Electric power warehouse material monitoring method, system and equipment for intelligent warehouse management, and medium

Abstract

The invention is suitable for the technical field of warehouse material monitoring, and provides an electric power warehouse material monitoring method, system and device for intelligent warehouse management, and a medium, the method comprises the following steps: obtaining an inventory set of electric power warehouse materials in a preset period time, the inventory set comprising the inventory in a plurality of time periods; calculating the predicted demand quantity of the electric power warehouse materials in the preset period time based on grey correlation analysis; judging whether the difference value between the predicted demand quantity and the stock quantity presents a decline trend or not; if yes, whether the difference value between the predicted demand quantity and the stock quantity is within a threshold value range or not is judged; if yes, the information entropy is used for updating the predicted demand quantity of the electric power warehouse materials in the preset period time, and the stock quantity of the materials in the electric power warehouse is allocated according to the updated predicted demand quantity; and if not, allocating the material inventory of the electric power warehouse according to the predicted demand quantity of the electric power warehouse materials in the preset period time, thereby effectively improving the comprehensiveness and efficiency of material monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse material monitoring, and in particular to a method, system, equipment and medium for monitoring materials in a power warehouse for intelligent warehouse management. Background Art

[0002] With the continuous development of the power system and the advancement of its intelligence, the scale of power material demand continues to increase. Ensuring the stable operation of the power system and ensuring power supply security has become a key task. At present, most power warehouse material management is to monitor warehouse materials by predicting material inventory and material demand. Among them, material forecasting occupies an important position in supply chain management. Linear regression model, moving average method and exponential smoothing method and other methods based on historical data analysis are widely used in material forecasting. These methods improve the prediction accuracy by analyzing historical trends and selecting models with the smallest mean square error. However, due to some uncertainties in power materials in some special cases, for example, when using smoothing algorithms to predict inventory or demand, the smoothing coefficient is fixed once determined and cannot be changed. This will result in a decrease in the accuracy of long-term forecasts and cannot meet the monitoring requirements of some special periods or frequencies.

[0003] In view of this, a method, system, equipment and medium for monitoring materials in a power warehouse for user intelligent warehouse management are needed. Summary of the invention

[0004] The embodiments of the present application provide a method, system, device and medium for monitoring materials in a power warehouse managed by a user's intelligent warehouse, which are used to solve the problem of insufficient accuracy in long-term prediction of material monitoring.

[0005] A first aspect of an embodiment of the present application provides a method for monitoring materials in a power warehouse managed by a user's intelligent warehouse, comprising:

[0006] Obtaining an inventory set of materials in a power warehouse within a preset period, wherein the inventory set includes inventory within several time periods;

[0007] Calculate the predicted demand for power warehouse materials within the preset period based on grey correlation analysis;

[0008] Determine whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend;

[0009] If so, determine whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory in any time period within the preset cycle time is within a threshold range;

[0010] If so, the information entropy is used to update the predicted demand for materials in the power warehouse within the preset period, and the material inventory in the power warehouse is allocated according to the updated predicted demand;

[0011] If not, the material inventory of the power warehouse is allocated according to the predicted demand for materials in the power warehouse within the preset period of time.

[0012] Furthermore, the calculation of the predicted demand for power warehouse materials within the preset period based on grey correlation analysis includes:

[0013] The demand for power warehouse materials is set as the parent sequence, and the indicators in the indicator system that generally affect the changes in power warehouse materials are set as subsequences;

[0014] Calculate the correlation coefficients between different indicators in the indicator system and demand respectively;

[0015] Performing dimensionless processing on the parent sequence and the child sequence, and calculating the correlation coefficient and correlation degree between the parent sequence and the child sequence;

[0016] The various indicators in the indicator system are sorted from large to small according to the correlation degree to determine the corresponding correlation sequence.

