Material intelligent allocation system and method for hydropower basin maintenance

Abstract

The invention provides an intelligent material allocation system and method for hydropower basin maintenance, and relates to the technical field of intelligent material allocation. Equipment gathering points in a hydropower basin and material types required by the equipment gathering points are recorded, a storage environment label corresponding to each material is set, and position information of each equipment gathering point is acquired; and based on the equipment aggregation points and the material types, analyzing the consumption condition of each equipment aggregation point for each type of materials, counting the consumption rate of each equipment aggregation point for each type of materials, and determining the demand degree of each equipment aggregation point for each type of materials according to the consumption rate. Through cooperation of the structures, compared with an existing method, the method has the following beneficial effects that firstly, all the modules are closely connected, and the manual intervention link is reduced; secondly, the material storage is ensured to conform to the environmental constraint and be close to the demand concentration area; and thirdly, inventory is monitored in real time, supplement is prompted in time, and the maintenance progress is prevented from being influenced by material shortage.

Description

Technical Field

[0001] This invention relates to the field of intelligent material allocation technology, and in particular to an intelligent material allocation system and method for hydroelectric field maintenance. Background Technology

[0002] In the field of maintenance material management in hydroelectric basins, the traditional management model is based on manual operation. The specific implementation method is as follows: manual traversal of equipment clusters with wide coverage and scattered distribution within the basin, such as power plants and power consumption plants, where maintenance-related equipment is concentrated, to collect statistics on the demand for various maintenance materials such as cables, seals, and testing instruments; planning material transportation routes based on manual experience and determining the location of material storage points based on experience; and using regular manual inventory to monitor inventory quantities, and then judging whether to replenish materials based on the inventory results, thereby supporting the maintenance work in hydroelectric basins.

[0003] However, this traditional model has significant drawbacks: First, the efficiency of material allocation is low. Due to the wide coverage of the hydropower field and the scattered locations of equipment, manual statistics on demand are easily affected by information transmission delays and record omissions, leading to deviations in the judgment of material demand at each gathering point. Moreover, the transportation route lacks systematic analysis, often resulting in repeated transportation or detours, which not only consumes a lot of manpower and time costs but may also delay the maintenance progress. Secondly, the storage of materials lacks scientific methods. Traditional management methods often ignore the specific requirements of different materials for storage environment. For example, materials that require special storage conditions such as moisture protection, heat preservation, and light protection are mixed with ordinary materials, which leads to the deterioration or loss of materials due to incompatibility with the environment. At the same time, the location of storage points is mostly based on historical experience or the principle of simplicity and convenience, and fails to be optimized in combination with the actual material consumption distribution of each equipment gathering point. This results in some warehouses being located far from high demand areas, increasing the distance and difficulty of material retrieval and secondary transportation. Third, the stability of material supply is difficult to guarantee. Regular manual inventory cannot keep track of material consumption dynamics in a timely manner. When the consumption rate of materials exceeds expectations, shortages are likely to occur. Excessive stockpiling to avoid shortages will lead to idle resources and increased storage costs. This supply and demand imbalance is more prominent when maintenance tasks are concentrated, which seriously affects the continuity of maintenance work.

[0004] In summary, the existing hydroelectric power field maintenance material management model, which is based on manual records and experience-based judgment, suffers from low material allocation efficiency, insufficient scientific storage, and poor supply stability due to the lack of precise quantitative analysis of material demand, systematic optimization design of material storage locations, and real-time dynamic monitoring of inventory. It is difficult to meet the high-efficiency, accurate, and continuous material management requirements of maintenance work in the complex environment of hydroelectric power fields. This technical problem needs to be solved. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is to provide an intelligent material allocation system and method for hydroelectric power field maintenance. This system can accurately quantify the material demand at each equipment gathering point, optimize the storage location by combining material characteristics with the distribution of gathering points, and monitor inventory in real time to trigger replenishment prompts. This achieves intelligent management of the entire process of intelligent inventory early warning and scheduling, solving the problems of low material allocation efficiency, insufficient scientific storage, and poor supply stability under the traditional manual management model.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The present invention provides an intelligent material allocation system and method for hydroelectric power field maintenance, comprising the following steps: S1. Record the equipment clusters and the types of materials required by each equipment cluster within the hydroelectric current domain. Set storage environment tags for each type of material and obtain the location information of each equipment cluster. S2. Based on the equipment clusters and material types recorded in step S1, analyze the consumption of each type of material by each equipment cluster, calculate the consumption rate of each type of material by each equipment cluster, and determine the demand for each type of material by each equipment cluster based on the consumption rate. S3. Based on the demand for each type of material by each equipment cluster determined in step S2, and combined with the location information of each equipment cluster obtained in step S1, first select the initial storage locations for each type of material. S1. Based on the demand of each type of material at each equipment aggregation point, the initial storage points are optimized to obtain the preferred storage points for each type of material; S2. Based on the storage environment tags of various materials set in step S1, the preferred storage points of all materials obtained in step S3 are adjusted to obtain temporary storage points; S3. Based on the preset minimum distance constraints, the relative positions of the temporary storage points obtained in step S4 are corrected to determine the warehouse establishment point; S4. The inventory of materials in the warehouse establishment point determined in step S5 is monitored in real time, and replenishment prompts are generated based on the demand of each type of material at each equipment aggregation point obtained in step S2.

[0007] In the preferred embodiment, step S1 specifically includes the following steps: S11. After authorization, execute the equipment cluster point identification and counting algorithm, traverse all areas where equipment is deployed within the hydroelectric current domain, mark them as valid equipment cluster points, and form a set of equipment cluster points. ,in For the i-th valid equipment cluster point, count to obtain the number of equipment cluster points P, where P is a positive integer and P≥1. Equipment cluster points include power plants and power consumption plants. S12. Execute the material type statistics and mutual exclusion label judgment algorithm, collect the historical maintenance material list and real-time demand list of each equipment aggregation point, remove duplicates and classify the materials in the list to form a material type set. ,in For the j-th type of maintenance materials, count to obtain the total number of material types Q, where Q is a positive integer and Q≥1; For Category j materials Set storage environment tag set If there are two types of supplies , Its tag set satisfies Then determine For mutually exclusive storage environment tag groups; in, This is the set of storage environment tags for category j1 materials. This is the set of storage environment tags for category j2 materials. For set intersection operators, It is an empty set; S13. Execute the BeiDou coordinate transformation algorithm to establish a spatial coordinate system; The i-th device aggregation point is collected based on the BeiDou positioning system. latitude and longitude The algorithm for converting latitude and longitude to Cartesian coordinates is used to output the i-th device aggregation point from the BeiDou positioning system. latitude and longitude Convert to Cartesian coordinates ,in .

[0008] In the preferred embodiment, step S2 specifically includes the following steps: S21. Execute the consumption rate statistics algorithm for the i-th device aggregation point determined in step S1. Set a preset monitoring cycle ; Collect the i-th device aggregation point within the monitoring period For Category j materials initial inventory Ending inventory And recorded in the monitoring cycle. The quantity of supplies transported to this gathering point Calculate the actual consumption quantity , obtain consumption rate ; in, And consumption rate In order to monitor within the preset period The i-th device aggregation point For Category j materials The amount consumed; S22. Execute the demand determination algorithm and preset the consumption rate threshold. ; like Then determine the i-th device aggregation point. For the j-th type of maintenance materials Demand This indicates there is no urgent need for allocation; if Then determine the i-th device aggregation point. For the j-th type of maintenance materials Demand This indicates an urgent need for allocation.

[0009] In the preferred embodiment, step S3 specifically includes the following steps: S31. Execute the data retrieval algorithm for the j-th type of materials. Call the P device aggregation points obtained in step S2 For the j-th type of maintenance materials Demand set and the P device aggregation points obtained in step S1 Location coordinate set ; S32. Execute the preliminary storage point selection algorithm. The preliminary storage point selection algorithm uses a distance-sum minimization algorithm, based on the set of location coordinates. Filter out all device aggregation points Construct the objective function based on the distance and the shortest position. The positions that satisfy the objective function are calculated through iterative traversal and used as the initial storage point coordinates for the j-th type of material. ;in, for arrive The straight-line distance; S33. Execute the optimal storage point optimization algorithm: based on the demand set. For the initial storage point Iterative optimization was performed, adjusting the location to favor high-demand clusters, to obtain the optimal storage location coordinates for the j-th type of material. .

