Electric power material operation equipment scheduling method, system, equipment and medium
By analyzing the load distribution and redistributing tasks of power material sorting and loading/unloading equipment, the problem of unbalanced equipment load was solved, balanced equipment load scheduling was achieved, and the efficiency and stability of power material operations were improved.
Patent Information
- Application Number
- CN202510964030.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-14
- Publication Date
- 2025-11-11
AI Technical Summary
The lack of refined analysis and dynamic adjustment in the scheduling of power material sorting and unloading equipment leads to uneven equipment load, resulting in overload or idleness, which affects the timeliness and economy of material supply.
By calculating cargo turnover frequency and equipment load distribution based on cargo flow data, identifying load peak and valley characteristics, implementing task redistribution, optimizing equipment load balancing scheduling, and combining cargo flow density and equipment processing capacity, equipment load balancing is achieved.
It improved equipment utilization efficiency, reduced equipment idleness and overload, optimized resource allocation in power material operation processes, and enhanced the stability and efficiency of the overall operation system.
Smart Images
Figure CN120930985A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power supply chain technology, and in particular to a method, system, equipment and medium for scheduling power supply operation equipment. Background Technology
[0002] In the power industry, the efficient flow of materials is crucial for ensuring the safe and stable operation of the power grid. Among these, the sorting and unloading of power materials, as a key node in the logistics process, directly impacts the timeliness and economy of material supply. With the expansion of the power grid and the increase in the variety of power materials, the scheduling efficiency of sorting and unloading equipment becomes increasingly important for the smooth operation of the overall logistics system.
[0003] Currently, the operation of power material sorting and unloading equipment relies heavily on traditional experience or fixed procedures for scheduling, lacking refined analysis and dynamic adjustment of equipment load fluctuations. On the one hand, due to significant temporal differences in power material demand—such as concentrated allocation of materials during peak summer demand and flood control repairs, or the random occurrence of scattered demands in routine maintenance—the turnover frequency of goods fluctuates greatly at different times, and the distribution of goods flow exhibits obvious peak and trough characteristics. On the other hand, existing scheduling methods fail to fully integrate the actual processing capacity and real-time operating status of the equipment, making it difficult to accurately match the volume of goods processed with the equipment load. This often results in an imbalance where equipment operates under overload conditions during some periods and is idle during others. This extensive scheduling model not only wastes equipment resources and increases energy consumption but may also increase the risk of equipment failure due to load imbalance, thereby affecting the efficiency of material sorting and unloading and delaying the supply of emergency materials.
[0004] Therefore, how to achieve dynamic optimization scheduling of power material sorting and unloading equipment has become a key issue in improving the efficiency of power material logistics and reducing operating costs. Summary of the Invention
[0005] To address the aforementioned problems, this invention provides a method, system, equipment, and medium for scheduling power material operation equipment.
[0006] In a first aspect, embodiments of the present invention provide a method for scheduling power material operation equipment, including: The cargo turnover frequency is calculated based on the cargo flow data of the target warehouse, and the equipment load distribution information is obtained by recursive analysis of the cargo turnover frequency. The equipment load distribution information is analyzed in a time series to obtain the peak and valley characteristics of the load during different time periods, and the ratio of the peak and valley characteristics of the load during different time periods is summarized to obtain the equipment utilization ratio during different time periods. The equipment time period utilization ratio is converted into a quantity ratio to obtain the equipment cargo time period utilization ratio, and the equipment cargo time period utilization ratio is compared with a threshold to obtain several inefficient time periods, wherein the inefficient time period is the time period in which the equipment cargo utilization ratio is less than a first threshold. Differential analysis is performed on the equipment operation data during each inefficient period to identify the load-imbalanced equipment during that period. Based on load matching benchmark data, the load-imbalanced equipment is task-reassigned to obtain an equipment load balancing scheduling strategy. The load matching benchmark data includes cargo flow distribution density and equipment rated processing capacity.
[0007] Preferably, after performing differential analysis on the equipment operation data for each inefficient period to obtain the corresponding load-imbalanced equipment for that inefficient period, and performing task reallocation on the load-imbalanced equipment based on load matching benchmark data to obtain an equipment load balancing scheduling strategy, the method further includes: By implementing the aforementioned equipment load balancing scheduling strategy, task allocation instruction execution data is obtained, and load balancing determination results are obtained by performing a load balancing verification on the task allocation instruction execution data.
