Indoor storage material checking and spatial positioning method based on improved cuckoo algorithm

Abstract

The invention provides an indoor storage material checking and spatial positioning method based on an improved cuckoo algorithm, and relates to the technical field of warehouse logistics intelligent management, and the method comprises the steps: obtaining an indoor storage environment, constructing a three-dimensional model, and determining candidate sensor distribution points and a target material storage point set; introducing a point distribution constraint condition, an error correction mechanism, a hybrid search optimization strategy and an incremental node strategy to generate an improved cuckoo algorithm; establishing a single-sensor error model to calculate a single-sensor positioning error, further calculating a multi-sensor joint positioning error and determining a positioning precision standard; based on an improved cuckoo algorithm, an initial solution vector is constructed through binary coding, the solution vector is iterated through a fitness function and a dual point distribution target, and an optimal sensor point distribution set is generated, so that a sensor point distribution scheme can be optimized, the efficiency and accuracy of material inventory are improved, the industrial positioning precision requirement is met, and the equipment deployment cost is reduced. And the automation level of warehouse material management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of warehousing logistics, and particularly to an indoor warehousing material inventory and space positioning method based on an improved cuckoo algorithm. Background Art

[0002] In modern warehousing systems, due to problems such as low efficiency, large data errors, and management loopholes in traditional manual material inventory and space management methods, it has become difficult to meet the requirements of efficient and accurate operations.

[0003] In addition, the existing technologies do not fully consider the physical constraints in the warehousing environment, such as shelf occlusion, obstacle distribution, etc., which leads to relatively large positioning errors; the standard cuckoo search algorithm often relies on idealized search space assumptions and is prone to falling into local optima in complex warehousing topological structures, with slow convergence speed and poor stability; the existing technologies lack a systematic sensor layout optimization strategy, making it difficult to minimize the number of devices while meeting the accuracy requirements, which leads to relatively high sensor deployment costs.

[0004] Therefore, it is necessary to provide an indoor warehousing material inventory and space positioning method based on an improved cuckoo algorithm to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an indoor warehousing material inventory and space positioning method based on an improved cuckoo algorithm, which is used to solve the problems of insufficient positioning accuracy, weak environmental adaptability, and high sensor deployment costs of traditional indoor warehousing material inventory and space positioning methods.

[0006] The indoor warehousing material inventory and space positioning method based on an improved cuckoo algorithm provided by the present invention includes: Obtain the indoor warehousing environment, construct a three-dimensional warehousing environment model corresponding to the indoor warehousing environment by using three-dimensional laser scanning technology, and determine a set of candidate sensor layout points and a set of target material storage points; Based on the standard cuckoo algorithm, introduce layout constraints, error correction mechanisms, hybrid search optimization strategies, and incremental node strategies to generate an improved cuckoo algorithm; Establish a single-sensor error model based on the Euclidean distance and calculate the contribution of single-sensor positioning error, calculate the multi-sensor joint positioning error based on the single-sensor error contribution by using the weighted covariance formula, and determine the positioning accuracy standard based on the multi-sensor joint positioning error; Based on the improved cuckoo algorithm, import the 3D warehousing environment model, the candidate sensor placement set, the target material storage point set, and the positioning accuracy standard. Construct an initial solution vector through binary coding, and iteratively update the initial solution vector through the fitness function and the corresponding dual placement objectives to generate an optimal sensor placement set.

[0007] Preferably, for obtaining the indoor warehousing environment, a 3D warehousing environment model corresponding to the indoor warehousing environment is constructed using 3D laser scanning technology, and a candidate sensor placement set and a target material storage point set are determined, which specifically includes: Obtain the indoor warehousing environment, use the 3D laser scanning technology to determine the physical structure of the indoor warehousing environment, and mark the shelf coordinates, aisle directions, and obstacle areas to generate the 3D warehousing environment model; Based on the 3D warehousing environment model, determine multiple sensor installation positions to form the candidate sensor placement set, and mark the coordinates of the material storage area to form the target material storage point set.

[0008] Preferably, the placement constraint conditions include signal occlusion rules, sensor installation height limitations, and warehousing operation process constraints; the error correction mechanism obtains historical positioning data by establishing a real-time error feedback link to calibrate the parameters of the single-sensor error model; the hybrid search optimization strategy includes a global search strategy and a local optimization strategy. The global search strategy explores the sensor placement scheme through the Levy flight mechanism, and the local optimization strategy introduces a neighborhood search operator to refine the sensor placement scheme; the incremental node strategy dynamically adjusts the search granularity according to the scale of the indoor warehousing environment.

[0009] Preferably, for establishing a single-sensor error model based on the Euclidean distance and calculating the contribution of single-sensor positioning error, it specifically includes: Based on the single-sensor error model, calculate the candidate sensor placement in the candidate sensor placement set S and the target material storage point in the target material storage point set T where: represents the Euclidean distance between the candidate sensor placement and the target material storage point ; Based on the Euclidean distance , calculate the contribution of the single-sensor positioning error : where: represents the distance attenuation coefficient; Represents the basic error term.

[0010] Preferably, the weighted covariance formula is used to calculate the multi-sensor joint positioning error based on the single-sensor error contribution, and the positioning accuracy standard is determined based on the multi-sensor joint positioning error, specifically including: The storage point of the target material Is simultaneously observed by the inventory sensors at k of the candidate sensor deployment points Based on the maximum likelihood estimation theory, calculate the single-sensor positioning error contribution of all the inventory sensors The weighted harmonic mean of is the multi-sensor joint positioning error : Based on the multi-sensor joint positioning error Construct a candidate positioning accuracy set , and select the minimum value in the candidate positioning accuracy set A as the positioning accuracy standard .

