage characteristic value of the warehouse sub-area increases, it may mean that the warehouse needs a larger storage space and a more sophisticated management strategy, which may lead to an increase in the monitoring characterization value. However, due to the influence of the environmental characteristic value, even if the storage characteristic value increases, the change of the monitoring characterization value may be limited. For example, if the environmental characteristic value is low, indicating that the warehouse environment conditions are good, then even if the storage characteristic value increases, the monitoring characteristic value may not increase significantly, or may even decrease. In addition, the distribution of scatter points also shows the differences between the various warehouse sub-areas. Each warehouse sub-area may have different environmental conditions and storage capacities. These factors work together to lead to the discreteness of the relationship between the monitoring characteristic value and the storage characteristic value.

[0115] In this embodiment, by analyzing the textile monitoring characterization values ​​of each warehouse sub-area, the storage status and quality of each area can be clearly quantified and evaluated, which allows managers to intuitively understand the advantages and disadvantages of different areas and carry out targeted resource allocation and optimization management. It also provides key data basis for realizing refined warehouse management.

[0116] The textile storage management module is used to screen abnormal warehouse sub-areas for risk warning management based on the textile monitoring characterization values ​​of each warehouse sub-area, and to intelligently manage and adjust the in-service sensor devices in each warehouse sub-area.

[0117] In this embodiment, the abnormal warehouse sub-areas are screened for risk warning management, and the in-service sensor devices in each warehouse sub-area are intelligently managed and adjusted. The specific process is as follows: the textile monitoring risk threshold of the warehouse sub-area stored in the database is extracted.

[0118] When the textile monitoring characterization value of a warehouse sub-area is greater than the textile monitoring risk threshold of the warehouse sub-area, the warehouse sub-area is marked as an abnormal warehouse sub-area and a risk warning is issued. Conversely, when the textile monitoring characterization value of a warehouse sub-area is less than or equal to the textile monitoring risk threshold of the warehouse sub-area, it is recorded as a steady-state warehouse sub-area, and each abnormal warehouse sub-area and each steady-state warehouse sub-area are counted in turn.

[0119] Taking this embodiment as an example, the risk warning is to synchronously transmit the warning content to the receiving end of the corresponding management personnel. The specific warning content can be that the textile monitoring characteristic value of the current warehouse sub-area has exceeded the risk threshold, and there may be abnormal textile storage in the area. Please take corresponding measures immediately to adjust and inspect the quality of textiles to ensure the storage safety and stable quality of textiles in the warehouse, and ensure the normal operation of the warehousing business.

[0120] In a specific embodiment, by screening abnormal warehouse sub-areas for risk warning management, potential problems and risks can be discovered in advance, which is conducive to taking timely measures to prevent and resolve them, avoiding the decline or loss of textile quality due to environmental abnormalities, improper storage, etc., and improving the reliability and stability of warehouse management.

[0121] The default execution and collection frequency of the in-service sensor devices in each warehouse sub-area is obtained, and the in-service sensor devices in each steady-state warehouse sub-area are controlled and managed with the corresponding default execution and collection frequency.

[0122] It should be noted that the default execution collection frequency refers to the frequency of data collection pre-set by each in-service sensor device in the warehouse sub-area under normal conditions. The default execution collection frequency is stored in the database and is a basic collection frequency value set based on comprehensive consideration of multiple factors such as the usual environmental changes in the steady-state warehouse sub-area, the general storage requirements of textiles, and the equipment's own performance and energy consumption.

[0123] The difference between the textile monitoring characterization value and the textile monitoring risk threshold of each abnormal warehouse sub-area is extracted and recorded as the device adjustment reference value of each abnormal warehouse sub-area, and matched with the acquisition frequency supplement value corresponding to each device adjustment reference value interval stored in the database to obtain the acquisition frequency supplement value corresponding to each abnormal warehouse sub-area, and accumulated with the default execution acquisition frequency of the in-service sensor device in each abnormal warehouse sub-area to obtain the designated acquisition frequency of the in-service sensor device in each abnormal warehouse sub-area for control and management, thereby completing the intelligent management and adjustment of the in-service sensor devices in each warehouse sub-area.

[0124] It should be understood that the accumulation of the default execution acquisition frequency of the in-service sensor devices in each abnormal warehouse sub-area refers to the process of adding the acquisition frequency supplement value corresponding to each abnormal warehouse sub-area to the basic acquisition frequency value originally executed by each in-service sensor device in a normal state according to the established settings, that is, the default execution acquisition frequency.