[0008] Preferably, the step of calculating the cargo turnover frequency based on the cargo flow data of the target warehouse and performing recursive analysis on the cargo turnover frequency to obtain equipment load distribution information includes: Acquire the cargo flow data of the target warehouse and calculate the cargo turnover frequency based on the cargo flow data, wherein the cargo flow data includes the inbound and outbound quantities of different categories of goods within several time periods; Based on the cargo turnover frequency, the cargo throughput rate of each storage area in the target warehouse is calculated, and a threshold comparison is performed on each cargo throughput rate to obtain several high-flow channels, wherein the high-flow channels are the storage areas whose cargo throughput rate exceeds the second threshold. Determine the operating equipment cluster corresponding to each high-flow channel, and obtain the number of goods processed by each operating equipment in each operating equipment cluster during each time period; The load rate of each operating device in the corresponding time period is calculated based on the number of goods processed by each operating device in each time period, and the equipment load distribution information is obtained by summarizing the load rates of all operating devices in all time periods.
[0009] Preferably, the step of performing time-series analysis on the equipment load distribution information to obtain time-period load peak-valley characteristics, and summarizing the time-period load peak-valley characteristics to obtain the equipment time-period utilization ratio, includes: Perform trend analysis on the load rate of each working device in each time period in the equipment load distribution information to obtain the corresponding load peak and valley periods of the working device; The utilization rate of each operating device during each peak and off-peak period is calculated based on the volume of goods handled by each operating device during each peak and off-peak period. The equipment time-period utilization rate is obtained by summing the utilization rates of all the aforementioned operating equipment during all the aforementioned time periods.
[0010] Preferably, the step of converting the equipment time-period utilization ratio into a quantity ratio to obtain the equipment cargo time-period utilization ratio, and then comparing the equipment cargo time-period utilization ratio with a threshold to obtain several inefficient time periods, includes: The ratio of the utilization rate of each working equipment in each time period to the cargo processing quantity is calculated to obtain the equipment cargo utilization rate of the corresponding working equipment in the corresponding time period. The equipment cargo utilization rate for all said operating equipment is obtained by summing up the equipment cargo utilization rate for all said operating equipment in all said time periods; The utilization rate of each operating device in each time period is compared with a first threshold to obtain several inefficient time periods where the utilization rate of the equipment is less than the first threshold.
[0011] Preferably, the step of performing differential analysis on the equipment operation data during each inefficient period to obtain the corresponding load-imbalanced equipment during the inefficient period, and then performing task reallocation on the load-imbalanced equipment based on load matching benchmark data to obtain an equipment load balancing scheduling strategy, includes: Acquire equipment operation data for each of the aforementioned inefficient periods, the equipment operation data including load rate; Perform load rate difference calculation on all operating equipment within each inefficient period to obtain the load rate difference set corresponding to the inefficient period; A threshold comparison is performed on the set of load rate differences for each inefficient period to obtain the load imbalance equipment corresponding to the inefficient period; Based on the cargo flow distribution density and the rated processing capacity of the equipment, the load imbalance equipment is redistributed to obtain an equipment load balancing scheduling strategy, wherein the equipment load balancing scheduling strategy includes the working period and the amount of tasks allocated.
[0012] Preferably, the step of obtaining task allocation instruction execution data by implementing the equipment load balancing scheduling strategy, and obtaining a load balancing determination result by performing a load balancing degree check on the task allocation instruction execution data, includes: Based on the equipment load balancing scheduling strategy, a corresponding task allocation instruction is generated, and the task allocation instruction execution data is obtained by executing the task allocation instruction through the load imbalance equipment. The load balance degree of the load imbalance equipment is obtained by calculating the standard deviation of the task allocation instruction execution data, and the load balance degree is compared with the preset balance standard value to obtain the load balance degree judgment result.
[0013] Secondly, embodiments of the present invention provide a power material operation equipment dispatching system, comprising: The load distribution determination module is used to calculate the cargo turnover frequency based on the cargo flow data of the target warehouse, and to perform recursive analysis on the cargo turnover frequency to obtain equipment load distribution information. The ratio aggregation module is used to perform time-series analysis on the equipment load distribution information to obtain the peak and valley characteristics of the load during the time period, and to perform ratio aggregation on the peak and valley characteristics of the load during the time period to obtain the equipment utilization ratio during the time period. The threshold comparison module is used to convert the equipment time period utilization ratio into a quantity ratio to obtain the equipment cargo time period utilization ratio, and to compare the equipment cargo time period utilization ratio with a threshold to obtain a number of inefficient time periods, wherein the inefficient time period is the time period in which the equipment cargo utilization ratio is less than a first threshold. The scheduling strategy determination module is used to perform differential analysis on the equipment operation data in each inefficient period to obtain the load imbalance equipment in the corresponding inefficient period, and to perform task redistribution on the load imbalance equipment based on load matching benchmark data to obtain the equipment load balancing scheduling strategy. The load matching benchmark data includes cargo flow distribution density and equipment rated processing capacity.