[0011] Preferably, based on the improved cuckoo algorithm, import the three-dimensional warehouse environment model, the candidate sensor deployment point set, the target material storage point set, and the positioning accuracy standard, construct an initial solution vector through binary coding, and iteratively update the initial solution vector through the fitness function and the corresponding double deployment objective to generate an optimal sensor deployment point set, specifically including: Import the three-dimensional warehouse environment model, the candidate sensor deployment point set S, the target material storage point set T, and the positioning accuracy standard Into the improved cuckoo algorithm; Use the binary coding To represent the deployment point selection result, Indicates deploying the inventory sensor at the candidate sensor deployment point , Indicates not deploying the inventory sensor at the candidate sensor deployment point , construct the initial solution vector , Represents the candidate sensor deployment point in the candidate sensor deployment point set S The number of, and the solution space size of the initial solution vector X is ; Iteratively update the deployment point selection result in the initial solution vector X through the fitness function and the corresponding double deployment objective , until the preset convergence condition of the improved cuckoo algorithm is satisfied. The expressions of the fitness function and the corresponding double deployment objective are as follows: In the formula, represents the result of the sensor placement selection and the fitness function value; represents the number of the optimal sensor placements in the optimal sensor placement set A; represents the penalty coefficient; represents the result of the sensor placement selection and the corresponding multi-sensor joint positioning error ; max represents taking the maximum value.

[0012] Preferably, the preset convergence condition of the improved cuckoo algorithm is that the degree of fitness change of the fitness function value for v consecutive generations is less than a preset threshold , and the corresponding expression is as follows: In the formula, represents the fitness function value of the u-th generation; represents the fitness function value of the (u - v)-th generation.

[0013] An indoor material inventory and space positioning system based on the improved cuckoo algorithm, the system includes: A sensor placement and material storage point determination module, configured to obtain the indoor storage environment, construct a three-dimensional storage environment model corresponding to the indoor storage environment by using three-dimensional laser scanning technology, and determine a candidate sensor placement set and a target material storage point set; An improved cuckoo algorithm setting module, configured to generate an improved cuckoo algorithm based on the standard cuckoo algorithm by introducing a sensor placement constraint condition, an error correction mechanism, a hybrid search optimization strategy, and an incremental node strategy; A positioning error and accuracy standard calculation module, configured to establish a single-sensor error model based on the Euclidean distance and calculate the contribution of the single-sensor positioning error, calculate the multi-sensor joint positioning error based on the single-sensor error contribution by using a weighted covariance formula, and determine a positioning accuracy standard based on the multi-sensor joint positioning error; An optimal sensor placement set generation module, configured to import the three-dimensional storage environment model, the candidate sensor placement set, the target material storage point set, and the positioning accuracy standard based on the improved cuckoo algorithm, construct an initial solution vector by binary coding, and iteratively update the initial solution vector through a fitness function and a corresponding dual sensor placement target to generate an optimal sensor placement set.

[0014] An electronic device includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the steps of the indoor storage material inventory and space positioning method based on the improved cuckoo algorithm described in any one of the above.

[0015] A readable storage medium stores a computer program. When the computer program is executed by a processor, it is used to implement the steps of the indoor storage material inventory and space positioning method based on the improved cuckoo algorithm described in any one of the above.

[0016] Compared with the related technologies, the indoor storage material inventory and space positioning method based on the improved cuckoo algorithm provided by the present invention has the following beneficial effects: The present invention obtains the indoor storage environment, constructs a three-dimensional storage environment model corresponding to the indoor storage environment by using three-dimensional laser scanning technology, and determines a candidate sensor placement point set and a target material storage point set; based on the standard cuckoo algorithm, introduces placement constraints, an error correction mechanism, a hybrid search optimization strategy, and an incremental node strategy to generate an improved cuckoo algorithm; establishes a single-sensor error model based on the Euclidean distance and calculates the single-sensor positioning error contribution, calculates the multi-sensor joint positioning error based on the single-sensor error contribution by using the weighted covariance formula, and determines the positioning accuracy standard based on the multi-sensor joint positioning error; based on the improved cuckoo algorithm, imports the three-dimensional storage environment model, the candidate sensor placement point set, the target material storage point set, and the positioning accuracy standard, constructs an initial solution vector through binary coding, and iteratively updates the initial solution vector through the fitness function and the corresponding dual placement target to generate an optimal sensor placement point set, thereby realizing the intelligent optimization of the sensor placement scheme, improving the efficiency and accuracy of material inventory, significantly reducing the equipment deployment cost on the premise of meeting the industry positioning accuracy requirements, and improving the automation and precision level of warehouse material management.