[0010] In the preferred embodiment, step S33 is as follows: S331. Execute the optimization parameter calculation algorithm, based on the i-th device aggregation point. Demand for Category j goods Determine the initial storage location The optimization direction and optimization distance; Calculate the initial storage location To the equipment gathering point initial distance ; The optimization direction is determined to start from the initial storage point. Pointing to the i-th device aggregation point Position coordinates The unit direction vector, using the unit direction vector express; in, , This is the coordinate difference. This is the initial distance; The optimal distance is determined as follows , for The ratio of the demand for type j materials to the sum of the demand for type j materials at P equipment aggregation points is used to calculate the proportion of demand. ; in, For P device aggregation points For the j-th type of maintenance materials The total demand is calculated; if the total is 0, no optimization is needed, and the resource storage point can be deleted directly. for arrive The straight-line distance; S332, Execute the iterative optimization algorithm, and Substituting the above calculations for optimization direction and optimization distance, the initial storage point is then... Perform E iterations of updates, with each iteration based on the i-th device aggregation point. The demand weights are updated to store the coordinates of the storage point. In the i-th iteration, the coordinates are based on the unit direction vector. and optimized distance Update coordinates to ; in, These are the coordinates of the storage point after a single iteration. This represents the distance moved in a single iteration. They are respectively The x and y components; After E iterations, the optimal storage location coordinates for the j-th type of material are finally obtained. ; in, , The coordinates are after the Eth iteration.

[0011] In the preferred embodiment, step S4 specifically includes the following steps: S41. Execute the algorithm for generating the optimal storage point set, and Substituting these values ​​into step S3, we obtain the set of optimal storage location coordinates for type Q materials. ; S42. Execute the storage point distance monitoring algorithm and preset the storage point distance judgment threshold. ; Calculate any two types of materials , The distance between the preferred storage points for the two types of materials , The distance between the preferred storage points is calculated using the Euclidean distance formula, as follows: ; like Then, the storage environment label for the j1 type of material set in step S1 is invoked. Storage environment label for Category J2 materials ; S43. Execute the storage point classification adjustment algorithm, if and There are mutually exclusive storage environment tag groups, i.e. Then the j1 type of materials The temporary storage point is set as its preferred storage point. Category J2 materials The temporary storage point is set as its preferred storage point. (Avoid storing incompatible materials together); like and Non-exclusive label groups, i.e. Then the j1 type of materials With Category J2 materials They belong to the same storage point, and the coordinates of the same storage point are: and The formula for calculating the midpoint coordinates of the same storage point is: ; in, These are the coordinates of the common storage point for materials of type j1 and type j2, respectively.

[0012] In the preferred embodiment, step S5 specifically includes the following steps: S51. Execute the temporary storage point recording algorithm, record the N temporary storage points output in step S4, where N is a positive integer and N≥1, and the set of location coordinates of the N temporary storage points. ,in, Let Z be the coordinates of the z-th temporary storage point; S52. Set the minimum radius R for the preset storage point; Execute the storage point distance judgment algorithm based on the z1th temporary storage point. Iterate through the z2th temporary storage point and calculate the distance between them. ; like If the position of the z1th temporary storage point is corrected, then the position of the z1th temporary storage point will be corrected. like Then select the position coordinates of the z1th temporary storage point. As a warehouse establishment point; in, ; And z2≠z1.

[0013] In the preferred embodiment, in step S52, if Then, the storage point location correction algorithm is executed, and the specific steps are as follows: S521. Determine the preferred storage location for the materials corresponding to the z1th temporary storage location. The preferred storage point for materials corresponding to the z2th temporary storage point ; S522, Calculate the distance from the z1th temporary storage point to... unit correction vector ; in, Let z1 be the distance from the z1th temporary storage point to the preferred storage point for the corresponding material; S523, Set the preset correction distance K, and ; S524. Move the z1th temporary storage point along the unit correction vector. Move the preset correction distance K, and move the z2th temporary storage point along the unit correction vector. Move the coordinates by the preset correction distance K to obtain the corrected coordinates. The warehouse establishment point for the z1th temporary storage point is determined based on the corrected coordinates; where, They are respectively The x and y components.

[0014] In the preferred embodiment, step S6 specifically includes the following steps: S61. Execute the real-time inventory monitoring algorithm. After the warehouse establishment points determined in step S5 are completed, collect the inventory quantity of the j-th type of material in each warehouse establishment point in real time. ; S62. Preset the replenishment threshold for the j-th type of material. The formula for calculating the replenishment threshold of the j-th type of material is: ; in, For the P device aggregation points output in step S2 The total demand for category j goods This is the preset minimum quantity of materials to be used over a given period. like If this occurs, a supplementary notification message will be generated and sent to the administrator, prompting them to replenish material type j; the supplementary notification message includes the material category. Current inventory And the quantity to be replenished .

[0015] In a preferred embodiment, the present invention also provides an intelligent material allocation system for hydroelectric current domain maintenance, characterized in that the system is applied to the intelligent material allocation method for hydroelectric current domain maintenance described in any one of the above embodiments, and the system includes a basic data acquisition module, a material demand analysis module, a storage point location analysis module, a storage point location adjustment module, a storage point location correction module, and an inventory monitoring and alarm module. The basic data acquisition module is used to record the equipment aggregation points and the types of materials required for the equipment aggregation points within the hydroelectric current domain, set the storage environment label corresponding to each type of material, and obtain the location information of each equipment aggregation point. The material demand analysis module receives the equipment aggregation points, material types and location information output by the basic data acquisition module, analyzes the consumption of various materials at each equipment aggregation point, calculates the consumption rate and determines the demand level. The storage location analysis module receives demand data from the material demand analysis module and location information from the basic data acquisition module, filters preliminary storage locations for each type of material, and optimizes them to obtain the preferred storage locations. The storage location adjustment module receives all the preferred storage locations for materials output by the storage location analysis module, and adjusts the preferred storage location locations by combining them with the storage environment tags output by the basic data acquisition module. The storage point location correction module receives the adjusted storage point location output by the storage point location adjustment module, corrects the location based on the relative distance between each storage point, and determines the warehouse establishment point. The inventory monitoring and alarm module is used to monitor the quantity of materials in the warehouse in real time after the warehouse establishment point is determined by the storage point location correction module and the warehouse construction is completed. When the quantity of materials is lower than the preset threshold, the module will prompt the management personnel to replenish the materials.

[0016] This invention provides an intelligent material allocation system and method for hydroelectric power field maintenance. Through the coordination of the above-mentioned structures, compared with existing methods, it has the following advantages: First, the modules are closely connected, forming a closed loop from basic information collection to final inventory monitoring, reducing manual intervention. By analyzing the needs of equipment aggregation points and the characteristics of materials, the system automatically optimizes storage locations, avoids blind allocation, shortens material transportation routes, reduces labor and time costs, and makes the supply of maintenance materials more efficient. Secondly, by combining the requirements of the material storage environment with the distribution of aggregation points, multiple rounds of location optimization and adjustment were conducted to ensure that the storage of materials not only meets environmental constraints but is also close to areas of concentrated demand. Incompatible materials are stored separately to reduce losses, while materials with similar needs are grouped together for easier management, thus improving the overall scientific and practical nature of the storage. Third, real-time monitoring of inventory and timely replenishment alerts are crucial to prevent maintenance progress from being affected by material shortages. Inventory thresholds are dynamically adjusted based on demand to prevent excessive stockpiling that ties up resources, while ensuring sufficient supplies at critical moments, thus providing reliable material support for the continuous maintenance of hydroelectric power grids. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of an intelligent material allocation system for hydroelectric field maintenance according to the present invention; Figure 2 This is a flowchart of a method for intelligent material allocation for hydroelectric field maintenance according to the present invention. Detailed Implementation

[0018] To better understand the purpose, system architecture, and functional implementation of this embodiment, the embodiments and features in the embodiments of this application can be combined with each other without conflict. The exemplary embodiments disclosed in this application will be described below with reference to the accompanying drawings, including specific technical details disclosed in this embodiment to aid understanding; however, these details should be considered exemplary rather than restrictive. Therefore, those skilled in the art should understand that various improvements and adjustments can be made to the embodiments described herein without departing from the scope and core ideas of the invention. Similarly, for clarity, the following description omits well-known technologies, functions, and structures (such as the standard construction process of ordinary warehouses, the basic satellite signal receiving principle of the BeiDou positioning system, and the general data transmission protocol of IoT sensors).

[0019] In the field of hydroelectric power plant maintenance technology, the timeliness and rationality of material allocation directly determine the efficiency, quality, and cost of maintenance work, and are a core link in ensuring the stable operation of equipment clusters such as power plants and power consuming plants within the hydroelectric power plant area. In the traditional hydroelectric power plant maintenance material management model, the statistics of material demand, the selection of storage locations, and the replenishment of inventory all rely heavily on manual records and experience-based judgment. Material allocation is completed by manually checking the material consumption ledgers of each equipment cluster to determine demand, selecting warehouse locations based on the experience of management personnel, and monitoring inventory through regular inventory checks (such as once a month).

[0020] As the coverage of hydroelectric basins expands and the complexity of equipment increases, equipment aggregation points exhibit characteristics of "dispersed distribution and diverse needs": on the one hand, aggregation points may be scattered across different areas along the basin, with distances reaching tens of kilometers; on the other hand, the types of materials required for maintenance are numerous, and the storage environment requirements for various materials differ significantly. Some existing technologies attempt to introduce basic positioning tools (such as GPS) to assist in the selection of storage points, or monitor inventory through simple weighing sensors, but these are mostly limited to optimizing a single aspect. For example, they only calculate the "nearest" storage point based on the spatial location of the aggregation point, without considering the urgency of the actual demand for materials at each aggregation point; or they only collect inventory quantities through sensors, without dynamically setting replenishment thresholds in conjunction with demand, resulting in problems such as "redundant transportation routes, mixed storage losses, and imbalance between supply and demand of inventory" in material allocation, making it difficult to adapt to the maintenance needs of complex basins.