[0014] Thirdly, embodiments of the present invention provide a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power material operation equipment scheduling method as described above.
[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power material operation equipment scheduling method as described above.
[0016] Compared with existing technologies, the present invention provides a method, system, equipment, and medium for scheduling power material operations. Its advantages include: Based on the recursive load distribution derived from cargo turnover frequency, combined with time-series analysis and peak-valley feature extraction, it accurately locates the load fluctuation patterns of equipment, providing data support for scheduling strategies; by using ratio conversion and threshold screening to identify inefficient periods, it redistributes tasks for equipment with load imbalances based on cargo distribution density and rated processing capacity, effectively improving equipment utilization efficiency; and by using data-driven load balancing scheduling, it reduces equipment idleness and overload operation, optimizes resource allocation in power material operations, and enhances the stability and efficiency of the overall operating system. This invention achieves closed-loop management from load characteristic identification to dynamic scheduling, providing reliable technical support for the intelligent and refined scheduling of power material warehousing operations. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating a method for scheduling power material operation equipment according to an embodiment of the present invention; Figure 2 This is another flowchart illustrating a method for scheduling power material operation equipment according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a power material operation equipment dispatching system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a terminal device according to an embodiment of the present invention; Figure label: 1. Load distribution determination module; 2. Ratio summarization module; 3. Threshold comparison module; 4. Scheduling strategy determination module; 5000. Terminal device; 5001. Processor; 5002. Bus; 5003. Memory; 5004. Transceiver. Detailed Implementation
[0018] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.
[0019] In the description of this invention, it should be understood that the terms "first" and "second," etc., are used to distinguish different objects, rather than to describe a specific order.
[0020] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by those skilled in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0021] like Figure 1 The diagram shown is a flowchart illustrating a method for scheduling power material operation equipment according to an embodiment of the present invention. (Refer to...) Figure 1 An embodiment of the present invention provides a method for scheduling power material operation equipment, comprising the following steps: S1. Calculate the cargo turnover frequency based on the cargo flow data of the target warehouse, and perform recursive analysis on the cargo turnover frequency to obtain equipment load distribution information; Specifically, step S1 includes: 1) Obtain the cargo flow data of the target warehouse and calculate the cargo turnover frequency based on the cargo flow data; The logistics management system retrieves the flow data of the target warehouse, which includes the inbound and outbound quantities of different categories of goods over several time periods. Furthermore, the data includes the inbound and outbound times of each item. By calculating the average dwell time of the same category of goods, the turnover rate of different goods can be reflected. Dividing the total inbound and outbound volume per unit time by the average dwell time yields the turnover frequency of each type of goods in each time period.
[0022] 2) Calculate the throughput rate of each storage area in the target warehouse based on the cargo turnover frequency, and compare the throughput rate of each cargo flow with thresholds to obtain several high-flow channels. High-flow channels are storage areas where the throughput rate exceeds a second threshold. Specifically, based on the cargo turnover frequency, the throughput rate of each storage area in the target warehouse is calculated. Then, the throughput rate of each storage area is compared with a preset second threshold to filter out storage areas whose throughput rate exceeds the threshold. These are defined as high-flow channels, thereby accurately identifying key areas of concentrated cargo flow within the target warehouse. The second threshold can be set comprehensively based on the historical cargo throughput rate distribution characteristics of the target warehouse and the baseline value of the storage area's carrying capacity.
[0023] 3) Determine the operating equipment cluster corresponding to each high-flow channel, and obtain the cargo processing quantity of each operating equipment in each operating equipment cluster in each time period. The system retrieves the corresponding equipment clusters for each high-traffic channel from the logistics management system, and obtains the cargo processing volume of each equipment within each equipment cluster in each time period. It should be noted that an equipment cluster includes several equipment units, including but not limited to sorting equipment and loading / unloading equipment.