[0017] The present invention constructs a three-dimensional storage environment model, calculates the multi-sensor joint positioning error, and applies the improved cuckoo algorithm, combines indoor positioning technology and intelligent recognition means, can optimize the number of sensors and the placement scheme, reduce the equipment deployment cost, and can realize the real-time positioning and automatic inventory of goods, reduce manual intervention, improve the long-term positioning stability, improve the intelligent level of warehouse management, and promote the development of the enterprise's intelligent logistics. The method of the present invention can solve the problems of time-consuming and error-prone of the traditional manual inventory method, accurately locate goods through intelligent inventory technology, realize real-time data synchronization, improve the accuracy of material inventory and space positioning, ensure that the positioning error is less than the industry standard, and improve the accuracy and update efficiency of inventory information. Description of the Drawings

[0018] Figure 1 A flowchart of an indoor warehouse material inventory and spatial positioning method based on an improved cuckoo algorithm provided in an embodiment of the present invention; Figure 2 A system block diagram of an indoor material inventory and spatial positioning system based on an improved cuckoo algorithm provided by an embodiment of the present invention; Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of an indoor storage material inventory and spatial positioning method based on an improved cuckoo algorithm provided by an embodiment of the present invention. Figure 1 The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDAs) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S4, as follows: S1, obtaining an indoor storage environment, constructing a 3D storage environment model corresponding to the indoor storage environment using 3D laser scanning technology, and determining a set of candidate sensor points and a set of target material storage points; Among them, the indoor storage environment refers to the indoor physical space for storing and managing materials, which has clear functional partitions, spatial structures, and business processes. Three-dimensional laser scanning technology is a spatial data acquisition technology based on the principle of laser ranging. By emitting laser beams and measuring the time difference or phase difference of the reflected signals, the three-dimensional coordinates and reflection intensity information of the object surface are obtained, forming high-density point cloud data. In the storage scenario, this technology is used to construct an accurate digital model of the indoor storage environment, including spatial features such as shelf contours, aisle directions, and obstacle distributions, providing a geometric constraint basis for subsequent sensor deployment and positioning algorithms. The three-dimensional storage environment model refers to a parametric model constructed based on three-dimensional laser scanning data. Through technologies such as point cloud processing and surface reconstruction, the storage environment can be abstracted into a digital entity containing spatial coordinates, object attributes, and topological relationships. Among them, object attributes include shelf height, material, etc., and topological relationships such as aisle connectivity. This model is the spatial basis for improving the cuckoo algorithm processing, supporting geometric operations such as distance calculation and collision detection, as well as signal propagation simulation such as multipath effect analysis.

[0021] Furthermore, the candidate sensor placement set is a finite set composed of all optional sensor installation positions in the storage environment, usually screened and determined based on building structures and signal coverage requirements. Among them, building structures such as ceiling keels and column surfaces, and signal coverage requirements such as the requirement for an unobstructed view. The target material storage point set refers to the set of fixed or dynamic material storage positions in the storage environment, and each target material storage point corresponds to the actual storage coordinates of the material.

[0022] In the indoor storage environment, first, three-dimensional laser scanning technology can be used to accurately measure the spatial structure and object distribution of the indoor storage environment. Through the processing and analysis of the scanned data, a corresponding three-dimensional storage environment model can be constructed. This model can visually present the layout of the storage space, including the positions of the shelves, the directions of the aisles, and the distributions of other obstacles, and then the candidate sensor placement set and the target material storage point set can be determined.

[0023] S2. Based on the standard cuckoo algorithm, introduce placement constraints, error correction mechanisms, hybrid search optimization strategies, and incremental node strategies to generate an improved cuckoo algorithm; It can be understood that the standard cuckoo algorithm is a meta-heuristic optimization algorithm that simulates the parasitic behavior of cuckoos laying eggs. It generates new solutions through the Levy flight mechanism and updates the population through the host bird expulsion mechanism to achieve global search. The standard cuckoo algorithm can assume that the search space is unconstrained and is applicable to continuous optimization problems. However, in discrete storage placement optimization, there are defects such as insufficient physical constraint modeling and weak local development ability.

[0024] Specifically, the node layout constraint conditions refer to the constraint conditions for the installation of sensors in the warehousing environment, which are set according to the actual situation of the warehousing environment and engineering requirements. For example, sensors cannot be installed in certain areas, or the installation positions need to meet the preset height and angle limitations, etc. The error correction mechanism refers to a dynamic parameter calibration mechanism based on real-time positioning error data. By analyzing and processing the errors generated in actual measurements, this mechanism can adjust the relevant parameters in the standard cuckoo algorithm, thereby improving the positioning accuracy and reducing the impact of errors on the positioning results. The hybrid search optimization strategy refers to a composite optimization strategy that combines global search and local development. It combines different search methods and integrates the advantages of global search and local search, enabling the algorithm to widely explore the search space and perform fine optimization in local areas during the process of finding the optimal solution. The incremental node strategy refers to an adaptive optimization strategy for warehousing environments of different scales. It can dynamically adjust the number and method of searched nodes according to the scale and characteristics of the warehousing environment, thereby improving the efficiency and adaptability of the algorithm. By introducing the above improvement mechanisms into the standard cuckoo algorithm, an improved cuckoo algorithm can be generated.

[0025] S3. Establish a single-sensor error model based on the Euclidean distance and calculate the contribution of the single-sensor positioning error. Use the weighted covariance formula to calculate the multi-sensor joint positioning error based on the contribution of the single-sensor error, and determine the positioning accuracy standard based on the multi-sensor joint positioning error. In practical applications, the Euclidean distance refers to the straight-line distance between two points in three-dimensional space. As a basic parameter for single-sensor error modeling, it reflects the physical distance between the sensor and the target point and is positively correlated with the signal propagation loss. The single-sensor error model refers to a model used to calculate the contribution of a single sensor to the positioning error of the target point. The weighted covariance formula refers to a joint evaluation formula used to calculate the positioning errors of multiple sensors. Considering the correlation and weight between different sensor errors, it can more accurately evaluate the positioning errors of multiple sensors. The positioning accuracy standard refers to the standard value of the positioning accuracy.