[0021] The existing technologies used in the allocation of maintenance materials in hydroelectric power fields have significant limitations: First, the demand assessment is too simplistic, only calculating the total consumption within a given period without converting this "total consumption" into "consumption rate" to quantify demand, resulting in a lack of precise demand guidance for storage point optimization. Second, the storage point location selection is one-sided, considering only spatial distance without considering the compatibility of the materials' storage environment labels, which can easily lead to material performance failure. Third, inventory management is lagging, relying on periodic inventory checks rather than real-time monitoring, and replenishment thresholds are mostly fixed values, failing to correlate with the dynamic total demand at various aggregation points, easily leading to supply and demand imbalances such as "shortages during peak maintenance periods and stockpiling during low-demand periods." Therefore, these technologies struggle to simultaneously achieve the multi-dimensional allocation goals of "precise demand, scientific storage, and dynamic inventory," and cannot fully adapt to the application scenarios of dispersed equipment, diverse material types, and continuous maintenance tasks in hydroelectric power fields. A systematic intelligent allocation solution is urgently needed to overcome these limitations.

[0022] Example 1 like Figure 2 As shown, a method for intelligent material allocation for maintenance in a hydroelectric current domain is characterized by the following steps: S1, recording the equipment aggregation points and the types of materials required by the equipment aggregation points within the hydroelectric current domain, setting storage environment tags for each type of material, and obtaining the location information of each equipment aggregation point. The purpose is to comprehensively and accurately obtain information on all equipment aggregation points within the hydroelectric current domain, the types of materials required, and their storage environment requirements, and to assign precise spatial coordinates to each equipment aggregation point. Step S1 specifically includes the following steps: S11. After authorization, execute the equipment cluster point identification and counting algorithm, traversing all areas where equipment is deployed within the hydroelectric current basin. The algorithm first accesses the basin's equipment management system database to obtain information on all registered equipment installation locations. Second, it verifies and supplements the database information by combining Geographic Information System (GIS) data with on-site inspection records to ensure no omissions. Finally, it marks all valid equipment concentration areas as valid equipment cluster points, forming a set of equipment cluster points. The number of equipment clusters, P, is obtained by counting, where P is a positive integer and P≥1. Equipment clusters include power plants and power-consuming plants, i.e. ; in For the i-th valid device aggregation point, This is the element counting operator for a set. Specifically, by quantifying the total number of effective equipment aggregation points within the watershed, a basic quantitative basis is provided for subsequent demand analysis and storage point optimization, ensuring coverage of all core demand areas.

[0023] In one feasible case, within the maintenance management system of the Yangtze River hydroelectric basin, after authorization, the system calls the equipment management API interface to obtain 15 registered densely populated equipment areas within the basin. Subsequently, the system compares this list with the latest GIS geofencing data and inspection work order records from the past three months. It discovers that a newly marked temporary flood control pumping station has been added to the GIS data, while an old, abandoned substation is listed in the inspection records. After confirmation, the flood control pumping station is added to the set, and the abandoned substation is removed. Ultimately, the number of valid equipment clusters, P = 14, is determined, including 8 hydroelectric powerhouses and 6 major substations.

[0024] S12. Execute the material type statistics and mutually exclusive label judgment algorithm to collect the historical maintenance material list of all materials involved in the maintenance records of each equipment cluster point over the past 3 years. Collect the materials for the current maintenance tasks to be pre-declared at the cluster point, i.e., the real-time demand list. Merge the "historical material pool" and "real-time demand pool" of all cluster points, and remove duplicates by comparing dimensions such as "material name, model, and function". Then classify the materials according to "functional attributes (such as electrical, mechanical, and sealing)" or "maintenance scenario (such as generator maintenance and transmission line maintenance)" to form a material type set. By performing the "number of elements" operation on the set, the total number of material types Q is obtained, where Q is a positive integer and Q≥1. ; in For the j-th category of maintenance materials after deduplication and classification, This is the element counting operator for a set. Specifically, the total categories of materials required for watershed maintenance should be clearly defined to avoid overlooking critical materials during subsequent storage, allocation, and analysis. Subsequently, the j-th category of materials... Set storage environment tag set If there are two types of supplies , Its tag set satisfies Then determine For mutually exclusive storage environment tag groups; among them, This is the set of storage environment tags for category j1 materials. This is the set of storage environment tags for category j2 materials. For set intersection operators, It is an empty set; Specifically, the above method can accurately identify combinations of materials that cannot be stored together, and avoid material losses caused by conflicting storage environments in advance, such as storing materials labeled "wet" together with materials labeled "dry".

[0025] In one feasible case, the statistics of material types are as follows: collection A historical list of power plant clusters in the city: "High-voltage insulating bushings ( Thermal grease ( )";collection Historical list of power plant clusters along the river in the urban area: "Waterproof sealing rings ( "Rust-proof paint ( Real-time list: "High-voltage insulating bushings ( )”; After merging and deduplication, the material categories are grouped as follows: Substituting into the formula, we get .

[0026] Storage tags and mutual exclusion judgment: For (High-voltage insulating bushing), the database indicates that it needs to be "dried (relative humidity)". ), avoid light (light intensity) Storage is set to "lux" (lux), hence the setting. ; right (Waterproof sealing ring), the database indicates that it needs to be "humid (relative humidity)". ), normal temperature ( Storage, therefore set ; Calculate the intersection: Then "dry" and "damp" completely conflict and have no common label, therefore it is determined that... For mutually exclusive storage environment tag groups, if and Mixed storage, or It fails due to moisture, or Because they harden due to drying, they need to be stored separately.

[0027] S13. This step aims to assign precise coordinate information for each device aggregation point, which can be used for spatial analysis and distance calculation.

[0028] First, a Cartesian coordinate system suitable for this hydroelectric current domain needs to be established. Then, based on the positioning service of the BeiDou Navigation Satellite System (BDS), the aggregation points of each device are collected. Geodetic coordinates, i.e., longitude and latitude Since latitude and longitude are spherical coordinates, directly using them to calculate straight-line distances would be very complex and unintuitive. Therefore, to simplify the optimization calculations regarding distance and location in subsequent steps S3 and S5, the geographic coordinates (latitude and longitude) obtained through the BeiDou system need to be mathematically transformed and mapped to a Cartesian coordinate system, thereby obtaining the planar coordinates of each aggregation point. Its unit is usually meters, which makes it easy to calculate directly using the Euclidean distance formula; The i-th device aggregation point is collected based on the BeiDou positioning system. latitude and longitude The algorithm for converting latitude and longitude to Cartesian coordinates is used to output the i-th device aggregation point from the BeiDou positioning system. latitude and longitude Convert to Cartesian coordinates ,in .

[0029] The formula for converting BeiDou geographic coordinates to Cartesian coordinates is as follows:

[0030] in, , For the i-th device aggregation point The longitude and latitude coordinates of the BeiDou system; , The coordinates are the BeiDou latitude and longitude coordinates of the origin of the coordinate system; v is the coordinate scaling factor, which can be set according to the watershed area, for example... Unit: meters per degree; ensure coordinate values ​​are within a reasonable range. The cosine of the latitude at the origin is used to correct for the problem that "the Earth is an ellipsoid, and the actual distance corresponding to each degree of longitude decreases as latitude increases." The higher the latitude, the smaller the circumference of the longitude circle, hence the use of... The distance along the longitude direction is scaled. Ultimately, Let x and y be the coordinates of the i-th device aggregation point in a Cartesian coordinate system. Specifically, the spherical geographic coordinates provided by the BeiDou system are converted into Cartesian coordinates, which are more suitable for Euclidean distance calculation, geometric optimization, and visualization analysis, providing a unified, accurate, and convenient coordinate system basis for subsequent calculations such as "distance and shortest distance" and "optimized movement".