[0024] 4) Calculate the load rate of each operating equipment in each time period based on the amount of goods processed by each operating equipment in each time period, and obtain the equipment load distribution information by summarizing the load rates of all operating equipment in all time periods.
[0025] First, based on the quantity of goods processed by each operating device in each time period, the load rate of the corresponding operating device in the corresponding time period is calculated. Then, by aggregating the load rate data of all operating devices in all time periods, the equipment load distribution information is finally formed, thus comprehensively presenting the load status of each operating device in different time periods. Specifically, the load rate of the corresponding operating device in the corresponding time period is obtained by dividing the quantity of goods processed by each operating device in each time period by the actual processing capacity of the device. This embodiment forms complete load distribution information by aggregating the load rate change data of all operating devices within 24 hours (in hourly time periods). This distribution information not only reflects the operating status of the equipment but also provides an important basis for subsequent scheduling strategies.
[0026] S2. Perform time-series analysis on the equipment load distribution information to obtain the peak and valley characteristics of the load during the time period, and summarize the ratio of the peak and valley characteristics of the load during the time period to obtain the equipment utilization ratio during the time period; Specifically, step S2 includes: 1) Perform trend analysis on the load rate of each working device in each time period in the equipment load distribution information to obtain the peak and valley periods of the corresponding working device; This embodiment arranges the load rate data of each operating device in the equipment load distribution information over 24 hours into a time series on an hourly basis, calculates the load rate difference between adjacent time periods, and identifies the load rise and fall periods by the positive or negative change of the difference. When the load rate continuously rises for a preset number of time periods and reaches a preset threshold, it is marked as the peak start point; when it continuously falls below the preset threshold, it is marked as the trough start point, thus obtaining the load peak and trough periods for each operating device. The preset number of time periods and the preset threshold can be determined comprehensively based on the historical load fluctuation cycle characteristics of the operating device and the benchmark value of operating efficiency.
[0027] 2) Calculate the utilization rate of each operating device during the corresponding peak and valley periods based on the amount of goods handled by each operating device during each peak and valley period; The utilization rate of each operating device during peak and off-peak periods is obtained by dividing the cargo handling volume of each operating device during each peak and off-peak period by the product of the rated handling capacity of the device and the effective operating time during that period.
[0028] 3) By summarizing the utilization rates of all operating equipment in all time periods, the equipment time period utilization rate is obtained.
[0029] S3. Calculate the equipment time period utilization ratio by converting it into a quantity ratio to obtain the equipment cargo time period utilization ratio, and compare the equipment cargo time period utilization ratio with a threshold to obtain several inefficient time periods. Specifically, step S3 includes: 1) Calculate the ratio of the utilization rate of each working equipment in each time period to the quantity of goods processed in the equipment time period utilization ratio, and obtain the equipment and cargo utilization ratio of the corresponding working equipment in the corresponding time period. It's important to note that the calculation of equipment utilization rate reveals the essential characteristics of operational equipment efficiency. This rate, obtained by dividing the equipment utilization rate by the number of goods processed, reflects the degree of equipment resource occupancy required to process a unit of goods. For example, when the equipment utilization rate is 80%, corresponding to a goods processing quantity of 400 units, the calculated equipment utilization rate is 0.2% / unit, meaning that processing each unit of goods utilizes 0.2% of the equipment's capacity. The importance of this indicator lies in its elimination of the impact of fluctuations in goods volume on equipment efficiency assessment, truly reflecting the equipment's processing efficiency.
[0030] 2) By summarizing the equipment cargo utilization rate of all operating equipment in all time periods, the equipment cargo utilization rate for each time period is obtained; 3) Compare the equipment utilization rate of each working equipment in each time period with the first threshold to obtain several inefficient time periods where the equipment utilization rate is less than the first threshold.
[0031] Inefficient periods are identified using a threshold-based mechanism. The first threshold is typically determined based on historical operational data and industry standards, representing the minimum acceptable efficiency level. When the equipment utilization rate falls below this threshold, it indicates abnormally low equipment processing efficiency. In other words, inefficient periods are those where the equipment utilization rate is less than the first threshold. For example, under normal circumstances, the equipment utilization rate is 0.15% / unit to 0.25% / unit, but during a certain period it drops to 0.05% / unit, far below the first threshold of 0.1% / unit; this period is then marked as an inefficient period.