[0026] S4. Based on the improved cuckoo algorithm, import the three-dimensional warehousing environment model, the candidate sensor layout set, the target material storage point set, and the positioning accuracy standard. Construct an initial solution vector through binary coding, and iteratively update the initial solution vector through the fitness function and the corresponding dual layout objectives to generate an optimal sensor layout set.

[0027] Among them, the initial solution vector is the initial input of the improved cuckoo algorithm, which can be randomly generated or generated by heuristic methods, such as preferentially deploying high-coverage points. This solution vector can serve as the starting point for subsequent algorithm optimization and needs to meet the point placement constraint conditions to avoid invalid calculations. The fitness function refers to a quantitative function used to evaluate the quality of the sensor placement scheme. The dual placement objective refers to the combination of optimization objectives of the improved cuckoo algorithm, that is, minimizing the number of sensors and maximizing the positioning accuracy compliance rate.

[0028] Finally, based on the improved cuckoo algorithm, the three-dimensional warehouse environment model, the candidate sensor placement set, the target material storage point set, and the positioning accuracy standard can be used as inputs. An initial solution vector is constructed by binary encoding. Binary encoding is a method of representing the solution of a problem as a binary string. Among them, each bit corresponds to a candidate placement position, 0 means not selecting this position, and 1 means selecting. Then, through the fitness function and the corresponding dual placement objective, the initial solution vector can be iteratively updated, and finally an optimal sensor placement set is generated. This set can minimize the number of sensors used to the greatest extent while meeting the positioning accuracy standard, achieving a balance between cost and performance.

[0029] In the specific implementation process, to obtain the indoor warehouse environment, a three-dimensional warehouse environment model corresponding to the indoor warehouse environment is constructed by using three-dimensional laser scanning technology, and a candidate sensor placement set and a target material storage point set are determined. Specifically, it includes: Obtain the indoor warehouse environment, use the three-dimensional laser scanning technology to determine the physical structure of the indoor warehouse environment, mark the shelf coordinates, aisle directions, and obstacle areas, and generate the three-dimensional warehouse environment model; Based on the three-dimensional warehouse environment model, determine multiple sensor installation positions to form the candidate sensor placement set, and mark the coordinates of the material storage area to form the target material storage point set.

[0030] In the digital construction of the indoor warehouse environment and the deployment of the positioning system, after obtaining the target environment, accurate modeling and data annotation of the physical space can be realized through three-dimensional laser scanning technology. First, the three-dimensional laser scanning technology can be used to collect comprehensive data of the indoor warehouse environment, obtain the three-dimensional coordinate information of each object in the space through the principle of laser ranging, and then analyze the physical structure of the warehouse environment, including the spatial coordinates of the shelves, the distribution and direction of the aisles, and the position and contour of the obstacle areas. Based on these data, a three-dimensional warehouse environment model containing geometric features and topological relationships can be constructed. This model can intuitively reflect the three-dimensional layout of the warehouse space and provide basic spatial data for subsequent sensor deployment and positioning algorithms.

[0031] After the 3D model construction is completed, it is necessary to further determine the candidate positions for sensor deployment and the target areas for material positioning. Based on the 3D warehouse environment model, considering signal coverage requirements, equipment installation feasibility such as load-bearing limitations and wiring convenience, as well as the warehouse operation process, multiple eligible sensor installation positions are screened out in the space to form a candidate sensor placement set. These positions need to effectively cover the material storage area while avoiding adverse factors such as obstacle occlusion. At the same time, by analyzing the material storage rules and historical data in the warehouse management system, the specific area coordinates of material storage are marked to form a set of target material storage points, clarifying the key areas that need to be accurately positioned.

[0032] Through 3D laser scanning technology, the digital mapping of the warehouse environment can be realized. Combining engineering constraints and business requirements, the pre-planning of sensor placement and material storage points can be completed, providing a structured spatial data basis and optimization object for subsequent positioning optimization based on the improved cuckoo algorithm, and then ensuring that the sensor deployment plan not only meets the physical environment limitations but also can satisfy the accuracy and coverage requirements of material positioning.

[0033] The placement constraint conditions include signal occlusion rules, sensor installation height limitations, and warehouse operation process constraints; the error correction mechanism obtains historical positioning data by establishing a real-time error feedback link to calibrate the parameters of the single-sensor error model; the hybrid search optimization strategy includes a global search strategy and a local optimization strategy. The global search strategy explores the sensor placement plan through the Levy flight mechanism, and the local optimization strategy introduces a neighborhood search operator to refine the sensor placement plan; the incremental node strategy dynamically adjusts the search granularity according to the scale of the indoor warehouse environment.

[0034] It should be noted that the material positioning method based on the intelligent optimization algorithm can effectively overcome the limitations of traditional rule-based positioning methods and improve the utilization rate of space resources. However, the standard cuckoo algorithm, that is, the existing Cuckoo Search (CS) algorithm, mainly has the following problems when applied to the actual warehouse scenario: The search space assumption of the standard cuckoo algorithm is too idealized, without fully considering the physical constraints of the warehouse environment, which leads to a low feasibility of the search results; the positioning accuracy of this algorithm is difficult to meet the requirements of the warehouse scenario, and it lacks an error correction mechanism, which may cause a large positioning deviation in a complex warehouse environment; the global search and local search capabilities of this algorithm are unbalanced. It often relies on Levy flight for global search, but it is easy to fall into local optima in high-dimensional optimization problems, affecting the convergence speed and solution stability; due to the significant differences in the topological structure of the warehouse environment, the single optimization strategy of this algorithm is difficult to meet the optimization needs of warehouses of different scales.