[0017] Furthermore, the expression for calculating the correlation coefficient and correlation degree between the parent sequence and the child sequence is as follows:

[0018] Correlation coefficient δ ik :

[0019]

[0020] Where: min i min k Δ ik is the absolute difference Δ ik The minimum value, max i max k Δ ik is the absolute difference Δ ik The maximum value of , ρ is the resolution coefficient;

[0021] Correlation γ i :

[0022]

[0023] Where: n is the number of indicators in the indicator system.

[0024] Furthermore, the expression for calculating the predicted demand for power warehouse materials within the preset period of time based on grey correlation analysis is as follows:

[0025]

[0026] Where: L m is the predicted demand, m is the time period within the preset cycle time, p is the preset cycle time, λ j It is the grey correlation.

[0027] Furthermore, the determining whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend includes:

[0028] Calculate the proportion of the inventory quantity corresponding to the negative difference between the predicted demand quantity of the power warehouse materials within the preset cycle time and the inventory quantity in several time periods within the preset cycle time to the inventory quantity set;

[0029] If so, it is determined that the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend.

[0030] Furthermore, the method of updating the predicted demand for materials in the power warehouse within the preset period of time by using information entropy includes:

[0031] Calculate the information entropy value increment of the inventory in any time period, and determine the total information entropy increment according to the information entropy value increment;

[0032] The predicted demand for materials in the power warehouse within the preset period is updated based on the total increment of information entropy.

[0033] Furthermore, the expression for calculating the information entropy value increment of the inventory in any time period is as follows:

[0034]

[0035] Where: ΔH w is the information entropy increment, W is the holding loss caused by materials, x i is the inventory of materials, s is the forecast demand for materials, p i Purchasing price for materials

[0036] A second aspect of an embodiment of the present application provides a power warehouse material monitoring system for intelligent warehouse management, including:

[0037] An inventory quantity set acquisition unit, used to acquire an inventory quantity set of materials in a power warehouse within a preset period of time, wherein the inventory quantity set includes inventory quantities within a number of time periods;

[0038] A predicted demand calculation unit, used to calculate the predicted demand for power warehouse materials within the preset period based on grey correlation analysis;

[0039] The first difference judgment unit is used to judge whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend;

[0040] The second difference judgment unit is used to judge whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory quantity in several time periods within the preset cycle time is within a threshold range when the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory quantity in several time periods within the preset cycle time presents a decreasing trend;

[0041] A predicted demand updating unit, used to update the predicted demand of the power warehouse materials within the preset cycle time by using information entropy when the difference between the predicted demand of the power warehouse materials within the preset cycle time and the inventory in any time period within the preset cycle time is within a threshold range, and to allocate the inventory of materials in the power warehouse according to the updated predicted demand;

[0042] The material inventory allocation unit is used to allocate the material inventory of the power warehouse according to the predicted demand for materials in the power warehouse within the preset cycle time when the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in any time period within the preset cycle time is not within a threshold range.

[0043] A third aspect of the embodiments of the present application provides a computer device, including:

[0044] memories, transceivers, processors, and bus systems;

[0045] Wherein, the memory is used to store programs;

[0046] The processor is used to execute the program in the memory, including executing the power warehouse material monitoring method for intelligent warehouse management as described in any one of the above;

[0047] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0048] A fourth aspect of an embodiment of the present application provides a readable storage medium, including instructions, which, when executed on a computer, enables the computer to execute the power warehouse material monitoring method for intelligent warehouse management as described in any one of the above items.

[0049] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0050] This application uses the grey correlation analysis method to calculate the predicted demand for materials in the power warehouse within a certain period of time, and then first determines whether the difference between the predicted demand and the inventory in several time periods within the period shows a decreasing trend. If so, it is determined whether it is within the threshold range. If so, the information entropy is used to update the predicted demand. If not, the calculated demand is directly used to allocate inventory. In this application, the grey correlation degree is calculated to obtain the demand without the influencing factors of the emergency. When it is determined that the difference between the demand and the inventory is within the preset range, it means that an emergency has occurred in this time period. The information entropy is set to recalculate the predicted demand, and then the materials are allocated in a targeted manner. In this way, both the monitoring of materials in general situations and the allocation of materials in emergency special situations are considered, making the material monitoring more comprehensive and avoiding the situation where materials are in short supply in the event of an emergency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic flow chart of an embodiment of a method for monitoring materials in a power warehouse for intelligent warehouse management in the present invention;