[0032] S4. Perform differential analysis on the equipment operation data in each inefficient period to obtain the load imbalance equipment in the corresponding inefficient period, and perform task redistribution on the load imbalance equipment based on the load matching benchmark data to obtain the equipment load balancing scheduling strategy.
[0033] Specifically, step S4 includes: 1) Obtain equipment operation data during each inefficient period; Equipment operation data includes load rate, as well as the quantity of goods processed, utilization rate, and runtime. By obtaining detailed equipment operation data during these inefficient periods, it's possible to deeply analyze the specific reasons for inefficiency. This could be due to insufficient goods supply causing equipment idling, equipment malfunction leading to decreased processing speed, or unreasonable work processes causing excessive waiting times. This precise data provides a quantitative basis for subsequent operational optimization, helping to develop targeted improvement measures and enhance overall equipment utilization efficiency.
[0034] 2) Perform load rate difference calculation on all operating equipment in each inefficient period to obtain the set of load rate difference values for the corresponding inefficient period; For each inefficient period, the load rate difference between the operating equipment during that period is collected and a set is formed, namely the load rate difference set.
[0035] 3) Perform threshold comparison on the load rate difference set for each inefficient period to obtain the load imbalance equipment for the corresponding inefficient period; By comparing each difference in the load rate difference set with a preset threshold, devices with significant load imbalances (load rate differences greater than the preset threshold) are screened out, thus identifying the load-imbalanced devices during the corresponding inefficient periods and providing clear targets for subsequent task reallocation. The preset threshold can be determined based on the difference in rated processing capacity of the devices and the fluctuation range of the difference under historical load balancing conditions. In this embodiment, the preset threshold is set to 50%. For example, if device A has a load rate of 10%, while device B has a load rate as high as 95% during the same period, the 85% load rate difference between the two far exceeds the 50% preset threshold, clearly indicating a severe imbalance in load distribution. Both are thus marked as load-imbalanced devices.
[0036] 4) Based on the cargo flow distribution density and the rated processing capacity of the equipment, the tasks of the unbalanced equipment are redistributed to obtain the equipment load balancing scheduling strategy.
[0037] Load matching baseline data includes cargo flow distribution density and equipment rated processing capacity. Specifically, based on the distribution density of cargo flow in different storage areas and time periods, and combined with the rated processing capacity of each unbalanced equipment, the workload of high-load and idle equipment is redistributed. The start and end times of operation and the proportion of cargo processing volume undertaken by each equipment are adjusted, ultimately forming an equipment load balancing scheduling strategy that includes the operating time periods and allocated workload of each equipment, thereby achieving load balancing. In other words, the equipment load balancing scheduling strategy includes operating time periods and allocated workload.
[0038] It's important to note that the task redistribution process fully considers the differences in equipment processing capacity. Based on the rated processing capacity and current load of each piece of equipment, the amount of tasks that can be transferred is calculated. For example, a high-load device processes 600 items per hour, approaching its rated capacity of 700 items, while an idle device, with a rated capacity of 500 items, is only processing 50. By transferring 200 tasks from the high-load device to the idle device, the load rates of both devices are kept within a reasonable range. The adjusted equipment operation plan, i.e., the equipment load balancing scheduling strategy, includes detailed execution elements. Each piece of equipment has a clearly defined operating time slot; for example, the idle device's operation time is adjusted from 8 hours to 10 hours, increasing peak-hour operating time. The allocated task volume is also precisely calculated to ensure that the processing capacity of each piece of equipment matches its capacity. This equipment load balancing scheduling strategy not only improves equipment utilization but also extends equipment lifespan, achieving an overall improvement in the efficiency of the operating system.
[0039] like Figure 2 The diagram shown is another flowchart illustrating a method for scheduling power material operation equipment according to an embodiment of the present invention. (Refer to...) Figure 2 According to an embodiment of the present invention, a method for scheduling power material operation equipment includes, after step S4, step S5: obtaining task allocation instruction execution data by implementing an equipment load balancing scheduling strategy, and performing load balancing verification on the task allocation instruction execution data to obtain a load balancing determination result.
[0040] Specifically, step S5 includes: 1) Generate corresponding task allocation instructions based on the equipment load balancing scheduling strategy, and obtain task allocation instruction execution data by executing the task allocation instructions through the load imbalance equipment; Based on the equipment load balancing scheduling strategy, task allocation instructions containing equipment number, operation period and assigned task quantity are generated. These instructions are then sent to the corresponding unbalanced equipment, which executes the operation tasks according to the instructions. During this process, information such as the actual running time of the equipment and the quantity of goods processed is collected to form task allocation instruction execution data, which provides a valid basis for subsequent evaluation of the scheduling strategy's effectiveness.