[0031] Step S2 is the core analysis step in the intelligent resource allocation method of this embodiment. Its core objective is to transform the basic static data obtained in step S1 into dynamic and quantifiable demand insights. This step is based on the set of equipment aggregation points established in step S1. Collection of material types By introducing a time dimension, we can conduct in-depth analysis of each device cluster point and its coordinates. For each type of material Based on historical consumption patterns, and accordingly determine the true and urgent degree of demand. The output of this step provides crucial weighting information for subsequent storage point optimization, ensuring that the optimization direction is precisely tilted towards high-demand areas, thereby achieving efficient resource allocation. Step S2 specifically includes the following steps: S21. This step aims to accurately quantify the consumption capacity of each equipment aggregation point for specific materials per unit time, and execute a consumption rate statistical algorithm for the i-th equipment aggregation point determined in step S1. First, the system is set to a fixed preset monitoring cycle. The cycle should be set reasonably based on the stability of material consumption and business reporting cycle. If the cycle is too long, it may not be able to reflect recent changes, while if the cycle is too short, it is easily affected by accidental factors. Subsequently, during the monitoring period, the system collects data on the i-th device aggregation point through IoT sensors or manual data entry. For Category j materials initial inventory Ending inventory At the same time, record the quantity of the supplies delivered to the gathering point during this period. Calculate the actual consumption quantity , obtain consumption rate ; in, And consumption rate In order to monitor within the preset period The i-th device aggregation point For Category j materials The amount consumed; Consumption rate calculation formula: ; in, Let be the consumption rate of the j-th type of material at the i-th aggregation point, in units of: pieces / day or pieces / month, and . Unit matching; The actual amount consumed within the monitoring period; For the preset monitoring cycle; At the start of the monitoring period, for category j materials At the i-th device aggregation point Inventory quantity, At the end of the monitoring period, the j-th type of materials At the i-th device aggregation point Inventory quantity; Within the monitoring period, the equipment arrives at the i-th equipment aggregation point. Category j materials The amount of supplies.

[0032] Specifically, the "inventory change" is transformed into "rate" to eliminate the impact of the length of the monitoring period on consumption assessment and to achieve horizontal comparison of consumption of different clusters and different materials. For example, if cluster A consumes 5 items in 10 days and cluster B consumes 15 items in 30 days, the consumption rate is 0.5 items / day, and they can be assessed equally.

[0033] In one feasible case, step S1 is used. Equipment aggregation point "Urban Power Plant Cluster" "Cluster of power plants along the river in the urban area" "County hydropower station cluster"; supplies "High-voltage insulating bushing" Thermal grease "Waterproof sealing ring" Using "rust-preventive paint" as an example, the system is set with a monitoring cycle. Heaven, to and Perform statistics. The system reads the due date inventory. Items received within the cycle for replenishment Items, end-of-period inventory count Item.

[0034] Calculate consumption: Item.

[0035] Calculate the consumption rate: Items / day.

[0036] This means that the urban power plant cluster consumes an average of about 1.33 high-voltage insulating bushings per day.

[0037] S22. The objective of this step is to objectively determine the urgency of a particular type of material demand at a given equipment cluster based on the calculated consumption rate, assign a quantified demand value, and execute a demand determination algorithm. The system then assigns a demand value to each type of material. Preset a consumption rate threshold This threshold is a key criterion for distinguishing between "routine consumption" and "low consumption / non-urgent need," and can be set based on the median, average, or management experience of historical consumption data. The determination logic is as follows: the consumption rate calculated in step S21 is used as the basis for the determination. With consumption rate threshold Compare them.

[0038] like Then determine the i-th device aggregation point. For the j-th type of maintenance materials Demand This indicates that the consumption rate is too low and may only be needed for occasional replacements or temporary needs. It is not continuous or urgent, so this need should be ignored in the subsequent overall optimization layout to avoid wasting resources. like Then determine the i-th device aggregation point. For the j-th type of maintenance materials Demand This indicates that consumption has reached a significant level, and the demand is continuous and urgent. The demand level is equal to the consumption rate; the higher the rate, the greater the demand level, and the higher its weight in optimization. In other words, the demand level is determined by whether the consumption rate reaches a threshold. Let i be the demand for the j-th type of goods at the i-th gathering point; Let be the consumption rate threshold for the j-th type of material; Specifically, filtering out "low-consumption, non-urgent" demands (such as a gathering point consuming only one item of supplies in six months) will prevent subsequent storage point optimization from tilting towards areas with no actual demand, ensuring that demand data accurately reflects core needs.

[0039] Continuing with the above implementation plan, for and High-voltage insulating bushing Items / month ≈ 0.1 items / day; It is known that... =1.33 pieces / day, which meets the requirements. ≥ ( ),therefore Items / day.

[0040] right and Waterproof sealing ring, Items / month ≈ 0.33 items / day =0.2 items / day ( ),therefore .

[0041] Step S3 is the core optimization step of this method. Its purpose is to scientifically determine an optimal storage location for each type of material. Step S3 adopts a two-stage optimization strategy: First, based on the demand for each equipment aggregation point corresponding to each type of material determined in Step S2, and combined with the location information of each equipment aggregation point obtained in Step S1, preliminary storage points for each type of material are selected. Then, demand is introduced as a weight, and the preliminary storage points are iteratively adjusted according to the demand for each equipment aggregation point corresponding to each type of material to obtain a preferred storage point that is closer to the high-demand area. This ensures that the material storage considers both overall transportation efficiency and focuses on serving the core demand points. Step S3 specifically includes the following steps: S31. This step is the data preparation stage for subsequent optimization calculations. The data retrieval algorithm is executed for the j-th type of materials. Two types of key data are retrieved from the system database: the P device aggregation points obtained in step S2. For the j-th type of maintenance materials Demand set This collection reflects the demand for supplies at each gathering point. The urgency of the need; calling the P device aggregation points obtained from step S1. Location coordinate set ; In one feasible case, with the first Type of materials (High-voltage insulating bushing) as an example: Demand set: In step S2, the equipment aggregation point Urban power plant clusters Items / day The cluster of power plants along the river in the urban area Items / day County hydropower station cluster Due to consumption rate threshold It is determined to be a non-urgent need, therefore the demand set is: .

[0042] Location coordinate set: In step S13, The plane coordinates are rice, The plane coordinates are rice, The plane coordinates are Meters, therefore the set of position coordinates is .

[0043] S32. The goal of this step is to find the location with the shortest overall distance to all device aggregation points in space. A preliminary storage point selection algorithm is then executed. This algorithm uses a distance sum minimization algorithm, which is essentially a spatial optimization problem: finding a point that minimizes the sum of its Euclidean distances to a given set of points. This point is often referred to internationally as the "point with the minimum Euclidean distance sum" or the "geometric median." Based on the set of position coordinates Filter out all device aggregation points Construct the objective function based on the distance and the shortest position. The positions that satisfy the objective function are calculated through iterative traversal and used as the initial storage point coordinates for the j-th type of material. ; Preliminary formula for calculating the objective function at the storage point: ;in, Let these be the initial storage coordinates for the j-th type of material; This is the sum of the distances from the initial storage point to all gathering points; Indicates to Find the minimum value of the total distance; Calculate the initial storage location To the equipment gathering point The formula for the initial distance is: ; in, for arrive The straight-line distance; , Let i be the coordinates of the i-th aggregation point; Specifically, by using the principle of "distance and minimum", we ensure that the initial storage points serve all gathering points in a spatially balanced manner, and avoid the initial location being too far away from the core demand area, such as the center of the watershed.

[0044] In one feasible case, for the first Type of materials By iteratively traversing the planar coordinates, different arrive , , The distance and sum when trying coordinates Rice time: arrive Distance: rice; arrive Distance: rice; arrive Distance: rice; Distance and sum: rice.

[0045] Continuing the traversal, it was found that when When the distance was less than 19,500 meters, the coordinates were finally determined to be... Initial storage location: rice.

[0046] S33. The initial storage points only considered spatial geometric relationships and did not reflect the differences in demand intensity among various cluster points. This step aims to optimize the initial storage points based on the demand weights obtained in step S2, making them more biased towards high-demand areas. The system executes the optimal storage point optimization algorithm, which is an iterative process; based on the demand set... For the initial storage point Iterative optimization was performed, adjusting the location to favor high-demand clusters, to obtain the optimal storage location coordinates for the j-th type of material. .

[0047] The specific steps of step S33 are as follows: S331, for each device aggregation point This will exert a "pull" on the initial storage point. The magnitude and direction of this pull are determined by its effect on the materials. Demand determines the execution of an optimization parameter calculation algorithm based on the i-th device cluster point. Demand for Category j goods Determine the initial storage location The optimization direction and optimization distance; The optimization direction is determined to start from the initial storage point. Pointing to the i-th device aggregation point Position coordinates The unit direction vector is calculated using the following formula: ;in, To start from the initial storage point Pointing to the i-th aggregation point The unit direction vector; , This is the coordinate difference. This is the initial distance; Specifically, the optimization direction of the storage point is precisely defined to ensure that each iteration moves towards the corresponding cluster point and avoids directional deviation. For example, if the cluster point is northeast of the initial storage point, then all vector components are positive and the storage point moves northeast.

[0048] The optimal distance is determined as follows , for The ratio of the demand for type j materials to the sum of the demand for type j materials at P equipment aggregation points is used to calculate the proportion of demand. ,in ; in, This represents the proportion of demand for type j goods at the i-th aggregation point. Let i be the demand for the j-th type of goods at the i-th gathering point; For P device aggregation points For the j-th type of maintenance materials The total demand is calculated; if the total is 0, no optimization is needed, and the resource storage point can be deleted directly. for arrive The straight-line distance; Specifically, the weight of the demand of a single aggregation point in the total demand is quantified to provide a basis for optimizing the distance of storage points. For example, for an aggregation point with a demand of 30%, the distance that storage points need to move to it is 3 times that of an aggregation point with a demand of 10%.