[0052] Figure 2 It is a flow chart of another embodiment of a method for monitoring materials in a power warehouse for intelligent warehouse management in the present invention;

[0053] Figure 3 It is a structural block diagram of an embodiment of a power warehouse material monitoring system for intelligent warehouse management in the invention;

[0054] Figure 4 The figure is a structural block diagram of a computer device in the present invention. DETAILED DESCRIPTION

[0055] The embodiments of the present application provide a method, system, device and medium for monitoring materials in a power warehouse for intelligent warehouse management, which solves the problem of insufficient accuracy in long-term prediction of material monitoring.

[0056] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "corresponding to" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] Embodiment 1

[0058] The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and there is no specific limitation. The following will introduce the power warehouse material monitoring method for intelligent warehouse management in this application from the perspective of system implementation. Figure 1 , the method provided in the embodiment of the present application comprises the following steps:

[0059] S11. Obtaining a set of inventory quantities of materials in a power warehouse within a preset period of time, the inventory quantity set includes the inventory quantities within several time periods;

[0060] In this embodiment, the preset cycle time is to intercept a certain period of time, and the inventory within a certain time interval within this period of time is combined into an inventory set. For example, the inventory of materials in the power warehouse every day in September is recorded in set A, that is, set A = a1, a2, a3, ..., a n , where a1 is the inventory on the first day and continues until the last day of September.

[0061] S12. Calculate the predicted demand for power warehouse materials within a preset period of time based on grey correlation analysis;

[0062] Based on the above inventory set, the forecast demand for power warehouse materials in September is calculated using grey correlation analysis. Specifically, the indicators in the forecast evaluation index system set by grey correlation analysis are set as influencing factors under general circumstances, such as considering the cost and loss under general storage conditions. The grey correlation analysis method used here does not consider inventory changes and deployment changes caused by special circumstances.

[0063] S13. Determine whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory within a certain period of time within the preset cycle time shows a decreasing trend;

[0064] In this embodiment, determining whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory within a plurality of time periods within the preset cycle time presents a decreasing trend further includes the following steps:

[0065] 1. Calculate the proportion of the inventory volume corresponding to the negative difference between the predicted demand volume of power warehouse materials within the preset cycle time and the inventory volume in several time periods within the preset cycle time to the inventory volume set;

[0066] 2. If yes, it is determined that the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time shows a decreasing trend.

[0067] Specifically, if the difference between the predicted demand and the inventory is negative, it means that the demand is higher than the inventory, resulting in a situation of supply exceeding demand. During the preset period of time, the proportion of the difference between the predicted demand and the inventory that is negative exceeds the set proportion. For example, if the set proportion is 50%, then if the calculated proportion exceeds 50%, it means that an emergency occurred during the intercepted period, resulting in a long period of supply exceeding demand. Here, the relationship between demand and inventory is determined by setting the proportion of negative differences, so as to obtain the approximate proportion of supply exceeding demand. In this way, the probability of an emergency event of supply exceeding demand during this period can be inferred, which can provide an effective data basis for the subsequent allocation of materials.

[0068] S14. When the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory in several time periods within the preset cycle time shows a decreasing trend, it is determined whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory in any time period within the preset cycle time is within the threshold range;

[0069] S15. When the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory in any time period within the preset cycle time is within the threshold range, the predicted demand for power warehouse materials within the preset cycle time is updated using information entropy, and the inventory of materials in the power warehouse is allocated according to the updated predicted demand;

[0070] S16. When the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in any time period within the preset cycle time is not within the threshold range, the inventory of materials in the power warehouse is adjusted according to the predicted demand for materials in the power warehouse within the preset cycle time.