[0041] 2) Calculate the standard deviation of the task allocation instruction execution data to obtain the load balance of the unbalanced equipment, and compare the load balance with the preset balance standard value to obtain the load balance judgment result.
[0042] Based on the task allocation instruction execution data, the load balance is calculated by determining the standard deviation of the change in the resource utilization ratio of each unbalanced device. The obtained load balance is then compared with a preset balance standard value to determine whether the load balance among the devices meets the standard, thus forming the load balance judgment result. Generally, when the standard deviation is controlled within 5%, the load distribution among the devices can be considered relatively balanced.
[0043] If the resource utilization rate reaches the preset balance standard (standard deviation less than the preset balance standard value), i.e., the load balance meets the standard, it indicates that the load balance among the equipment has reached an ideal state. This quantitative judgment method avoids the uncertainty of subjective assessment and provides a reliable basis for subsequent optimization decisions.
[0044] If the resource utilization rate fails to reach the preset balance standard (standard deviation greater than the preset balance standard value), i.e., the load balance is not up to standard, then the equipment load fluctuations during inefficient periods are re-analyzed, and the priority of task allocation instructions is adjusted according to the urgency of cargo processing and equipment availability. Specifically, firstly, the load rate data of equipment during inefficient periods is re-analyzed. By calculating the load rate change amplitude between adjacent periods, statistically analyzing the fluctuation frequency and duration, equipment with frequent load fluctuations and stable operation is identified. Secondly, the urgency is classified according to the cargo delivery deadline, and the availability level is determined by combining the current running time of the equipment and the number of historical failures. Finally, according to the principle of prioritizing the allocation of urgent cargo to high-availability equipment and allocating ordinary cargo to other equipment, the priority of task allocation instructions is adjusted to achieve dynamic optimization of the scheduling strategy.
[0045] This invention discloses a method for scheduling power material handling equipment. Based on the recursive load distribution derived from cargo turnover frequency, and combined with time-series analysis and peak-valley feature extraction, it accurately locates the load fluctuation patterns of equipment, providing data support for scheduling strategies. By using ratio conversion and threshold screening to identify inefficient periods, and for equipment with load imbalance, it implements task reallocation based on cargo distribution density and rated processing capacity, effectively improving equipment utilization efficiency. Data-driven load balancing scheduling reduces equipment idleness and overload operation, optimizes resource allocation in power material handling processes, and enhances the stability and efficiency of the overall operating system. This invention achieves closed-loop management from load characteristic identification to dynamic scheduling, providing reliable technical support for intelligent and refined scheduling of power material warehousing operations.
[0046] like Figure 3 The diagram shown is a structural schematic of a power material operation equipment dispatching system according to an embodiment of the present invention. (Refer to...) Figure 3 An embodiment of the present invention provides a power material operation equipment dispatching system, comprising: Load distribution determination module 1 is used to calculate the cargo turnover frequency based on the cargo flow data of the target warehouse, and to perform recursive analysis on the cargo turnover frequency to obtain equipment load distribution information; The ratio aggregation module 2 is used to perform time-series analysis on the equipment load distribution information to obtain the peak and valley characteristics of the load during the time period, and to perform ratio aggregation on the peak and valley characteristics of the load during the time period to obtain the equipment utilization ratio during the time period. The threshold comparison module 3 is used to convert the equipment time period utilization ratio into a quantity ratio to obtain the equipment cargo time period utilization ratio, and to compare the equipment cargo time period utilization ratio with a threshold to obtain several inefficient time periods. The inefficient time period is the time period in which the equipment cargo utilization ratio is less than the first threshold. The scheduling strategy determination module 4 is used to perform differential analysis on the equipment operation data in each inefficient period to obtain the load imbalance equipment in the corresponding inefficient period, and to perform task redistribution on the load imbalance equipment based on the load matching benchmark data to obtain the equipment load balancing scheduling strategy. The load matching benchmark data includes cargo flow distribution density and equipment rated processing capacity.
[0047] It should be noted that each module in the aforementioned power material operation equipment dispatching system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module. For specific limitations regarding the power material operation equipment dispatching system, please refer to the limitations regarding the power material operation equipment dispatching method described above; both have the same function and role, and will not be repeated here.