[0035] To address the above problems, the present invention constructs an Improved Cuckoo Search (ICS). By setting the cuckoo placement constraint conditions, introducing an error correction mechanism, combining local optimization and global optimization operators, and adopting an incremental node optimization strategy, it can perform adaptive optimization in warehousing environments of different scales, ultimately forming an optimal sensor placement pattern and improving the accuracy and robustness of warehousing material positioning.

[0036] The establishment of a single-sensor error model based on the Euclidean distance and the calculation of the single-sensor positioning error contribution specifically include: Based on the single-sensor error model, calculate the candidate sensor placement in the candidate sensor placement set S and the target material storage point in the target material storage point set T wherein: represents the Euclidean distance between the candidate sensor placement and the target material storage point ; Based on the Euclidean distance , calculate the single-sensor positioning error contribution : wherein: represents the distance attenuation coefficient; represents the basic error term.

[0037] The calculation of the multi-sensor joint positioning error based on the weighted covariance formula using the single-sensor positioning error contribution and the determination of the positioning accuracy standard based on the multi-sensor joint positioning error specifically include: The target material storage point is simultaneously observed by the inventory sensors at k candidate sensor placements. Based on the maximum likelihood estimation theory, calculate the weighted harmonic mean of the single-sensor positioning error contributions of all the inventory sensors as the multi-sensor joint positioning error : Based on the multi-sensor joint positioning error construct a candidate positioning accuracy set , and select the minimum value in the candidate positioning accuracy set A as the positioning accuracy standard .

[0038] It should be noted that for the candidate sensor placement set and the target material storage point set, it is necessary to calculate the spatial distance between any candidate sensor placement and the target material storage point. The Euclidean distance is used as the standard to measure the spatial interval between the two, and this distance represents the straight-line distance between two points in three-dimensional space. This distance value directly reflects the physical interval between the sensor and the target point and is the core parameter for evaluating signal propagation loss.

[0039] Then, a single-sensor error model can be established based on the Euclidean distance. This model describes the relationship between distance and positioning error in a linear relationship. Among them, the distance attenuation coefficient represents the error increment corresponding to a unit distance and reflects the attenuation rate of signal strength with the increase of distance; the basic error term includes environmental interference factors such as the inherent error of the sensor device and multipath effects. Through this model, the spatial distance can be converted into the contribution value of the single sensor to the positioning error of the target point, providing basic data for multi-sensor error fusion.

[0040] Furthermore, when the target material storage point is observed by multiple inventory sensors simultaneously, the weighted covariance method can be used to calculate the joint positioning error. Based on the maximum likelihood estimation theory, the reciprocal square of the contribution of each single-sensor error is used as the weight to perform weighted harmonic averaging on the errors. And sensors with smaller errors have higher credibility for the positioning result, so larger weights are assigned; conversely, sensors with larger errors are assigned smaller weights. In this way, the multi-sensor joint positioning error can comprehensively reflect the accuracy contributions of all observed sensors and optimize the positioning result.

[0041] Finally, a candidate accuracy set can be constructed based on the multi-sensor joint positioning error. This set contains the joint error values corresponding to all target material storage points. By selecting the minimum value in the candidate accuracy set as the positioning accuracy standard, it is ensured that the positioning errors of all target points in the warehouse environment do not exceed this threshold to meet the highest accuracy requirements.

[0042] By establishing a quantization model of single-sensor error through the Euclidean distance, realizing the fusion and optimization of multi-sensor errors using weighted covariance, and taking the minimum joint error as the benchmark for positioning accuracy, a complete technical chain of single-sensor modeling, multi-sensor collaboration, and accuracy standard establishment is formed. The above method fully considers the spatial relationship between the sensor and the target point, improves the reliability of the positioning result through the weight allocation mechanism, and finally provides a scientific accuracy evaluation basis for sensor placement optimization to ensure high-precision requirements are met under the premise of meeting engineering constraints.

[0043] Based on the improved cuckoo algorithm, import the three-dimensional warehousing environment model, the candidate sensor placement set, the target material storage point set, and the positioning accuracy standard. Construct an initial solution vector through binary coding, and iteratively update the initial solution vector through the fitness function and the corresponding dual placement objectives to generate an optimal sensor placement set, specifically including: Import the three-dimensional warehousing environment model, the candidate sensor placement set S, the target material storage point set T, and the positioning accuracy standard into the improved cuckoo algorithm; Adopt the binary coding to represent the placement selection result, where 1 means deploying the inventory sensor at the candidate sensor placement point, and 0 means not deploying the inventory sensor at the candidate sensor placement point, and construct the initial solution vector , where \(n\) represents the number of candidate sensor placements in the candidate sensor placement set S, and the solution space size of the initial solution vector X is ; ; Iteratively update the placement selection result in the initial solution vector X through the fitness function and the corresponding dual placement objectives until the preset convergence condition of the improved cuckoo algorithm is satisfied. The expressions of the fitness function and the corresponding dual placement objectives are as follows: In the formula, represents the fitness function value of the placement selection result ; represents the number of optimal sensor placements in the optimal sensor placement set A; represents the penalty coefficient; represents the multi-sensor joint positioning error corresponding to the placement selection result ; max means taking the maximum value. ;

[0044] The preset convergence condition of the improved cuckoo algorithm is that the fitness change degree of the fitness function value for continuous v generations is less than the preset threshold , and the corresponding expression is as follows: In the formula, represents the fitness function value of the u-th generation; represents the fitness function value of the (u - v)-th generation.