[0071] In this embodiment, when the difference between the predicted demand and inventory of the power warehouse materials in a captured period of time shows a decreasing trend, it means that the demand is increasing as the inventory decreases during this period of time. However, it is impossible to judge whether a sudden shortage of supply occurs during this period of time based on the presented delivery trend. Therefore, it is necessary to judge whether the difference between the predicted demand for power warehouse materials and the inventory at any time during this period of time is within the threshold range on this basis. The threshold range here is the threshold for setting whether an emergency event occurs at any time. For example, in a week in September, it is calculated that the region urgently needs the power warehouse to provide materials exceeding a certain inventory, and the gray correlation analysis method does not take into account the influencing factors of the emergency. Therefore, when the calculated difference is within the threshold range, the information entropy method is needed to re-predict the demand, and the information entropy method is used to calculate the demand, which takes into account the demand in an emergency situation, and then the inventory is allocated according to the predicted demand in an emergency situation.

[0072] Specifically, the information entropy is used to update the predicted demand for power warehouse materials within a preset period of time, including:

[0073] 1. Calculate the information entropy increment of the inventory in any time period, and determine the total information entropy increment based on the information entropy increment;

[0074] The expression for calculating the information entropy increment of inventory in any time period is as follows:

[0075]

[0076] Where: ΔH w is the information entropy increment, W is the holding loss caused by materials, x i is the inventory of materials, s is the forecast demand for materials, p i The purchase price of materials.

[0077] 2. Update the predicted demand for power warehouse materials within the preset period based on the total increment of information entropy.

[0078] The total increase in information entropy is calculated based on the increase in information entropy when the inventory is greater than the demand and when the inventory is less than the demand.

[0079] Finally, when the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in any time period within the preset cycle time is not within the threshold range, it means that although the difference between the demand and the inventory shows a decreasing trend, there is no event that causes supply shortage or oversupply during this period, and the inventory of materials in the power warehouse is then allocated according to the predicted demand for materials in the power warehouse within the preset cycle time.

[0080] The above embodiment calculates the demand without the influencing factors of emergency through grey correlation. When it is determined that the difference between the demand and the inventory is within the preset range, it means that an emergency occurs in this time period. Then the information entropy is set to recalculate the predicted demand, and then make targeted material allocation. In this way, both the monitoring of materials in general situations and the allocation of materials in emergency special situations are considered, making material monitoring more comprehensive and avoiding the situation where the supply of materials is insufficient to meet the demand in an emergency.

[0081] Embodiment 2

[0082] See also Figure 2 , the calculation of the predicted demand for power warehouse materials within a preset period of time based on grey correlation analysis also includes the following steps:

[0083] S121. The demand for power warehouse materials is set as a parent sequence, and the indicators in the indicator system that generally affect the change of power warehouse materials are set as subsequences;

[0084] 1. Select the parent sequence and set the demand for power warehouse materials as the parent sequence. The expression is as follows:

[0085] X0={x 01 ,x 02 ,...,x 0n}

[0086] 2. Select a word sequence and set the indicators in the indicator system that generally affects the changes in materials in the power warehouse as a subsequence. The expression is as follows:

[0087] X i ={x i1 ,x i2 ,...,x in}

[0088] S122. Calculate the correlation coefficients between different indicators in the indicator system and demand respectively;

[0089] Specifically, the correlation coefficients between different indicators and material demand in the distribution calculation indicator system need to be deleted from the word sequence because of their weak correlation with material demand.

[0090] S123. Perform dimensionless processing on the parent sequence and the child sequence, and calculate the correlation coefficient and correlation degree between the parent sequence and the child sequence;

[0091] In this embodiment, there are many ways to perform dimensionless processing on the sequence, and the subsequent correlation sequence also has certain differences. Therefore, after comparing the discrete degree of the correlation degree, the dimension processing methods such as initialization, averaging, centering and extremization are selected.