[0125] It should also be understood that the designated collection frequency refers to a specific frequency for data collection by the in-service sensor devices in the area, which is ultimately determined through cumulative calculation based on the specific conditions of each abnormal warehouse sub-area.

[0126] In a specific embodiment, by intelligently managing and adjusting the in-service sensor devices in each warehouse sub-area, dynamic optimization and efficient operation of the warehouse monitoring system are achieved. The working parameters and collection frequency of the sensor devices can be automatically adjusted according to the actual needs and environmental changes of different areas, thereby improving the intelligence level and sustainability of the entire warehouse management system.

[0127] In a specific embodiment, the cloud computing-based intelligent warehousing management system for textiles also includes intelligent outbound management of textiles. The specific process is as follows: obtaining outbound instructions and counting the types of textiles in the outbound instructions, thereby obtaining a number of stored textiles in the warehouse that are consistent with the types of textiles in the outbound instructions, and marking them as textiles ready for outbound delivery.

[0128] It should be noted that the statistical types of textiles in the outbound delivery instructions refer to the detailed identification and classification of the characteristics of the textiles involved in the outbound delivery instructions. The types of textiles include but are not limited to cotton fabrics, linen fabrics, silk fabrics, polyester fiber textiles, nylon textiles, acrylic textiles, etc.

[0129] It should also be noted that the statistically obtained number of stored textiles in the warehouse that are consistent with the types of textiles in the outbound instruction refers to a process of comprehensive search and screening of the stored textiles in the entire warehouse based on the textile type information specified in the outbound instruction.

[0130] Statistics are collected on the warehouse sub-areas to which each textile to be shipped belongs, and then sensor log data corresponding to each textile to be shipped belongs is obtained, which is recorded as the shipping impact data of each textile to be shipped. The shipping impact data includes the temperature, humidity and light intensity collected by the corresponding in-service sensor device each time for each textile to be shipped in the existing cycle.

[0131] It should be noted that the counting of warehouse sub-areas to which each textile to be shipped belongs refers to a process of determining the specific warehouse sub-area where each textile to be shipped is located by sorting out and locating the storage location information of each textile to be shipped in the warehouse in detail.

[0132] It should be noted that the sensor log data corresponding to the warehouse sub-areas to which the textiles to be shipped out belong are extracted from the database.

[0133] It should also be understood that the existing cycle refers to the storage time of each textile product to be shipped out in the corresponding warehouse sub-area.

[0134] According to the type of textiles in the outbound instruction, the outbound reference data sets of each type of textiles stored in the database are matched to obtain the outbound reference data set corresponding to the type of textiles in the outbound instruction, which is recorded as the target outbound reference data set. The target outbound reference data set includes reference temperature, reference humidity, and reference light intensity.

[0135] According to the outbound impact data of each textile to be shipped and the target outbound reference data set, a comprehensive analysis and processing is performed to obtain the outbound priority index of each textile to be shipped.

[0136] The outbound priority index of each textile to be shipped out is a numerical result of quantifying the outbound impact data of each textile to be shipped out and the target outbound reference data set, and is used to characterize the outbound priority of textiles in each warehouse sub-area.

[0137] In a specific embodiment, the specific formula of the priority index of each of the textile products to be shipped out is as follows:

[0138]

[0139] Among them, D j is the priority index of the jth textile product to be shipped out, a jk is the temperature collected by the in-service sensor device for the kth time in the existing cycle for the jth textile to be shipped out, a0 is the reference temperature, b jk is the humidity collected by the in-service sensor device for the kth time in the existing cycle for the jth textile to be shipped out, b0 is the reference humidity, c jk is the light intensity collected by the in-service sensor device for the kth time for the jth textile to be shipped out in the existing cycle, c0 is the reference light intensity, t j is the existing cycle duration of the jth textile to be prepared for shipment, z1 is the temperature weight, z2 is the humidity weight, z3 is the light intensity weight, z4 is the existing cycle duration weight, j is the number of each textile to be prepared for shipment, j=1,2,...,m, m is the number of textiles to be prepared for shipment, k is the number of each collection, k=1,2,...,s, s is the number of collections, and e is a natural constant.

[0140] It should be noted that the temperature weight has a value range between 0 and 1. In this embodiment, the temperature weight is a temperature weight preset in the database, which represents a numerical value of the influence of temperature on the textile delivery priority index. When used, the preset temperature weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the temperature and the temperature weight preset in the database form a mapping set, and the real-time temperature is input into the mapping set to obtain the temperature weight, wherein the mapping relationship is one-to-one.