[0045] Among them, the preprocessed three-dimensional warehousing environment model, the candidate sensor placement set, the target material storage point set, and the positioning accuracy standard can be input into the improved cuckoo algorithm. Among them, the three-dimensional warehousing environment model includes spatial features such as shelf layout, aisle orientation, and obstacle distribution; the candidate sensor placement set is the set of positions that meet the installation conditions; the target material storage point set defines the material areas that need to be located; and the positioning accuracy standard stipulates the maximum allowable positioning error threshold.

[0046] Secondly, a binary coding strategy can be adopted to construct an initial solution vector. This coding method represents the selection status of candidate placements through binary values. Among them, the value 1 indicates that an inventory sensor is deployed at the corresponding position, and 0 indicates no deployment. The dimension of the initial solution vector is the same as the number of candidate placements, forming a solution space that contains all possible placement combinations, and its scale grows exponentially with the number of candidate placements. For example, if the number of candidate placements is 100, the size of the solution space is , covering all possible sensor deployment schemes.

[0047] In the iterative optimization stage, the initial solution vector is evaluated and updated through a fitness function. The design of the fitness function needs to balance the dual placement goals, that is, minimizing the number of sensor deployments to reduce costs while ensuring that the positioning errors of all target points meet the accuracy requirements. Specifically, the fitness function value consists of two parts: one is the number of currently selected sensors, which is used to measure the deployment cost; the other is the penalty term for exceeding the positioning error. This penalty term is calculated based on the difference between the multi-sensor joint positioning error and the accuracy standard. If the error of a certain target point exceeds the threshold, the penalty term will increase linearly with the excess amount. This design enables the improved cuckoo algorithm to preferentially select a scheme that not only meets the accuracy requirements but also minimizes the number of sensors.

[0048] It should be noted that the improved cuckoo algorithm updates the solution vector by simulating the cuckoo's egg-laying behavior and the host bird's expulsion mechanism, that is, using the Lévy flight mechanism to generate new placement combinations, simulating global search, and performing fine-tuning on the current optimal solution through local neighborhood search to improve the quality of the solution. The combination of the two search strategies enables the improved cuckoo algorithm to explore a broader solution space and perform in-depth optimization in the local area, avoiding falling into local optima.

[0049] The convergence condition of the improved cuckoo algorithm is set based on the change amplitude of the fitness function value. Specifically, when the change degree of the fitness function value for consecutive v generations is less than the preset threshold, the algorithm is considered to have converged. This condition can judge whether the algorithm has approached the optimal solution or fallen into a stagnant state by comparing the fitness function values of the current generation and v generations ago. If the convergence condition is met, the algorithm terminates and outputs the optimal sensor placement set; otherwise, it continues to iterate until the maximum number of iterations is reached.

[0050] By inputting through a digital model, representing the solution space with binary coding, quantifying the optimization objective with a fitness function, and enhancing the optimization efficiency with a hybrid search strategy, a deployment plan with the minimum number of sensors can be generated under the premise of meeting the positioning accuracy. Thus, the physical constraints, signal propagation characteristics, and optimization objectives of the warehousing environment can be integrated into a unified framework of an improved cuckoo algorithm. Through an adaptive iteration mechanism, multi-objective optimization under complex constraints can be achieved, providing a scientific solution for the sensor deployment of intelligent warehousing systems. In practical applications, it can significantly reduce the hardware cost, improve the positioning accuracy, adapt to the dynamic adjustment requirements of different-scale warehousing environments, and enhance the automation and intelligence level of warehousing management.

[0051] As Figure 2 shown, it is a system block diagram of an indoor material inventory and space positioning system based on an improved cuckoo algorithm provided by an embodiment of the present invention. The system includes: A sensor placement and material storage point determination module, configured to obtain an indoor warehousing environment, construct a three-dimensional warehousing environment model corresponding to the indoor warehousing environment by using three-dimensional laser scanning technology, and determine a candidate sensor placement set and a target material storage point set; An improved cuckoo algorithm setting module, configured to generate an improved cuckoo algorithm based on the standard cuckoo algorithm by introducing placement constraints, an error correction mechanism, a hybrid search optimization strategy, and an incremental node strategy; A positioning error and accuracy standard calculation module, configured to establish a single-sensor error model based on the Euclidean distance and calculate the contribution of the single-sensor positioning error, calculate the multi-sensor joint positioning error based on the contribution of the single-sensor error by using a weighted covariance formula, and determine the positioning accuracy standard based on the multi-sensor joint positioning error; An optimal sensor placement set generation module, configured to import the three-dimensional warehousing environment model, the candidate sensor placement set, the target material storage point set, and the positioning accuracy standard based on the improved cuckoo algorithm, construct an initial solution vector through binary coding, and iteratively update the initial solution vector through a fitness function and corresponding dual placement objectives to generate an optimal sensor placement set.

[0052] Figure 2 The device of the shown embodiment can correspondingly be used to execute Figure 1 the steps in the method embodiment shown, and its implementation principle and technical effects are similar, which will not be elaborated here.

