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CN-122022667-A - Intelligent warehouse full-effect management method and system

CN122022667ACN 122022667 ACN122022667 ACN 122022667ACN-122022667-A

Abstract

The invention discloses an intelligent storage full-efficiency management method and system, which relate to the technical field of storage management and comprise the steps of carrying out cargo value processing calculation through storage multi-source related data to obtain a cargo processing value coefficient, determining cargo storage problems according to the ratio of the cargo processing value coefficient to a preset threshold value, carrying out information feedback, carrying out spatial position correlation on collected environment related data and cargo self data based on cargo position coordinates to obtain a cargo transportation state coefficient, carrying out dynamic scheduling on cargo transportation based on the cargo transportation state coefficient, and realizing the functions of determining cargo problem positions and feeding back according to combination of multi-source data and carrying out dynamic adjustment on cargo transportation paths by combining spatial correlation of environment data and cargo self.

Inventors

  • Lin Sanwu
  • JIN HONG

Assignees

  • 安徽烽瑞通信科技有限公司

Dates

Publication Date
20260512
Application Date
20251217

Claims (10)

  1. 1. The intelligent warehouse full-effect management method is characterized by comprising the following steps of: The method comprises the steps of obtaining storage multi-source related data, preprocessing the storage multi-source related data to obtain processed storage multi-source related data, wherein the storage multi-source related data comprise cargo self-data, equipment state data and cargo state data, the cargo self-data comprise cargo volume, cargo weight and cargo size, the equipment state data comprise equipment speed and equipment temperature, and the cargo state data comprise cargo storage duration and cargo access frequency; Carrying out cargo value processing calculation on the processed multi-source related data to obtain a cargo processing value coefficient, carrying out ratio calculation based on the cargo processing value coefficient and a preset cargo value threshold value, determining cargo storage problems according to ratio results, feeding back cargo storage problem information, and carrying out corresponding checking and repairing based on the problem information feedback, wherein the cargo processing value coefficient is obtained by carrying out comprehensive processing on the value based on cargo self, equipment state and cargo state; The method comprises the steps of receiving environment-related data, acquiring cargo position coordinates, carrying out spatial position correlation on the environment-related data and cargo self data based on the cargo position coordinates to obtain cargo transportation state coefficients, wherein the environment-related data comprises temperature data, humidity data and illumination intensity data, and carrying out dynamic scheduling on cargo transportation based on the cargo transportation state coefficients, so that full-efficiency management on warehousing is achieved.
  2. 2. The intelligent warehouse full-efficiency management method according to claim 1, wherein the process of performing the cargo value processing calculation on the processed warehouse multi-source related data comprises calculating a cargo self value coefficient based on the processed cargo self data, calculating a device state value coefficient based on the processed device state data, and calculating a cargo state value coefficient based on the processed cargo state data; and carrying out comprehensive value processing based on the value coefficient of the cargo, the value coefficient of the equipment state and the value coefficient of the cargo state to obtain a value coefficient of cargo processing.
  3. 3. The intelligent warehouse full-effect management method according to claim 2, wherein the process of calculating the value coefficient of the cargo based on the processed cargo data is as follows: Marking the processed cargo self data, wherein the cargo volume is marked as Z1i, the cargo weight is marked as Z2i, and the cargo size is marked as Z3i; And calculating the value of the goods per se based on the marked data of the goods per se to obtain a value coefficient Zi of the goods per se, wherein the calculation formula is as follows: Wherein Z10 is a preset standard cargo volume coefficient, Z20 is a preset standard cargo weight coefficient, Z30 is a preset standard cargo size coefficient, and k 1 is a cargo self weight coefficient; Wherein i is the number label of the acquired data of the warehouse multi-source related data, and i=1, 2, 3, & gt, n and n are the total number of the acquired data of the warehouse multi-source related data.
  4. 4. The intelligent warehouse overall efficiency management method as set forth in claim 3, wherein the process of calculating the device state value based on the processed device state data to obtain the device state value coefficient comprises: Marking the device speed as S1i and the device temperature as S2i; using the formula Calculating to obtain an equipment state value coefficient Si, wherein S10 is a preset standard equipment speed coefficient, S20 is a preset standard equipment temperature coefficient, and k 2 is an equipment state weight coefficient; the process of calculating the cargo state value based on the processed cargo state data to obtain the cargo state value coefficient comprises the following steps: Marking the goods storage time length as H1i, and marking the goods access frequency as H2i; using the formula And calculating a cargo state value coefficient Hi, wherein H10 is a preset standard cargo storage duration coefficient, H20 is a preset standard cargo access frequency coefficient, and k3 is a cargo state weight coefficient.