[0092] After dimensionless processing, the correlation coefficient and correlation degree of the parent sequence and the child sequence are calculated. The calculation formulas are as follows:

[0093] Correlation coefficient δ ik :

[0094]

[0095] Where: min i min k Δ ik is the absolute difference Δ ik The minimum value, max i max k Δ ik is the absolute difference Δ ik The maximum value of , ρ is the resolution coefficient;

[0096] Correlation γ i :

[0097]

[0098] Where: n is the number of indicators in the indicator system.

[0099] S124. Sort the indicators in the indicator system from large to small according to the degree of association, and determine the corresponding association sequence.

[0100] In this embodiment, after obtaining the correlation sequence of each influencing indicator, the first several indicators are selected from the sorted indicator set, and the principal component analysis is used for dimensionality reduction. At the same time, the functional relationship between the indicator set and the demand is fitted, and the accuracy of the detection set is analyzed to meet the corresponding requirements. After dimensionality reduction, the gray model g of the material forecast demand i The expression is as follows:

[0101]

[0102] Combined with the above dynamic correlation analysis of material demand, the purpose of final demand forecast is achieved, namely:

[0103]

[0104] Where: L m is the predicted demand, m is the time period within the preset cycle time, p is the preset cycle time, λ j It is the grey correlation.

[0105] The above embodiment uses the improved grey correlation analysis method to predict the demand of the power warehouse under normal circumstances, thereby effectively improving the accuracy of the demand prediction.

[0106] Embodiment 3

[0107] See also Figure 3 , an embodiment of the power warehouse material monitoring method for intelligent warehouse management in the present invention comprises the following steps:

[0108] An inventory quantity set acquisition unit is used to acquire an inventory quantity set of materials in a power warehouse within a preset period of time, and the inventory quantity set includes the inventory quantity within several time periods;

[0109] A forecast demand calculation unit, used to calculate the forecast demand for power warehouse materials within a preset period of time based on grey correlation analysis;

[0110] The first difference judgment unit is used to judge whether the difference between the predicted demand for materials in the power warehouse within a preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend;

[0111] The second difference judgment unit is used to judge whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory quantity in a certain time period within the preset cycle time is within a threshold range when the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory quantity in a certain time period within the preset cycle time presents a decreasing trend;

[0112] A forecast demand updating unit, used to update the forecast demand of the power warehouse materials within the preset cycle time by using information entropy when the difference between the forecast demand of the power warehouse materials within the preset cycle time and the inventory quantity in any time period within the preset cycle time is within a threshold range, and to allocate the inventory quantity of materials in the power warehouse according to the updated forecast demand;

[0113] The material inventory allocation unit is used to allocate the material inventory of the power warehouse according to the predicted demand for materials in the power warehouse within the preset cycle time when the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in any time period within the preset cycle time is not within a threshold range.

[0114] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0115] Embodiment 4

[0116] See also Figure 4 The present invention provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0117] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0118] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0120] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for monitoring materials in a power warehouse for intelligent warehouse management, characterized in that: include: Obtaining an inventory set of materials in a power warehouse within a preset period of time, wherein the inventory set includes inventory within several time periods; Calculate the predicted demand for power warehouse materials within the preset period based on grey correlation analysis; Determine whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend; If so, determine whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory in any time period within the preset cycle time is within a threshold range; If so, the information entropy is used to update the predicted demand for materials in the power warehouse within the preset period, and the material inventory in the power warehouse is allocated according to the updated predicted demand; If not, the material inventory of the power warehouse is allocated according to the predicted demand for materials in the power warehouse within the preset period of time.

2. The method for monitoring materials in a power warehouse for intelligent warehouse management according to claim 1, characterized in that: The method of calculating the predicted demand for materials in the power warehouse within the preset period of time based on grey correlation analysis includes: The demand for power warehouse materials is set as the parent sequence, and the indicators in the indicator system that generally affect the changes in power warehouse materials are set as subsequences; Calculate the correlation coefficients between different indicators in the indicator system and demand respectively; Performing dimensionless processing on the parent sequence and the child sequence, and calculating the correlation coefficient and correlation degree between the parent sequence and the child sequence; The various indicators in the indicator system are sorted from large to small according to the correlation degree to determine the corresponding correlation sequence.