[0048] This invention also provides a terminal device, which includes: Processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is configured to execute operations corresponding to the power material operation equipment scheduling method described above by invoking the operation instructions.
[0049] In one alternative embodiment, a terminal device is provided, such as Figure 4 As shown, Figure 4The terminal device 5000 shown includes a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the terminal device 5000 may also include a transceiver 5004. It should be noted that in practical applications, the transceiver 5004 is not limited to one type, and the structure of this terminal device 5000 does not constitute a limitation on the embodiments of the present invention.
[0050] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 5001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
[0051] Bus 5002 may include a path for transmitting information between the aforementioned components. Bus 5002 may be a PCI bus or an EISA bus, etc. Bus 5002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0052] The memory 5003 may be a ROM or other type of static storage device capable of storing static information and instructions, RAM or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.
[0053] The memory 5003 is used to store application code that executes the present invention, and its execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.
[0054] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for scheduling power material operation equipment.
[0055] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.
[0056] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0058] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0059] In summary, this invention provides a method, system, equipment, and medium for scheduling power material handling equipment. Based on the recursive load distribution derived from cargo turnover frequency, combined with time-series analysis and peak-valley feature extraction, it accurately identifies equipment load fluctuation patterns, providing data support for scheduling strategies. By using ratio conversion and threshold filtering to identify inefficient periods, and for equipment with load imbalance, it implements task reallocation based on cargo distribution density and rated processing capacity, effectively improving equipment utilization efficiency. Data-driven load balancing scheduling reduces equipment idleness and overload operation, optimizes resource allocation in power material handling processes, and enhances the stability and efficiency of the overall operating system. This invention achieves closed-loop management from load characteristic identification to dynamic scheduling, providing reliable technical support for intelligent and refined scheduling of power material warehousing operations.
[0060] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.
[0061] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.
Claims
1. A method for scheduling power material operation equipment, characterized in that, include: The cargo turnover frequency is calculated based on the cargo flow data of the target warehouse, and the equipment load distribution information is obtained by recursive analysis of the cargo turnover frequency. The equipment load distribution information is analyzed in a time series to obtain the peak and valley characteristics of the load during different time periods, and the ratio of the peak and valley characteristics of the load during different time periods is summarized to obtain the equipment utilization ratio during different time periods. The equipment time period utilization ratio is converted into a quantity ratio to obtain the equipment cargo time period utilization ratio, and the equipment cargo time period utilization ratio is compared with a threshold to obtain several inefficient time periods, wherein the inefficient time period is the time period in which the equipment cargo utilization ratio is less than a first threshold. Differential analysis is performed on the equipment operation data during each inefficient period to identify the load-imbalanced equipment during that period. Based on load matching benchmark data, the load-imbalanced equipment is task-reassigned to obtain an equipment load balancing scheduling strategy. The load matching benchmark data includes cargo flow distribution density and equipment rated processing capacity.
2. The method for scheduling power material operation equipment according to claim 1, characterized in that, After performing differential analysis on the equipment operation data for each inefficient period to obtain the corresponding load-disrupted equipment for that inefficient period, and performing task reallocation on the load-disrupted equipment based on load matching benchmark data to obtain an equipment load balancing scheduling strategy, the method further includes: By implementing the aforementioned equipment load balancing scheduling strategy, task allocation instruction execution data is obtained, and load balancing determination results are obtained by performing a load balancing verification on the task allocation instruction execution data.
3. The method for scheduling power material operation equipment according to claim 1, characterized in that, The calculation of cargo turnover frequency based on the cargo flow data of the target warehouse, and the recursive analysis of the cargo turnover frequency to obtain equipment load distribution information, includes: Acquire the cargo flow data of the target warehouse and calculate the cargo turnover frequency based on the cargo flow data, wherein the cargo flow data includes the inbound and outbound quantities of different categories of goods within several time periods; Based on the cargo turnover frequency, the cargo throughput rate of each storage area in the target warehouse is calculated, and a threshold comparison is performed on each cargo throughput rate to obtain several high-flow channels, wherein the high-flow channels are the storage areas whose cargo throughput rate exceeds the second threshold. Determine the operating equipment cluster corresponding to each high-flow channel, and obtain the number of goods processed by each operating equipment in each operating equipment cluster during each time period; The load rate of each operating device in the corresponding time period is calculated based on the number of goods processed by each operating device in each time period, and the equipment load distribution information is obtained by summarizing the load rates of all operating devices in all time periods.