[0141] It should be noted that the humidity weight has a value range between 0 and 1. In this embodiment, the humidity weight is a humidity weight preset in the database, which represents the numerical value of the influence of humidity on the textile outbound priority index. When used, the preset humidity weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the humidity and the humidity weight preset in the database form a mapping set, and the real-time humidity is input into the mapping set to obtain the humidity weight, wherein the mapping relationship is one-to-one.

[0142] It should be noted that the light intensity weight has a value range between 0 and 1. In the present embodiment, the light intensity weight is a light intensity weight preset in the database, which indicates the numerical value of the influence of light intensity on the textile outbound priority index. When used, the preset light intensity weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the light intensity and the light intensity weight preset in the database form a mapping set, and the real-time light intensity is input into the mapping set to obtain the light intensity weight, wherein the mapping relationship is one-to-one.

[0143] It should be noted that the existing cycle duration weight has a value range between 0 and 1. In this embodiment, the existing cycle duration weight is an existing cycle duration weight preset in the database, which represents the numerical value of the influence of the existing cycle duration on the textile delivery priority index. When used, the preset existing cycle duration weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the existing cycle duration and the existing cycle duration weight preset in the database form a mapping set, and the real-time existing cycle duration is input into the mapping set to obtain the existing cycle duration weight, wherein the mapping relationship is one-to-one correspondence.

[0144] It should also be noted that in this embodiment, the priority index of each textile to be shipped out is obtained by processing the temperature, humidity, light intensity, and existing cycle duration collected each time, taking into account the mutual influence between these parameters. For example, a higher temperature may accelerate the reaction rate of certain chemicals in the textile, thereby affecting its quality and performance. If the existing cycle duration is also long at this time, the textile that has been in a high temperature environment for a long time may have undergone a certain degree of aging or deterioration. If the humidity is low, it may slow down this aging process to a certain extent, but it may also make the textile dry and fragile. When the humidity is high, the textile is easy to absorb moisture, which may cause problems such as mold and deformation. If the light intensity is also strong, then under the combined effect of high humidity and light, the fading of the textile and the damage of the fiber may be accelerated. If it is in such an unfavorable environment for a long time, the quality of the textile will be reduced accordingly, so it should be shipped out first. The priority index obtained by comprehensively considering the temperature, humidity, light intensity, and existing cycle duration collected each time can fully and accurately reflect the current state and suitability of each textile to be shipped out, and reduce the economic loss of the warehouse to a certain extent.

[0145] In a specific embodiment, the priority index of each textile to be shipped out is obtained through analysis and processing, which realizes the scientific quantification and precise planning of the order of the textiles to be shipped out. By giving priority to the shipment of textiles with higher indexes, the inventory backlog cost can be better reduced and the warehouse operation efficiency can be improved. At the same time, it also helps to optimize the space utilization inside the warehouse, free up suitable storage locations for newly-stocked textiles, and realize the dynamic balance and virtuous cycle of warehouse management.

[0146] See also Figure 2 As shown, the second aspect of an embodiment of the present invention provides a textile intelligent warehousing management method based on cloud computing, comprising the following steps: S1, counting a number of integrated sensor devices installed in the textile warehouse, recorded as each in-service sensor device, and counting the area of ​​the sensor area belonging to each in-service sensor device to divide the textile warehouse into each warehouse sub-area.

[0147] S2, counting and monitoring the stored textiles in each warehouse sub-area, and obtaining monitoring data of the stored textiles in each warehouse sub-area.

[0148] S3, based on the stored textile monitoring data of each warehouse sub-area, and inputting it into the cloud computing center, analyzing and obtaining the textile monitoring characterization value of each warehouse sub-area.

[0149] S4, based on the textile monitoring characterization values ​​of each warehouse sub-area, screen abnormal warehouse sub-areas for risk warning management, and intelligently manage and adjust the in-service sensor devices in each warehouse sub-area.

[0150] In this embodiment, a cloud computing-based intelligent warehouse management system and method for textiles are provided, which realizes dynamic optimization and efficient operation of the warehouse monitoring system. The working collection frequency of the sensor device can be automatically adjusted according to the actual needs and environmental changes of different areas, thereby improving the intelligence level and sustainability of the entire warehouse management system.