3. The power warehouse material monitoring method for intelligent warehouse management according to claim 2 is characterized in that: The expression for calculating the correlation coefficient and correlation degree of the parent sequence and the child sequence is as follows: Correlation coefficient δ ik : Where: min i min k Δ ik is the absolute difference Δ ik The minimum value, max i max k Δ ik is the absolute difference Δ ik The maximum value of , ρ is the resolution coefficient; Correlation γ i : Where: n is the number of indicators in the indicator system.

4. The method for monitoring materials in a power warehouse for intelligent warehouse management according to claim 3, characterized in that: The expression for calculating the predicted demand for power warehouse materials within the preset period of time based on grey correlation analysis is as follows: Where: L m is the predicted demand, m is the time period within the preset cycle time, p is the preset cycle time, λ j It is the grey correlation.

5. The method for monitoring materials in a power warehouse for intelligent warehouse management according to claim 1, characterized in that: The determining whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend includes: Calculate the proportion of the inventory quantity corresponding to the negative difference between the predicted demand quantity of the power warehouse materials within the preset cycle time and the inventory quantity in several time periods within the preset cycle time to the inventory quantity set; If so, it is determined that the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend.

6. The method for monitoring materials in a power warehouse for intelligent warehouse management according to claim 1, characterized in that: The method of updating the predicted demand for power warehouse materials within the preset period of time by using information entropy includes: Calculate the information entropy value increment of the inventory in any time period, and determine the total information entropy increment according to the information entropy value increment; The predicted demand for materials in the power warehouse within the preset period is updated based on the total increment of information entropy.

7. The method for monitoring materials in a power warehouse for intelligent warehouse management according to claim 6, characterized in that: The expression for calculating the information entropy value increment of the inventory in any time period is as follows: Where: ΔH w is the information entropy increment, W is the holding loss caused by materials, x i is the inventory of materials, s is the forecast demand for materials, p i The purchase price of materials.

8. A power warehouse material monitoring system for intelligent warehouse management, characterized in that: include: An inventory quantity set acquisition unit, used to acquire an inventory quantity set of materials in a power warehouse within a preset period of time, wherein the inventory quantity set includes inventory quantities within a number of time periods; A predicted demand calculation unit, used to calculate the predicted demand for power warehouse materials within the preset period based on grey correlation analysis; The first difference judgment unit is used to judge whether the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in several time periods within the preset cycle time presents a decreasing trend; The second difference judgment unit is used to judge whether the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory quantity in several time periods within the preset cycle time is within a threshold range when the difference between the predicted demand for power warehouse materials within the preset cycle time and the inventory quantity in several time periods within the preset cycle time presents a decreasing trend; A predicted demand updating unit, used to update the predicted demand of the power warehouse materials within the preset cycle time by using information entropy when the difference between the predicted demand of the power warehouse materials within the preset cycle time and the inventory in any time period within the preset cycle time is within a threshold range, and to allocate the inventory of materials in the power warehouse according to the updated predicted demand; The material inventory allocation unit is used to allocate the material inventory of the power warehouse according to the predicted demand for materials in the power warehouse within the preset cycle time when the difference between the predicted demand for materials in the power warehouse within the preset cycle time and the inventory in any time period within the preset cycle time is not within a threshold range.

9. A computer device, characterized in that: include: memories, transceivers, processors, and bus systems; Wherein, the memory is used to store programs; The processor is used to execute the program in the memory, including executing the power warehouse material monitoring method for intelligent warehouse management according to any one of claims 1 to 7; The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

10. A readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, enable the computer to execute the power warehouse material monitoring method for intelligent warehouse management as claimed in any one of claims 1 to 7.

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