4. The method for scheduling power material operation equipment according to claim 1, characterized in that, The process of performing time-series analysis on the equipment load distribution information to obtain time-period load peak and valley characteristics, and summarizing the ratios of the time-period load peak and valley characteristics to obtain the equipment time-period utilization ratio, includes: Perform trend analysis on the load rate of each working device in each time period in the equipment load distribution information to obtain the corresponding load peak and valley periods of the working device; The utilization rate of each operating device during each peak and off-peak period is calculated based on the volume of goods handled by each operating device during each peak and off-peak period. The equipment time-period utilization rate is obtained by summing the utilization rates of all the aforementioned operating equipment during all the aforementioned time periods.
5. The method for scheduling power material operation equipment according to claim 1, characterized in that, The equipment time-period utilization ratio is converted into a quantity ratio to obtain the equipment cargo time-period utilization ratio, and a threshold comparison is performed on the equipment cargo time-period utilization ratio to obtain several inefficient time periods, including: The ratio of the utilization rate of each working equipment in each time period to the cargo processing quantity is calculated to obtain the equipment cargo utilization rate of the corresponding working equipment in the corresponding time period. The equipment cargo utilization rate for all said operating equipment is obtained by summing up the equipment cargo utilization rate for all said operating equipment in all said time periods; The utilization rate of each operating device in each time period is compared with a first threshold to obtain several inefficient time periods where the utilization rate of the equipment is less than the first threshold.
6. The method for scheduling power material operation equipment according to claim 1, characterized in that, The step involves performing differential analysis on the equipment operation data during each inefficient period to identify the load-imbalanced equipment within that period, and then redistributing tasks to the load-imbalanced equipment based on load matching benchmark data to obtain an equipment load balancing scheduling strategy, including: Acquire equipment operation data for each of the aforementioned inefficient periods, the equipment operation data including load rate; Perform load rate difference calculation on all operating equipment within each inefficient period to obtain the load rate difference set corresponding to the inefficient period; A threshold comparison is performed on the set of load rate differences for each inefficient period to obtain the load imbalance equipment corresponding to the inefficient period; Based on the cargo flow distribution density and the rated processing capacity of the equipment, the load imbalance equipment is redistributed to obtain an equipment load balancing scheduling strategy, wherein the equipment load balancing scheduling strategy includes the working period and the amount of tasks allocated.
7. The method for scheduling power material operation equipment according to claim 2, characterized in that, The process of obtaining task allocation instruction execution data by implementing the equipment load balancing scheduling strategy, and then performing load balancing verification on the task allocation instruction execution data to obtain a load balancing determination result, includes: Based on the equipment load balancing scheduling strategy, a corresponding task allocation instruction is generated, and the task allocation instruction execution data is obtained by executing the task allocation instruction through the load imbalance equipment. The load balance degree of the load imbalance equipment is obtained by calculating the standard deviation of the task allocation instruction execution data, and the load balance degree is compared with the preset balance standard value to obtain the load balance degree judgment result.
8. A power material operation equipment dispatching system, characterized in that, include: The load distribution determination module is used to calculate the cargo turnover frequency based on the cargo flow data of the target warehouse, and to perform recursive analysis on the cargo turnover frequency to obtain equipment load distribution information. The ratio aggregation module is used to perform time-series analysis on the equipment load distribution information to obtain the peak and valley characteristics of the load during the time period, and to perform ratio aggregation on the peak and valley characteristics of the load during the time period to obtain the equipment utilization ratio during the time period. The threshold comparison module is used to convert the equipment time period utilization ratio into a quantity ratio to obtain the equipment cargo time period utilization ratio, and to compare the equipment cargo time period utilization ratio with a threshold to obtain a number of inefficient time periods, wherein the inefficient time period is the time period in which the equipment cargo utilization ratio is less than a first threshold. The scheduling strategy determination module is used to perform differential analysis on the equipment operation data in each inefficient period to obtain the load imbalance equipment in the corresponding inefficient period, and to perform task redistribution on the load imbalance equipment based on load matching benchmark data to obtain the equipment load balancing scheduling strategy. The load matching benchmark data includes cargo flow distribution density and equipment rated processing capacity.
9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power material operation equipment scheduling method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power material operation equipment scheduling method as described in any one of claims 1 to 7.