[0049] S332, Execute the iterative optimization algorithm, and Substituting the above calculations for optimization direction and optimization distance, the initial storage point is then... Perform E iterations of updates, with each iteration based on the i-th device aggregation point. The demand weights are updated to store the coordinates of the storage point. In the i-th iteration, the coordinates are based on the unit direction vector. and optimized distance Update coordinates to ; The formula for updating coordinates in a single iteration is: ; in, These are the coordinates of the storage point after a single iteration. This represents the distance moved in a single iteration. They are respectively The x and y components; Specifically, the coordinates of storage points are progressively optimized. Each update adjusts the movement distance based on the demand ratio, ensuring the final optimal storage point is closer to high-demand clusters. Storage points near high-demand clusters reduce subsequent transportation distances. After E iterations, the optimal storage point coordinates for the j-th type of material are finally obtained. ;in, , The coordinates are after the Eth iteration.

[0050] Specifically, the system outputs the final storage location after demand optimization, ensuring that the location takes into account both the overall service area and the key areas of high demand, thus balancing transportation efficiency and coverage.

[0051] In one feasible case, for the first Type of materials The initial storage location is Rice, the demand set is Optimize: Calculate the total demand: Items / day.

[0052] Calculate the percentage of demand: ( The demand share was 62.5%); ( The demand share was 37.5%); ( (No urgent need, no movement during optimization).

[0053] First iteration (for) , ): Preliminary distance Calculate the initial storage location arrive Distance:

[0054] Unit direction vector : Optimized distance: rice; Coordinates after iteration: ; ; Second iteration (for) , Preliminary distance (current coordinates) arrive ):

[0055] Unit direction vector :

[0056] Optimized distance: rice; Coordinates after iteration:

[0057]

[0058] Third iteration (for) , ): because The optimized distance is 0, and the coordinates remain unchanged.

[0059] final, The preferred storage point coordinates are approximately Rice, compared to the initial storage point, is now closer to areas with high demand. and This can reduce the distance for transporting goods between these two gathering points.

[0060] Step S4 is a crucial step in this method, bridging the preceding and following steps. Its main purpose is to introduce the key constraint of the material storage environment label defined in step S1, based solely on the preferred storage points output in step S3 (which are based solely on demand and geometric relationships), for global coordination and adjustment. This step addresses the problem that storage points for different types of materials may become too close due to optimization calculations, leading to storage environment conflicts or low management efficiency. Based on the storage environment labels set in step S1, the preferred storage points for all materials obtained in step S3 are adjusted to obtain provisional storage points. Step S4 specifically includes the following steps: S41. This step is the data preparation stage for subsequent adjustment operations. It involves executing the algorithm for generating the optimal storage point set. Substituting these values ​​into step S3, we obtain the set of optimal storage location coordinates for type Q materials. ; The optimal storage locations for each type of material in step S3 are summarized into a unified set, providing a data foundation for subsequent "distance judgment and merging / separation decision-making for multiple material storage locations".

[0061] S42. Execute the storage point distance monitoring algorithm and preset the storage point distance judgment threshold. ; Calculate any two types of materials , The distance between the preferred storage points for the two types of materials , The distance between the preferred storage points is calculated using the Euclidean distance formula, as follows: ;in, The straight-line distance between the preferred storage points for materials of category j1 and category j2; , The coordinates of the preferred storage point for category j1 materials; , The coordinates of the optimal storage point for category j2 materials; Specifically, the spatial distance between the storage points of the two types of materials is quantified to determine whether further adjustments are needed. If the distance is too close, it is necessary to combine the labels to determine whether to merge them in order to avoid duplication of warehouse construction.

[0062] like Then, the storage environment label for the j1 type of material set in step S1 is invoked. Storage environment label for Category J2 materials ; S43. Execute the storage point classification adjustment algorithm, if and There are mutually exclusive storage environment tag groups, i.e. Then the j1 type of materials The temporary storage point is set as its preferred storage point. Category J2 materials The temporary storage point is set as its preferred storage point. Adhere to the principle of safety first, and it is better to maintain a small distance than to store incompatible materials together, so as to avoid the deterioration, damage or safety accidents of the materials. like and Non-exclusive label groups, i.e. Then the j1 type of materials With Category J2 materials They belong to the same storage point, and the coordinates of the same storage point are: and The formula for calculating the midpoint coordinates of the same storage point is: ; in, These are the coordinates of the common storage point for materials of type j1 and type j2, respectively.

[0063] Specifically, under the premise that materials can be stored together, calculate the location of a balanced common storage point to ensure that the transportation distance of both types of materials is optimal. For example, if material A is stored in the east and material B in the west, the midpoint can shorten the transportation distance in both the east and west directions.

[0064] In one feasible case, step S3 involves three types of materials, namely... The optimized storage locations are as follows: supplies (High-voltage insulating bushing): The preferred storage location coordinates are as follows: The meter, this position, optimized in step S3, is closer to the target. High-demand equipment clusters , ; supplies (Thermal grease): The preferred storage point coordinates are as follows Rice, its demand is concentrated , Nearby, after optimization in step S3, it is closer to this area; supplies (Waterproof sealing ring): The preferred storage location coordinates are as follows Rice, demand is concentrated Nearby, after optimization in step S3, close to ; The optimal storage locations for all resources are then set as follows: .

[0065] Preferred storage locations for the above three types of materials will be set. Meters, calculate the distance between any two pairs of objects: and Distance: Substitute coordinates , ,have to:

[0066] because Further judgment is needed. and Compatibility.

[0067] and Distance: Substitute coordinates , ,have to:

[0068] because Further judgment is needed. and The compatibility is known in step S12. and The repulsive relationship is used here to verify the handling logic when the distance is too close.

[0069] and Distance: Substitute coordinates , ,have to:

[0070] because Further judgment is needed. and The compatibility, that is: Storage label ; of ,but and The intersection is They are not mutually exclusive.

[0071] Based on the materials and storage tags in the example, materials that are "too close" are processed as follows: and Processing (mutual exclusion): Known Storage label , Storage label ,but They have no common tags and their storage environments are incompatible.

[0072] therefore, The temporary storage location will remain its preferred storage location. rice, The temporary storage location will remain its preferred storage location. rice.

[0073] and Compatibility processing: Storage label for thermal grease ,but They share common tags and are compatible in storage environments.

[0074] calculate and Coordinates of the common storage point (midpoint):

[0075]

[0076] therefore, and The temporary storage points are merged into rice.

[0077] and Processing (the merged results need to be considered after compatibility): because Already with Merge into a common storage point The merging point needs to be reassessed. Preferred storage location Distance: Approximately rice rice; And the storage labels for the merged materials are ,and of The intersection is (Still compatible), we can further calculate new common storage points, the logic is: if they are compatible, merge them, if they are exclusive, separate them.

[0078] Finally, the set of temporary storage points is as follows: ,Right now and merge, Because of and They are stored independently and mutually exclusive.

[0079] Step S5 is a crucial step in this method to ensure the practical operability of the solution. Its purpose is to perform a final physical space compliance check and adjustment on the temporary storage point layout output in step S4. Based on the preset minimum distance constraint, the relative positions of the temporary storage points obtained in step S4 are corrected to determine the warehouse establishment point. Step S5 specifically includes the following steps: S51. Execute the temporary storage point recording algorithm, record the N temporary storage points output in step S4, where N is a positive integer and N≥1, and the set of location coordinates of the N temporary storage points. ,in, Let Z be the coordinates of the z-th temporary storage point; S52. Establish a minimum radius R for the preset storage points, and filter out the temporary storage point pairs with a spacing less than the minimum radius R. If these storage points are used directly as warehouses, there will be safety hazards or management chaos, and further correction is required; storage points with a spacing that meets R do not need to be corrected and can be used directly as warehouse candidate points. Execute the storage point distance judgment algorithm based on the z1th temporary storage point. Iterate through the z2th temporary storage point to form a storage point pair. Calculate the distance between the two ; in, Let z1 be the straight-line distance between the z1th and z2th temporary storage points; z2 be the coordinates of the z1th temporary storage point. The coordinates of the z2th temporary storage point; Specifically, it is necessary to determine whether the temporary storage location meets the minimum distance constraints for warehouse construction, so as to avoid safety hazards or low management efficiency caused by excessively close spacing.

[0080] like If the position of the z1th temporary storage point is corrected, then the position of the z1th temporary storage point will be corrected. like Then select the position coordinates of the z1th temporary storage point. As a warehouse establishment point; in, ; And z2≠z1.

[0081] In step S52, if Then, the storage point location correction algorithm is executed, and the specific steps are as follows: S521. Determine the preferred storage location for the materials corresponding to the z1th temporary storage location. The preferred storage point for materials corresponding to the z2th temporary storage point ; S522, Calculate the distance from the z1th temporary storage point to... The unit correction vector is given by the following formula: ; in, Let z1 be the distance from the z1th temporary storage point to the preferred storage point for the corresponding material; Let Z be the coordinates of the preferred storage point for the materials corresponding to the z1th temporary storage point; This is the distance from the z1th temporary storage point to the corresponding preferred storage point.