[0151] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0152] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. The cloud computing-based textile intelligent warehouse management system is characterized by: include: The storage monitoring area division module is used to count a number of integrated sensor devices installed in the textile warehouse, recorded as each in-service sensor device, and count the area of ​​the subordinate sensor area of ​​each in-service sensor device to divide the textile warehouse into each warehouse sub-area; The warehouse textile storage monitoring module is used to count the storage textiles in each warehouse sub-area for monitoring and obtain the storage textile monitoring data of each warehouse sub-area; A monitoring data analysis module is used to store textile monitoring data of each warehouse sub-area and input it into the cloud computing center to analyze and obtain the textile monitoring characterization value of each warehouse sub-area; The textile storage management module is used to screen abnormal warehouse sub-areas for risk warning management based on the textile monitoring characterization values ​​of each warehouse sub-area, and to intelligently manage and adjust the in-service sensor devices in each warehouse sub-area.

2. The cloud computing-based textile intelligent warehouse management system according to claim 1 is characterized by: The process of monitoring the stored textiles in each warehouse sub-area by counting the stored textiles in each warehouse sub-area to obtain the stored textile monitoring data of each warehouse sub-area is as follows: Under a preset monitoring cycle, the storage textiles in each warehouse sub-area are monitored in real time to obtain the storage textile monitoring data of each warehouse sub-area; The storage textile monitoring data of each warehouse sub-area includes first textile data and second warehouse environment data; The first textile data includes the average daily storage number of textiles in each warehouse sub-area, the average daily storage density of textiles, the cumulative number of types of stored textiles, and the average daily use frequency of textiles; The second warehouse environment data includes the daily average dust concentration, the maximum microbial content, the daily average temperature difference and the daily average humidity difference of each warehouse sub-area.

3. The cloud computing-based textile intelligent warehouse management system according to claim 2 is characterized by: The analysis obtains the textile monitoring characterization value of each warehouse sub-area, and the specific analysis process is as follows: Obtaining a textile storage characteristic value of each warehouse sub-area based on the first textile data analysis and processing; Based on the analysis and processing of the second warehouse environmental data, the environmental characteristic value of each warehouse sub-area is obtained; According to the textile storage characteristic value of each warehouse sub-area and the environmental characteristic value of each warehouse sub-area, a textile monitoring characteristic value of each warehouse sub-area is obtained by comprehensive analysis and processing; The textile monitoring characterization value of each warehouse sub-area is a numerical result of quantifying the textile storage characteristic value of each warehouse sub-area and the environmental characteristic value of each warehouse sub-area, and is used to characterize the monitoring risk level of textiles in each warehouse sub-area.

4. The cloud computing-based textile intelligent warehouse management system according to claim 3 is characterized by: The textile storage characteristic values ​​of each warehouse sub-area are analyzed in detail as follows: Extracting textile reference data stored in a database, the textile reference data including a reference average daily storage number of textiles, a reference average daily storage density of textiles, a reference cumulative number of types of stored textiles, and a reference average daily use frequency of textiles; According to the first textile data and the textile reference data, a textile storage characteristic value of each warehouse sub-area is obtained by comprehensive analysis and processing; The textile storage characteristic value of each warehouse sub-area is a numerical result of quantifying the first textile data and the textile reference data, and is used to characterize the textile storage load degree of each warehouse sub-area.

5. The cloud computing-based textile intelligent warehouse management system according to claim 3 is characterized by: The specific analysis process of the environmental characteristic values ​​of each warehouse sub-area is as follows: Extracting warehouse reference environment data stored in a database, wherein the warehouse reference environment data includes a defined dust concentration, a defined microbial content, a defined temperature difference, and a defined humidity difference of the warehouse; Based on the second warehouse environment data and the warehouse reference environment data, the environmental characteristic value of each warehouse sub-area is obtained by comprehensive analysis and processing; The environmental characteristic value of each warehouse sub-area is a numerical result of quantifying the second warehouse environmental data and the warehouse reference environmental data, and is used to characterize the complexity of environmental changes in each warehouse sub-area.

6. The cloud computing-based textile intelligent warehouse management system according to claim 1 is characterized by: The screening of abnormal warehouse sub-areas for risk early warning management and intelligent management and adjustment of the in-service sensor devices in each warehouse sub-area are as follows: extracting textile monitoring risk thresholds for warehouse sub-areas stored in the database; When the textile monitoring characterization value of a certain warehouse sub-area is greater than the textile monitoring risk threshold of the warehouse sub-area, the warehouse sub-area is marked as an abnormal warehouse sub-area and a risk warning is issued. Conversely, when the textile monitoring characterization value of a certain warehouse sub-area is less than or equal to the textile monitoring risk threshold of the warehouse sub-area, it is recorded as a stable warehouse sub-area, and each abnormal warehouse sub-area and each stable warehouse sub-area are counted in turn; Obtaining the default execution acquisition frequency of the in-service sensor devices in each warehouse sub-area, and controlling and managing the in-service sensor devices in each steady-state warehouse sub-area with the corresponding default execution acquisition frequency; The difference between the textile monitoring characterization value and the textile monitoring risk threshold of each abnormal warehouse sub-area is extracted and recorded as the device adjustment reference value of each abnormal warehouse sub-area, and matched with the acquisition frequency supplement value corresponding to each device adjustment reference value interval stored in the database to obtain the acquisition frequency supplement value corresponding to each abnormal warehouse sub-area, and accumulated with the default execution acquisition frequency of the in-service sensor device in each abnormal warehouse sub-area to obtain the designated acquisition frequency of the in-service sensor device in each abnormal warehouse sub-area for control and management, thereby completing the intelligent management and adjustment of the in-service sensor devices in each warehouse sub-area.