[0053] An electronic device includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the steps of the indoor warehousing material inventory and space positioning method based on the improved cuckoo algorithm as described in any one of the above.

[0054] As shown Figure 3 in the figure, it is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32, and a computer program. Among them The memory 32 is used to store the computer program, and the memory can also be a flash memory. The computer program is, for example, an application program, a functional module, etc. that implement the above method.

[0055] The processor 31 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For specific details, please refer to the relevant descriptions in the foregoing method embodiments.

[0056] Optionally, the memory 32 can be either independent or integrated with the processor 31.

[0057] When the memory 32 is a device independent of the processor 31, the device may further include: A bus 33 for connecting the memory 32 and the processor 31.

[0058] A readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the steps of the indoor storage material inventory and space positioning method based on the improved cuckoo algorithm as described in any one of the above.

[0059] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0060] The present invention also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of the device can read the execution instructions from the readable storage medium, and the execution of the execution instructions by at least one processor enables the device to implement the methods provided by the above various embodiments.

[0061] In the embodiments of the above device, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, abbreviated: CPU), or may also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated: DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated: ASIC), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the present invention can be directly embodied as being completed by the execution of a hardware processor, or completed by a combination of hardware and software modules in the processor.

[0062] Through the introduction of the above embodiments, the present invention provides a method for indoor storage material inventory and space positioning based on an improved cuckoo algorithm. By obtaining the indoor storage environment, a three-dimensional storage environment model corresponding to the indoor storage environment is constructed using three-dimensional laser scanning technology, and a candidate sensor placement point set and a target material storage point set are determined; based on the standard cuckoo algorithm, a placement constraint condition, an error correction mechanism, a hybrid search optimization strategy, and an incremental node strategy are introduced to generate an improved cuckoo algorithm; a single-sensor error model is established based on the Euclidean distance and the single-sensor positioning error contribution is calculated, the multi-sensor joint positioning error is calculated based on the single-sensor error contribution using a weighted covariance formula, and the positioning accuracy standard is determined based on the multi-sensor joint positioning error; based on the improved cuckoo algorithm, the three-dimensional storage environment model, the candidate sensor placement point set, the target material storage point set, and the positioning accuracy standard are imported, an initial solution vector is constructed through binary coding, and the initial solution vector is iteratively updated through a fitness function and the corresponding dual placement target to generate an optimal sensor placement point set, thereby realizing the intelligent optimization of the sensor placement scheme, improving the efficiency and accuracy of material inventory, significantly reducing the equipment deployment cost on the premise of meeting the industry positioning accuracy requirements, and improving the automation and precision level of warehouse material management.

[0063] Through constructing a three-dimensional warehouse environment model, calculating the combined positioning error of multi-sensors and applying an improved cuckoo algorithm, and combining indoor positioning technology and intelligent recognition means, the present invention can optimize the number of sensors and the layout scheme, reduce the equipment deployment cost, and can realize the real-time positioning and automatic inventory of goods, reduce manual intervention, improve the long-term positioning stability, enhance the intelligent level of warehouse management, and promote the development of the enterprise's intelligent logistics. The method of the present invention can solve the problems of time-consuming and error-prone of the traditional manual inventory method, accurately position goods through intelligent inventory technology, realize real-time data synchronization, improve the accuracy of material inventory and space positioning, ensure that the positioning error is less than the industry standard, and enhance the accuracy and update efficiency of inventory information.

[0064] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An indoor storage material inventory and space positioning method based on an improved cuckoo algorithm, characterized in that The method includes: Obtain an indoor storage environment, construct a three-dimensional storage environment model corresponding to the indoor storage environment by using three-dimensional laser scanning technology, and determine a candidate sensor placement point set and a target material storage point set; Based on the standard cuckoo algorithm, introduce a placement constraint condition, an error correction mechanism, a hybrid search optimization strategy, and an incremental node strategy to generate an improved cuckoo algorithm; Establish a single-sensor error model based on the Euclidean distance and calculate the contribution of the single-sensor positioning error. Use the weighted covariance formula to calculate the multi-sensor joint positioning error based on the contribution of the single-sensor error, and determine the positioning accuracy standard based on the multi-sensor joint positioning error; Based on the improved cuckoo algorithm, import the three-dimensional storage environment model, the candidate sensor placement point set, the target material storage point set, and the positioning accuracy standard. Construct an initial solution vector through binary coding, and iteratively update the initial solution vector through the fitness function and the corresponding dual placement target to generate an optimal sensor placement point set.

2. The indoor warehousing material inventory and space positioning method based on the improved cuckoo algorithm according to claim 1, wherein The obtaining of the indoor storage environment, constructing the three-dimensional storage environment model corresponding to the indoor storage environment by using three-dimensional laser scanning technology, and determining the candidate sensor placement point set and the target material storage point set specifically include: Obtain the indoor storage environment, use the three-dimensional laser scanning technology to determine the physical structure of the indoor storage environment, and mark the shelf coordinates, the channel direction, and the obstacle area to generate the three-dimensional storage environment model; Based on the three-dimensional storage environment model, determine multiple sensor installation positions to form the candidate sensor placement point set, and mark the coordinates of the material storage area to form the target material storage point set.