[0082] Specifically, ensure that the direction of the correction aligns with the core areas of material demand, and avoid the storage locations deviating from high-demand areas after correction. The area with high demand for the corresponding materials is in the north, so the correction vector is moved north, and the storage point is moved north.

[0083] S523, Set the preset correction distance K, and ; S524. Move the z1th temporary storage point along the unit correction vector. Move the preset correction distance K, and move the z2th temporary storage point along the unit correction vector. Move the coordinates by the preset correction distance K to obtain the corrected coordinates. The warehouse establishment point for the z1th temporary storage point is determined based on the corrected coordinates. in, Establish the coordinates of the warehouse after the correction of the z1th temporary storage point; K is the preset correction distance; They are respectively The x and y components; R is the minimum radius for establishing the storage point.

[0084] In one feasible case, based on the output of step S4, two temporary storage points are finally obtained ( ): The first temporary storage point z1: corresponds to (High-voltage insulating bushing) and The combined storage point for (thermal grease) is located at the following coordinates: ; The second temporary storage point z2: corresponds to The independent storage location for the (waterproof sealing ring) is located at [coordinates missing]. rice; The set of temporary storage points is then: Set storage point and establish minimum radius rice.

[0085] For the two temporary storage points z1 (49858.5, 20921) and z2 (52000, 19800) mentioned above, set the following... Meters, calculate the spacing:

[0086] because ( Therefore, both z1 and z2 satisfy the minimum radius constraint and are tentatively designated as warehouse establishment points to be confirmed.

[0087] To more clearly demonstrate the "judgment logic when constraints are not met", a temporary storage point z3 (51000, 20500) meters is added, and the distance between z2 and z3 is calculated:

[0088] because ( Therefore, z2 and z3 do not satisfy the constraints and need to be corrected in step S53.

[0089] Taking "z2 (52000, 19800) and z3 (51000, 20500) that do not satisfy the constraints" in step S52 as an example, the correction is as follows: Determine the optimal storage point and correction parameters for association: z2 related materials are (Waterproof sealing ring), its preferred storage location is the output of step S3. Mi, Z2 itself is The preferred storage point, with no offset; z3 is associated with newly added resources. (Rust-preventive paint), its preferred storage location is the output of step S3. rice; set up rice, rice( ).

[0090] Calculate the correction direction (unit vector) of z3: First calculate z3 to its associated preferred storage point. Distance: ; Calculate the unit direction vector again : ; calculate Corrected coordinates: ; Verify the corrected spacing: The corrected spacing between z2 (52000, 19800) and z3 (49927, 21036) is as follows:

[0091] because ( Therefore, after correction, the constraints are satisfied, and both z2 and z3 are determined as warehouse establishment points.

[0092] Based on the above corrections, the final set of warehouse creation points is as follows: Warehouse 1: Coordinates (49858.5, 20921) meters, associated materials (High-voltage insulating bushing) (thermal grease); Warehouse 2: Coordinates (52000, 19800) meters, associated materials (Waterproof sealing ring); Warehouse 3: Coordinates (49927, 21036) meters, associated materials (Rust-preventive paint); The distance between all warehouses is ≥2000 meters, which meets the minimum radius constraint and can be directly used for warehouse construction.

[0093] S6 is the closed-loop control and dynamic operation and maintenance stage of this method, marking the shift from static and forward-looking planning and deployment to dynamic and responsive operation and maintenance. Based on the final warehouse establishment point determined in step S5, this step monitors the material inventory in the warehouse establishment point determined in step S5 in real time, and generates replenishment prompts for each type of material based on the demand of each equipment aggregation point obtained in step S2.

[0094] Step S6 specifically includes the following steps: S61. This step is the perception layer of inventory management, responsible for continuously acquiring the latest inventory data. After the physical construction of the warehouse establishment points determined in step S5 is completed and put into operation, the system integrates Internet of Things (IoT) electronic shelf tags, RFID inventory systems, the API interface of the warehouse management system, or periodically manually entered data to execute a real-time inventory monitoring algorithm. After the warehouse establishment points determined in step S5 are completed, the system collects the inventory quantity of the j-th type of materials in each warehouse establishment point in real time. ; S62. Preset the replenishment threshold for the j-th type of material. The formula for calculating the replenishment threshold of the j-th type of material is: ;in, For the P device aggregation points output in step S2 The total demand for category j goods This is the preset minimum quantity of materials to be used over a given period. Specifically, by combining "actual demand" with "safety stock," we can avoid excessive stockpiling (reducing warehousing costs) or insufficient inventory (avoiding maintenance interruptions), thus ensuring that inventory levels are scientifically reasonable. like If this occurs, a supplementary notification message will be generated and sent to the administrator, prompting them to replenish material type j; the supplementary notification message includes the material category. Current inventory And the quantity to be replenished .

[0095] This represents the real-time inventory quantity of the j-th type of material; Specifically, by comparing real-time inventory with thresholds, automated replenishment prompts are achieved, reducing oversights and delays in manual inspections and ensuring the continuity of maintenance material supply.

[0096] In one feasible case, "Warehouse 1" (coordinates 49858.5, 20921 meters, associated) is determined in step S5. , For example: right (High-voltage insulating bushing, boxed, single piece weight 5kg): Deploy a weight sensor, set the sampling frequency to 1 hour / time; at a certain moment, the sensor displays a total weight of 300kg, which is converted to real-time inventory. Item; right (Thermal conductive silicone grease, bottled, 500g per bottle, density 0.9g / cm³): Deploy a liquid level sensor and set the sampling frequency to 6 hours / time; The time sensor displays a volume of 8000 cm³, and calculates the mass. g, converting real-time inventory The result is rounded up to 14 bottles. Since the supplies need to be issued in whole bottles, the decimal part is considered to be less than 1 bottle and is counted as 14 bottles. Inventory data is transmitted to the system via 4G and updated in real time in the ledger. 60 items 14 bottles.

[0097] by Taking high-voltage insulating bushings as an example, calculate the replenishment threshold. : Total demand extraction: In step S2, pieces per day, pieces per day, , so pieces per day; Parameter setting: Replenishment response cycle days, empirical value, average arrival time from the warehouse in the basin to the supplier, minimum usage cycle quantity pieces; Substitute into the formula for calculation:

[0098] Since the inventory quantity is an integer, round up to get pieces. If the threshold is set at 12 pieces and the demand during the replenishment period is 2.4 pieces, it may cause the inventory to drop to 12 - 2.4 = 9.6 pieces < C_1 = 10 pieces. Therefore, taking 13 pieces is safer.

[0099] Taking (waterproof seal ring) as an example: Total demand pieces per day, of , and the other aggregation points are 0; days, pieces; Calculated to get , round up to pieces.

[0100] Taking (high-voltage insulating bushing) in Warehouse 1 as an example for inventory monitoring: At the moment, the real-time inventory pieces, replenishment threshold pieces. Since , trigger a replenishment reminder; Generate a reminder message: "

Emergency Replenishment Reminder

[0101] Threshold basis: Average daily total demand of 0.8 pieces per day × replenishment response of 3 days + safety stock of 10 pieces = 13 pieces."; Push method: System pop-up window, APP notification and SMS. Since the quantity to be replenished is 7 pieces < pieces, do not trigger a phone reminder for the time; Closed-loop management: After receiving the notification, the manager submits a replenishment request for 7 items on the same day; 3 days later, the materials arrive, the weight sensor shows an increase of 35kg in total weight (7 items × 5kg / item), the real-time inventory is updated to 6+7=13 items, the system records "replenishment completed" and stops notifying. Then... Taking the inventory monitoring of (waterproof sealing rings) in warehouse 2 as an example: Real-time inventory Items, replenishment threshold Item, because This triggers a prompt; Quantity to be replenished The push notifications will be sent via app notifications and SMS. After the restock arrived, the inventory was updated to 6 units, and the process was terminated.