  5. 5. The intelligent warehouse full-efficiency management method according to claim 4, wherein the comprehensive value processing is performed based on the value coefficient of the goods, the value coefficient of the equipment state and the value coefficient of the goods state to obtain the value coefficient of the goods processing, and the calculation formula is as follows: Wherein Ji is a cargo handling value coefficient, alpha, beta, gamma are preset proportional coefficients, and alpha+beta+gamma=1.
  6. 6. The intelligent warehouse full-effect management method according to claim 1, wherein the process of determining the cargo warehouse problem according to the ratio result and feeding back the information of the cargo warehouse problem comprises the following steps: According to the formula Calculating a ratio result Bli; If Bli is more than 0 and less than or equal to 1, feeding back information about cargo state problems; if 1< Bli is less than or equal to 2, feeding back information about the equipment state problem; And if Bli >2, feeding back information about the problem of the goods.
  7. 7. The intelligent warehouse overall management method according to claim 1, wherein the process of correlating the environmental related data with the cargo itself data in space position based on the cargo position coordinates comprises: determining environment monitoring point coordinates for collecting environment related data; the goods self data are correlated with the goods position coordinates, and the goods self data of the correlated position coordinates are obtained; Position-associating the goods self-data of the associated position coordinates with the environment monitoring point coordinates to obtain associated goods environment association coefficients, and marking the goods environment association coefficients as 。
  8. 8. The intelligent warehouse overall management method according to claim 1, wherein the calculating process of the cargo transportation state coefficient comprises: Marking the environment-related data, wherein the temperature data is marked as Tj, the humidity data is marked as Sj, and the illumination intensity data is marked as Gj, wherein j is the number label of the environment-related data, and j=1, 2, 3, the number of the environment-related data is equal to m, and m is the total number of the environment-related data; and (3) carrying out correlation calculation on the environment-related data and the cargo self data based on the spatial position to obtain a cargo transportation state coefficient, wherein the formula is as follows: Where Yij is a cargo transportation state coefficient, p 1 is a temperature influence coefficient, p 2 is a humidity influence coefficient, and p 3 is an illumination intensity influence coefficient.
  9. 9. The intelligent warehouse overall management method according to claim 1, wherein the process of dynamically scheduling the transportation of the goods based on the goods transportation state coefficient comprises: Setting a cargo safety transportation range set [ Ymin, ymid, ymax ] based on the cargo transportation state coefficient Yij; wherein, ymin is the minimum value of the cargo transportation state, ymid is the median value of the cargo transportation state, and Ymax is the maximum value of the cargo transportation state; If YIj is more than or equal to Ymax, the transportation path is not required to be adjusted; if YIj epsilon [ Ymid, ymax), triggering a primary adjustment mode, and adjusting transportation parameters without adjusting a transportation path; if YIj epsilon [ Ymin, ymid), triggering a secondary adjustment mode, and adjusting the transportation path; If YIj < Ymin, triggering the three-stage adjustment mode, and suspending the transportation of the goods.
  10. 10. Intelligent warehouse full-effect management system, its characterized in that includes: The data processing module is used for acquiring storage multi-source related data and preprocessing the storage multi-source related data to obtain processed storage multi-source related data, wherein the storage multi-source related data comprises cargo self data, equipment state data and cargo state data, the cargo self data comprises cargo volume, cargo weight and cargo size, the equipment state data comprises equipment speed and equipment temperature, and the cargo state data comprises cargo storage duration and cargo access frequency; The positioning detection module is used for carrying out cargo value processing calculation on the processed multi-source related data to obtain a cargo processing value coefficient, carrying out ratio calculation based on the cargo processing value coefficient and a preset cargo value threshold value, determining cargo storage problems according to ratio results, feeding back cargo storage problem information, and carrying out corresponding checking and repairing based on the problem information feedback, wherein the cargo processing value coefficient is obtained by carrying out comprehensive processing on the value based on the cargo itself, equipment state and cargo state; The dynamic scheduling module is used for receiving environment-related data, acquiring cargo position coordinates, performing spatial position correlation on the environment-related data and cargo self data based on the cargo position coordinates to obtain cargo transportation state coefficients, wherein the environment-related data comprises temperature data, humidity data and illumination intensity data, and performing dynamic scheduling on cargo transportation based on the cargo transportation state coefficients so as to realize full-efficiency management on storage.