7. The cloud computing-based textile intelligent warehouse management system according to claim 1 is characterized by: It also includes intelligent outbound management of textiles. The specific process is as follows: Obtaining a delivery instruction and counting the types of textiles in the delivery instruction, thereby obtaining a number of stored textiles in the warehouse that are consistent with the types of textiles in the delivery instruction and marking them as textiles to be shipped; Count the subordinate warehouse sub-areas of each textile to be shipped out, and then obtain the sensor log data corresponding to the subordinate warehouse sub-areas of each textile to be shipped out, and record them as the shipping impact data of each textile to be shipped out, wherein the shipping impact data includes the temperature, humidity and light intensity collected by the corresponding in-service sensor device each time in the existing cycle of each textile to be shipped out; According to the textile type in the outbound instruction, the outbound reference data set of each textile type in the database is matched to obtain the outbound reference data set corresponding to the textile type in the outbound instruction, which is recorded as the target outbound reference data set, and the target outbound reference data set includes reference temperature, reference humidity, and reference light intensity; Based on the outbound impact data of each textile to be outbound and the target outbound reference data set, the outbound priority index of each textile to be outbound is obtained through comprehensive analysis and processing; The outbound priority index of each textile to be shipped out is a numerical result of quantifying the outbound impact data of each textile to be shipped out and the target outbound reference data set, and is used to characterize the outbound priority of textiles in each warehouse sub-area.

8. The cloud computing-based textile intelligent warehouse management system according to claim 3 is characterized by: The specific formula of the textile monitoring characterization value of each warehouse sub-area is as follows: Among them, C i is the textile monitoring characterization value of the i-th warehouse sub-area, A i is the textile storage characteristic value of the i-th warehouse sub-area, B i is the environmental characteristic value of the i-th warehouse sub-area, Store eigenvalue influencing factors for textiles, is the influencing factor of the environmental characteristic value, i is the number of each warehouse sub-area, i=1,2,...,n, n is the number of warehouse sub-areas, and e is a natural constant.

9. The cloud computing-based textile intelligent warehouse management system according to claim 7 is characterized by: The specific formula of the priority index of each textile product to be shipped out is as follows: Among them, D j is the priority index of the jth textile product to be shipped out, a jk is the temperature collected by the kth sensor device for the jth textile to be shipped out in the existing cycle, a0 is the reference temperature, b jk is the humidity collected by the in-service sensor device for the kth time in the existing cycle for the jth textile to be shipped out, b0 is the reference humidity, c jk is the light intensity collected by the in-service sensor device for the kth time for the jth textile to be shipped out in the existing cycle, c0 is the reference light intensity, t j is the existing cycle duration of the jth textile to be prepared for shipment, z1 is the temperature weight, z2 is the humidity weight, z3 is the light intensity weight, z4 is the existing cycle duration weight, j is the number of each textile to be prepared for shipment, j=1,2,...,m, m is the number of textiles to be prepared for shipment, k is the number of each collection, k=1,2,...,s, s is the number of collections, and e is a natural constant.

10. A textile intelligent warehousing management method based on cloud computing, characterized in that: The following steps are involved: S1, counting a number of integrated sensor devices installed in the textile warehouse, recorded as each in-service sensor device, and counting the area of ​​the subordinate sensor area of ​​each in-service sensor device to divide the textile warehouse into each warehouse sub-area; S2, counting the storage textiles in each warehouse sub-area for monitoring, and obtaining the storage textile monitoring data of each warehouse sub-area; S3, based on the stored textile monitoring data of each warehouse sub-area, and inputting it into the cloud computing center, analyzing and obtaining the textile monitoring characterization value of each warehouse sub-area; S4, based on the textile monitoring characterization values ​​of each warehouse sub-area, screen abnormal warehouse sub-areas for risk warning management, and intelligently manage and adjust the in-service sensor devices in each warehouse sub-area.

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