[0102] Example 2 The intelligent material allocation system for hydroelectric field maintenance provided by the present invention will be described below. The intelligent material allocation system for hydroelectric field maintenance described below can be referred to in correspondence with the intelligent material allocation method for hydroelectric field maintenance described above, and will be further explained in conjunction with Embodiment 1. like Figure 1 The structure shown is... Figure 1 The intelligent material allocation system for hydroelectric field maintenance provided in this application embodiment includes a basic data acquisition module, a material demand analysis module, a storage point location analysis module, a storage point location adjustment module, a storage point location correction module, and an inventory monitoring and alarm module. The basic data acquisition module is used to record the equipment aggregation points and the types of materials required for the equipment aggregation points within the hydroelectric current domain, set the storage environment label corresponding to each type of material, and obtain the location information of each equipment aggregation point. The basic data acquisition module is the fundamental data entry point for this system, used to collect, process, and standardize all the basic static data required for intelligent allocation of resources. Its specific functions include: Equipment aggregation point identification and recording: After authorization, traverse all equipment deployment areas within the hydroelectric current domain, identify and record valid equipment aggregation points, form a set of equipment aggregation points, and count to obtain the number of equipment aggregation points P; Material type statistics and classification: Collect historical maintenance material lists and real-time demand lists from each equipment aggregation point, deduplicate and standardize the material classification, form a material type set, and count to obtain the total number of material types Q; Storage environment label management: Set a corresponding set of storage environment labels for each type of material, and execute a label incompatibility judgment algorithm to identify incompatible storage environment label groups; Spatial coordinate acquisition and transformation: Establish a plane rectangular coordinate system, obtain the geodetic coordinates of the aggregation points of each device based on the Beidou positioning system, and convert them into plane rectangular coordinates; Data standardization output: The processed equipment aggregation point information, material type information, storage environment label information and location coordinate information are standardized and encapsulated to provide a unified data interface for subsequent modules.

[0103] The material demand analysis module receives the equipment aggregation points, material types and location information output by the basic data acquisition module, analyzes the consumption of various materials at each equipment aggregation point, calculates the consumption rate and determines the demand level. The material demand analysis module is the core of this system's dynamic data analysis, used to transform static basic data into quantifiable demand indicators. Its specific functions include: Consumption data collection: Receives equipment aggregation points, material types and location information output by the basic data acquisition module, sets the monitoring cycle, and collects the beginning inventory, ending inventory and replenishment quantity of various materials at each aggregation point; Consumption rate calculation: Based on the principle of material balance, calculate the consumption rate of each type of material at each equipment aggregation point within the monitoring period; Demand Determination: A consumption rate threshold is preset for each type of material. The consumption rate is compared with the threshold to determine the demand for each type of material at each equipment aggregation point. Demand data aggregation: Summarize and analyze the demand at all equipment aggregation points to form a demand distribution map for each type of material; Demand data output: Encapsulate demand data and its metadata (such as monitoring period, threshold settings, etc.) for use by subsequent modules.

[0104] The storage location analysis module receives demand data from the material demand analysis module and location information from the basic data acquisition module, filters preliminary storage locations for each type of material, and optimizes them to obtain the preferred storage locations. The storage location analysis module is the core of this system's space optimization, responsible for calculating the optimal storage location for each type of material. Its specific functions include: Data reception and verification: Receive demand data output from the material demand analysis module and location information output from the basic data acquisition module, and perform data integrity and consistency verification; Preliminary storage point calculation: For each type of material, a distance-minimum algorithm is used to calculate the preliminary storage point location with the shortest geometric distance to all equipment aggregation points; Demand-weighted optimization: Based on the demand weight, the initial storage point is iteratively optimized to shift the storage point towards areas with high demand; Optimization process monitoring: Monitor the convergence of the optimization algorithm to ensure the stability and rationality of the calculation results; Optimal Location Output: Outputs the coordinates of the optimal storage point for each type of material and the key parameters in the optimization process, providing a basis for subsequent adjustments.

[0105] The storage location adjustment module receives all the preferred storage locations for materials output by the storage location analysis module, and adjusts the preferred storage location locations by combining them with the storage environment tags output by the basic data acquisition module. The storage point location adjustment module is the core of this system's constraint processing, responsible for introducing material attribute constraints based on spatial optimization. Its specific functions include: Optimized storage point set integration: Receives all preferred storage points for materials output by the storage point location analysis module and forms a global storage point distribution map; Distance conflict detection: A preset storage point distance judgment threshold is used to detect distance conflicts between all preferred storage point pairs; Tag incompatibility verification: For storage point pairs with distance conflicts, the storage environment tag information in the basic data retrieval module is called to verify tag incompatibility; Storage location adjustment decision: Based on the results of distance conflict and label incompatibility, decide whether to keep them stored independently or merge them; Provisional Location Output: Outputs the adjusted set of provisional storage points, and records the reason and method of adjustment for each point. Storage Point Location Correction Module: Receives the adjusted storage point locations from the storage point location adjustment module, corrects the locations based on the relative distances between each storage point, and determines the warehouse establishment point. The storage point location correction module is the core of this system's physical constraint processing, ensuring that the solution meets actual construction requirements. Its specific functions include: Provisional point set reception: Receives the adjusted storage point location information output by the storage point location adjustment module; Physical constraint application: Establish a minimum radius for the preset storage point and detect physical space conflicts between all pairs of provisional storage points; Location correction calculation: For storage point pairs with physical conflicts, calculate the corrected location that meets the minimum radius requirement; Optimization of the correction strategy: Ensure that the corrected position is as close as possible to the theoretical optimal position, while maintaining the economy of the solution; Final location determination: Output the final set of coordinates of the warehouse construction points and generate a warehouse construction plan.

[0106] The inventory monitoring and alarm module is used to monitor the quantity of materials in the warehouse in real time after the warehouse establishment point is determined by the storage point location correction module and the warehouse construction is completed. When the quantity of materials is lower than the preset threshold, the module will prompt the management personnel to replenish the materials.

[0107] The inventory monitoring and alarm module is the core of this system's operation and maintenance, enabling closed-loop management of material allocation. Its specific functions include: Warehouse construction monitoring: Monitors the construction progress of the warehouse establishment point determined by the storage point location correction module, and activates the inventory monitoring function after construction is completed; Real-time inventory collection: Real-time collection of inventory quantities of various materials in each warehouse through IoT sensors, RFID systems or manual data entry. Inventory threshold calculation: Dynamically calculate the replenishment threshold for each type of material based on historical demand data and preset safety stock levels; Inventory status assessment: Real-time comparison of current inventory with replenishment thresholds to identify inventory shortages; Replenishment alert generation: When inventory falls below the threshold, a replenishment alert with detailed information is automatically generated and sent to management personnel through multiple channels; Replenishment effectiveness tracking: Monitor the implementation of replenishment, update inventory status, and form a closed-loop management system.

[0108] Taking the hydroelectric domain in Example 1 as an example, the modules of the system work collaboratively in the following order: 1) The basic data acquisition module first identifies 14 equipment aggregation points and 22 types of materials, and completes coordinate transformation and label setting; 2) The material demand analysis module analyzes the consumption data of each aggregation point and calculates the demand for materials such as high-voltage insulating oil; 3) The storage point location analysis module calculates 22 preferred storage points based on the demand and location data; 4) The storage point location adjustment module detects that the storage points for insulators and cables are too close but the labels are incompatible, and decides to keep them stored independently; 5) The storage point location correction module finds that the storage points for insulators and cables are too close, and performs location correction to ensure that the minimum radius requirement is met; 6) The inventory monitoring and alarm module monitors the inventory in real time after the warehouse is built, and generates a replenishment prompt when the inventory of high-voltage insulating oil is lower than the threshold.

[0109] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for intelligent allocation of materials for hydroelectric power field maintenance, characterized in that, Includes the following steps: S1. Record the equipment aggregation points and the types of materials required for the equipment aggregation points within the hydroelectric current domain, set the storage environment label for each type of material, and obtain the location information of each equipment aggregation point; S2. Based on the equipment aggregation points and material types recorded in step S1, analyze the consumption of each type of material by each equipment aggregation point, calculate the consumption rate of each type of material by each equipment aggregation point, and determine the demand of each type of material by each equipment aggregation point based on the consumption rate. S3. Based on the demand for each type of material and the equipment aggregation point determined in step S2, and combined with the location information of each equipment aggregation point obtained in step S1, first select the preliminary storage point for each type of material, and then optimize the location of the preliminary storage point according to the demand for each type of material and the equipment aggregation point, to obtain the preferred storage point for each type of material. S4. Based on the storage environment labels of various materials set in step S1, adjust the preferred storage points of all materials obtained in step S3 to obtain the provisional storage points. S5. Based on the preset minimum distance constraint, the relative position of the temporary storage point obtained in step S4 is corrected to determine the warehouse establishment point. S6. Monitor the inventory of materials in the warehouse establishment point determined in step S5 in real time, and generate replenishment prompts based on the demand of each type of material at each equipment aggregation point obtained in step S2.

2. The intelligent material allocation method for hydroelectric field maintenance according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11. After authorization, execute the equipment cluster point identification and counting algorithm, traverse all areas where equipment is deployed within the hydroelectric current domain, mark them as valid equipment cluster points, and form a set of equipment cluster points. ,in For the i-th valid equipment cluster point, count to obtain the number of equipment cluster points P, where P is a positive integer and P≥1. Equipment cluster points include power plants and power consumption plants. S12. Execute the material type statistics and mutual exclusion label judgment algorithm, collect the historical maintenance material list and real-time demand list of each equipment aggregation point, remove duplicates and classify the materials in the list to form a material type set. ,in For the j-th type of maintenance materials, count to obtain the total number of material types Q, where Q is a positive integer and Q≥1; For Category j materials Set storage environment tag set If there are two types of supplies , Its tag set satisfies Then determine For mutually exclusive storage environment tag groups; in, This is the set of storage environment tags for category j1 materials. This is the set of storage environment tags for category j2 materials. For set intersection operators, It is an empty set; S13. Execute the BeiDou coordinate transformation algorithm to establish a spatial coordinate system; The i-th device aggregation point is collected based on the BeiDou positioning system. latitude and longitude The algorithm for converting latitude and longitude to Cartesian coordinates is used to output the i-th device aggregation point from the BeiDou positioning system. latitude and longitude Convert to Cartesian coordinates ,in .