Description

Intelligent warehouse full-effect management method and system Technical Field The invention relates to the technical field of warehouse management, in particular to an intelligent warehouse full-effect management method and system. Background Warehouse refers to the collective term for storing, safeguarding and storing materials by warehouse and the storage activities related to warehouse. It is generated along with the generation of material storage and also developed along with the development of productivity. The existing intelligent warehouse management method does not carry out cooperative judgment on all data related to warehouse, so that the efficiency of management feedback on intelligent warehouse is lower, and intelligent automatic management detection cannot be realized. Disclosure of Invention In order to solve the above-mentioned shortcomings in the background art, the present invention aims to provide an intelligent warehouse full-effect management method and system, which can determine the position of a cargo problem according to the combination of multi-source data and feed back the position, and dynamically adjust the cargo transportation path by combining the spatial correlation of environment data and the cargo itself. In a first aspect, the invention provides an intelligent warehouse full-effect management method, which comprises the following steps: The method comprises the steps of obtaining storage multi-source related data, preprocessing the storage multi-source related data to obtain processed storage multi-source related data, wherein the storage multi-source related data comprise cargo self-data, equipment state data and cargo state data, the cargo self-data comprise cargo volume, cargo weight and cargo size, the equipment state data comprise equipment speed and equipment temperature, and the cargo state data comprise cargo storage duration and cargo access frequency; Carrying out cargo value processing calculation on the processed multi-source related data to obtain a cargo processing value coefficient, carrying out ratio calculation based on the cargo processing value coefficient and a preset cargo value threshold value, determining cargo storage problems according to ratio results, feeding back cargo storage problem information, and carrying out corresponding checking and repairing based on the problem information feedback, wherein the cargo processing value coefficient is obtained by carrying out comprehensive processing on the value based on cargo self, equipment state and cargo state; The method comprises the steps of receiving environment-related data, acquiring cargo position coordinates, carrying out spatial position correlation on the environment-related data and cargo self data based on the cargo position coordinates to obtain cargo transportation state coefficients, wherein the environment-related data comprises temperature data, humidity data and illumination intensity data, and carrying out dynamic scheduling on cargo transportation based on the cargo transportation state coefficients, so that full-efficiency management on warehousing is achieved. With reference to the first aspect, in some implementations of the first aspect, the method further includes the steps of performing cargo value processing calculation on the processed storage multi-source related data, including calculating a cargo self value coefficient based on the processed cargo self data, performing device state value calculation based on the processed device state data to obtain a device state value coefficient, and performing cargo state value calculation based on the processed cargo state data to obtain a cargo state value coefficient; and carrying out comprehensive value processing based on the value coefficient of the cargo, the value coefficient of the equipment state and the value coefficient of the cargo state to obtain a value coefficient of cargo processing. With reference to the first aspect, in certain implementation manners of the first aspect, the method further includes calculating a cargo self-value coefficient based on the processed cargo self-data, where the process is as follows: Marking the processed cargo self data, wherein the cargo volume is marked as Z1i, the cargo weight is marked as Z2i, and the cargo size is marked as Z3i; And calculating the value of the goods per se based on the marked data of the goods per se to obtain a value coefficient Zi of the goods per se, wherein the calculation formula is as follows: Wherein Z10 is a preset standard cargo volume coefficient, Z20 is a preset standard cargo weight coefficient, Z30 is a preset standard cargo size coefficient, and k 1 is a cargo self weight coefficient; Wherein i is the number label of the acquired data of the warehouse multi-source related data, and i=1, 2, 3, & gt, n and n are the total number of the acquired data of the warehouse multi-source related data. With reference to the first aspect, in certain implementat