3. The indoor warehousing material inventory and space positioning method based on the improved cuckoo algorithm according to claim 1, characterized in that The placement constraint conditions include signal occlusion rules, sensor installation height limits, and storage operation process constraints; the error correction mechanism calibrates the parameters of the single-sensor error model by establishing a real-time error feedback link and obtaining historical positioning data; the hybrid search optimization strategy includes a global search strategy and a local optimization strategy. The global search strategy explores the sensor placement scheme through the Levy flight mechanism, and the local optimization strategy introduces a neighborhood search operator to refine the sensor placement scheme; the incremental node strategy dynamically adjusts the search granularity according to the scale of the indoor storage environment.

4. The indoor storage material inventory and space positioning method based on the improved cuckoo algorithm according to claim 1, characterized in that, The establishing of the single-sensor error model based on the Euclidean distance and calculating the contribution of the single-sensor positioning error specifically include: Based on the single-sensor error model, calculate the candidate sensor placement points in the candidate sensor placement set S and the target material storage points in the target material storage point set T for the Euclidean distance : In the formula, represents the candidate sensor layout point and the target material storage point of the Euclidean distance; Based on the Euclidean distance , calculate the contribution of the single-sensor positioning error : In the formula, represents the distance attenuation coefficient; represents the basic error term.

5. The indoor storage material inventory and space positioning method based on the improved cuckoo algorithm according to claim 4, characterized in that, The using of the weighted covariance formula to calculate the multi-sensor joint positioning error based on the contribution of the single-sensor error and determining the positioning accuracy standard based on the multi-sensor joint positioning error specifically include: The target material storage point is simultaneously observed by the inventory sensors at k of the candidate sensor deployment points Based on the maximum likelihood estimation theory, calculate the single-sensor positioning error contribution of all the inventory sensors The weighted harmonic mean of which is the multi-sensor joint positioning error : Based on the multi-sensor combined positioning error Construct a set of candidate positioning accuracies , and select the minimum value in the set A of the candidate positioning accuracies as the positioning accuracy standard .

6. The indoor storage material inventory and space positioning method based on the improved cuckoo algorithm according to claim 5, characterized in that, The based on the improved cuckoo algorithm, importing the three-dimensional storage environment model, the candidate sensor placement point set, the target material storage point set, and the positioning accuracy standard, constructing an initial solution vector through binary coding, and iteratively updating the initial solution vector through the fitness function and the corresponding dual placement target to generate an optimal sensor placement point set specifically include: Import the three-dimensional warehousing environment model, the candidate sensor placement set S, the target material storage point set T, and the positioning accuracy standard into the improved cuckoo algorithm; Adopt the binary encoding to represent the result of the point selection, indicating to deploy the inventory sensor at the candidate sensor location, indicating not to deploy the inventory sensor at the candidate sensor location, and construct the initial solution vector , representing the number of candidate sensor locations in the candidate sensor location set S of the candidate sensor, and the solution space size of the initial solution vector X is ; Update the sampling point selection result in the initial solution vector X iteratively through the fitness function and the corresponding dual sampling point objective until the preset convergence condition of the improved cuckoo algorithm is satisfied. The expressions of the fitness function and the corresponding dual sampling point objective are as follows: In the formula, represents the result of the sensor placement selection of the fitness function value; represents the number of the optimal sensor placements in the optimal sensor placement set A; represents the penalty coefficient; represents the result of the sensor placement selection corresponding to the multi-sensor joint positioning error ; max represents taking the maximum value.

7. The indoor storage material inventory and space positioning method based on the improved cuckoo algorithm according to claim 6, characterized in that, The preset convergence condition of the improved cuckoo algorithm is that the fitness change degree of the fitness function values for consecutive v generations is less than a preset threshold , and the corresponding expression is as follows: In the formula, represents the fitness function value of the u-th generation; represents the fitness function value of the (u - v)-th generation.

8. An indoor material inventory and space positioning system based on an improved cuckoo algorithm is applied to the indoor warehousing material inventory and space positioning method based on the improved cuckoo algorithm as described in any one of claims 1-7, and is characterized in that, The system includes: A sensor layout and material storage point determination module, which is used to obtain the indoor storage environment, construct a three-dimensional storage environment model corresponding to the indoor storage environment by using three-dimensional laser scanning technology, and determine a candidate sensor layout set and a target material storage point set; An improved cuckoo algorithm setting module, which is used to generate an improved cuckoo algorithm based on the standard cuckoo algorithm by introducing layout constraints, an error correction mechanism, a hybrid search optimization strategy, and an incremental node strategy; A positioning error and accuracy standard calculation module, which is used to establish a single-sensor error model based on the Euclidean distance and calculate the contribution of single-sensor positioning error, calculate the multi-sensor joint positioning error based on the single-sensor error contribution by using a weighted covariance formula, and determine the positioning accuracy standard based on the multi-sensor joint positioning error; An optimal sensor layout set generation module, which is used to import the three-dimensional storage environment model, the candidate sensor layout set, the target material storage point set, and the positioning accuracy standard based on the improved cuckoo algorithm, construct an initial solution vector through binary coding, and iteratively update the initial solution vector through a fitness function and the corresponding dual layout objective to generate an optimal sensor layout set.

9. An electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that, When the processor runs the computer program stored in the memory, the processor executes the steps of the indoor storage material inventory and space positioning method based on the improved cuckoo algorithm according to any one of claims 1-7.

10. A readable storage medium storing a computer program therein, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the indoor storage material inventory and space positioning method based on the improved cuckoo algorithm according to any one of claims 1-7.

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