3. The intelligent material allocation method for hydroelectric field maintenance according to claim 2, characterized in that, Step S2 specifically includes the following steps: S21. Execute the consumption rate statistics algorithm for the i-th device aggregation point determined in step S1. Set a preset monitoring cycle ; The i-th device aggregation point within the monitoring period For Category j materials initial inventory Ending inventory And recorded in the monitoring cycle. The quantity of supplies transported to this gathering point Calculate the actual consumption quantity , obtain consumption rate ; in, And consumption rate In order to monitor within the preset period The i-th device aggregation point For Category j materials The amount consumed; S22. Execute the demand determination algorithm and preset the consumption rate threshold. ; like Then determine the i-th device aggregation point. For the j-th type of maintenance materials Demand This indicates that there is no urgent need for allocation; like Then determine the i-th device aggregation point. For the j-th type of maintenance materials Demand This indicates an urgent need for allocation.

4. The intelligent material allocation method for hydroelectric field maintenance according to claim 3, characterized in that, Step S3 specifically includes the following steps: S31. Execute the data retrieval algorithm for the j-th type of materials. Call the P device aggregation points obtained in step S2 For the j-th type of maintenance materials Demand set and the P device aggregation points obtained in step S1 Location coordinate set ; S32. Execute the preliminary storage point selection algorithm. The preliminary storage point selection algorithm uses a distance-sum minimization algorithm, based on the set of location coordinates. Filter out all device aggregation points Construct the objective function based on the distance and the shortest position. The positions that satisfy the objective function are calculated through iterative traversal and used as the initial storage point coordinates for the j-th type of material. ; in, for arrive The straight-line distance; S33. Execute the optimal storage point optimization algorithm: based on the demand set. For the initial storage point Iterative optimization was performed, adjusting the location to favor high-demand clusters, to obtain the optimal storage location coordinates for the j-th type of material. .

5. The intelligent material allocation method for hydroelectric field maintenance according to claim 4, characterized in that, The specific steps of step S33 are as follows: S331. Execute the optimization parameter calculation algorithm, based on the i-th device aggregation point. Demand for Category j goods Determine the initial storage location The optimization direction and optimization distance; Calculate the initial storage location To the equipment gathering point initial distance ; The optimization direction is determined to start from the initial storage point. Pointing to the i-th device aggregation point Position coordinates The unit direction vector, using the unit direction vector express; in, , This is the coordinate difference. This is the initial distance; The optimal distance is determined as follows , for The ratio of the demand for type j materials to the sum of the demand for type j materials at P equipment aggregation points, i.e., the proportion of demand. ; in, For P device aggregation points For the j-th type of maintenance materials The total demand is calculated; if the total is 0, no optimization is needed, and the resource storage point can be deleted directly. for arrive The straight-line distance; S332, Execute the iterative optimization algorithm, and Substituting the above calculations for optimization direction and optimization distance, the initial storage point is then... Perform E iterations of updates, with each iteration based on the i-th device aggregation point. The demand weights are updated to store the coordinates of the storage point. In the i-th iteration, the coordinates are based on the unit direction vector. and optimized distance Update coordinates to ; in, These are the coordinates of the points stored after a single iteration. This represents the distance moved in a single iteration. They are respectively The x and y components; After E iterations, the optimal storage location coordinates for the j-th type of material are finally obtained. ; in, , The coordinates are after the Eth iteration.

6. The intelligent material allocation method for hydroelectric field maintenance according to claim 5, characterized in that, Step S4 specifically includes the following steps: S41. Execute the algorithm for generating the optimal storage point set, and Substituting these values ​​into step S3, we obtain the set of optimal storage location coordinates for type Q materials. ; S42. Execute the storage point distance monitoring algorithm and preset the storage point distance judgment threshold. ; Calculate any two types of materials , The distance between the preferred storage points for the two types of materials , The distance between the preferred storage points is calculated using the Euclidean distance formula, as follows: ； like Then, the storage environment label for the j1 type of material set in step S1 is invoked. Storage environment label for Category J2 materials ; S43. Execute the storage point classification adjustment algorithm, if and There are mutually exclusive storage environment tag groups, i.e. Then the j1 type of materials The temporary storage point is set as its preferred storage point. Category J2 materials The temporary storage point is set as its preferred storage point. (Avoid storing incompatible materials together); like and Non-exclusive label groups, i.e. Then the j1 type of materials With Category J2 materials They belong to the same storage point, and the coordinates of the same storage point are: and The formula for calculating the midpoint coordinates of the same storage point is: ; in, These are the coordinates of the common storage point for materials of type j1 and type j2, respectively.

7. The intelligent material allocation method for hydroelectric field maintenance according to claim 6, characterized in that, Step S5 specifically includes the following steps: S51. Execute the temporary storage point recording algorithm, record the N temporary storage points output in step S4, where N is a positive integer and N≥1, and the set of location coordinates of the N temporary storage points. ,in, Let Z be the coordinates of the z-th temporary storage point; S52. Set the minimum radius R for the preset storage point; Execute the storage point distance judgment algorithm based on the z1th temporary storage point. Iterate through the z2th temporary storage point and calculate the distance between them. ; like If the position of the z1th temporary storage point is corrected, then the position of the z1th temporary storage point will be corrected. like Then select the position coordinates of the z1th temporary storage point. As a warehouse establishment point; in, ; And z2≠z1.

8. The intelligent material allocation method for hydroelectric field maintenance according to claim 7, characterized in that, In step S52, if Then, the storage point location correction algorithm is executed, and the specific steps are as follows: S521. Determine the preferred storage location for the materials corresponding to the z1th temporary storage location. The preferred storage point for materials corresponding to the z2th temporary storage point ; S522, Calculate the distance from the z1th temporary storage point to... unit correction vector ; in, Let z1 be the distance from the z1th temporary storage point to the preferred storage point for the corresponding material; S523, Set the preset correction distance K, and ; S524. Move the z1th temporary storage point along the unit correction vector. Move the preset correction distance K, and move the z2th temporary storage point along the unit correction vector. Move the coordinates by the preset correction distance K to obtain the corrected coordinates. The warehouse establishment point for the z1th temporary storage point is determined based on the corrected coordinates; where, They are respectively The x and y components.

9. The intelligent material allocation method for hydroelectric field maintenance according to claim 6, characterized in that, Step S6 specifically includes the following steps: S61. Execute the real-time inventory monitoring algorithm. After the warehouse establishment points determined in step S5 are completed, collect the inventory quantity of the j-th type of material in each warehouse establishment point in real time. ; S62. Preset the replenishment threshold for the j-th type of material. The formula for calculating the replenishment threshold of the j-th type of material is: ； in, For the P device aggregation points output in step S2 The total demand for category j goods This is the preset minimum quantity of materials to be used over a given period. like If this occurs, a supplementary prompt message will be generated and sent to the administrator, prompting them to replenish the j-th type of materials; The supplementary information includes the category of supplies. Current inventory And the quantity to be replenished .

10. A smart material allocation system for hydroelectric power field maintenance, characterized in that, The system is applied to the intelligent material allocation method for hydroelectric domain maintenance as described in any one of claims 1 to 9. The system includes a basic data acquisition module, a material demand analysis module, a storage point location analysis module, a storage point location adjustment module, a storage point location correction module, and an inventory monitoring and alarm module. The basic data acquisition module is used to record the equipment aggregation points and the types of materials required for the equipment aggregation points within the hydroelectric current domain, set the storage environment label corresponding to each type of material, and obtain the location information of each equipment aggregation point. The material demand analysis module receives the equipment aggregation points, material types and location information output by the basic data acquisition module, analyzes the consumption of various materials at each equipment aggregation point, calculates the consumption rate and determines the demand level. The storage location analysis module receives demand data from the material demand analysis module and location information from the basic data acquisition module, filters preliminary storage locations for each type of material, and optimizes them to obtain the preferred storage locations. The storage location adjustment module receives all the preferred storage locations for materials output by the storage location analysis module, and adjusts the preferred storage location locations by combining them with the storage environment tags output by the basic data acquisition module. The storage point location correction module receives the adjusted storage point location output by the storage point location adjustment module, corrects the location based on the relative distance between each storage point, and determines the warehouse establishment point. The inventory monitoring and alarm module is used to monitor the quantity of materials in the warehouse in real time after the warehouse establishment point is determined by the storage point location correction module and the warehouse construction is completed. When the quantity of materials is lower than the preset threshold, the module will prompt the management personnel to replenish the materials.

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