Charging control method and system of storage robot and medium

Abstract

The invention relates to a charging control method and system of a storage robot and a medium, and belongs to the technical field of charging control of storage robots. According to the impact current information when the current charging power supply is in contact with the storage robot and the storage robot charging cloud knowledge graph, the probability value that the contact line is abnormal is determined, and finally the storage robot which is charged abnormally is identified based on the probability value that the contact line is abnormal. And control strategy optimization is carried out on the storage robot which is charged abnormally. According to the invention, the technical problems of circuit contact overheating, fire hazard and other safety accidents are avoided by fully considering that a circuit is greatly impacted at the moment when a connector or an electrode plate of a robot is inserted into a power supply and the contact surface of the connector or the electrode plate of the robot is easily corroded due to overlarge impact current; and the use safety of the storage robot is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of storage robot charging control, and in particular to a storage robot charging control method, system and medium. Background Art

[0002] With the widespread application of factory automation, flexible manufacturing systems, and automated warehouses, mobile storage robots, as an important configuration for connecting and regulating the continuity of logistics management system operations, have seen rapid development in their scope of application and technical level. Their charging technologies are mainly divided into full-cycle charging and random charging. Full-cycle charging means that the storage robot exits service and enters a designated charging area, and can only be charged when the battery charge drops to a specified range. Random charging aims to charge at any time at each docking station without time restrictions, ensuring that the energy lost by the storage robot during operation is replenished in a timely manner, achieving shallow charging and shallow discharge of the battery, which will greatly extend the battery's service life. Traditional storage robot charging methods still have shortcomings and deficiencies:

[0003] (1) Electricity safety issues: Since the power output end of the power supply is always energized, the robot's charging circuit will also have a certain capacitive load, which will cause a large impact circuit when the robot's connector or electrode is inserted into the power supply. Excessive impact current may easily cause corrosion to the contact surface of the robot's connector or electrode. The charging process may cause safety accidents such as overheating of the circuit contacts and fire.

[0004] (2) The charging lines are complex, the configuration cost is high, and the maintenance is inconvenient: For conventional robot charging solutions, a communication protocol needs to be selected between the warehouse robot and the charging pile, and a communication docking system needs to be configured so that the robot can exchange data and control commands with the charging pile. Communication requires relying on wired or wireless networks, and it cannot be used normally in some complex network noise environments or disconnected network environments.

[0005] (3) Unable to charge due to abnormal low power: When charging a robot, automatic charging is generally used. However, the robot battery may be abnormally low power due to excessive energy consumption or battery aging, and the normal automatic charging method cannot be used to complete the charging. Summary of the Invention

[0006] The present invention overcomes the deficiencies of the prior art and provides a charging control method, system and medium for a storage robot.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A first aspect of the present invention provides a charging control method for a storage robot, comprising the following steps:

[0009] collecting inrush current information each time a charging power source comes into contact with a storage robot, and processing the inrush current information when the charging power source comes into contact with the storage robot to obtain processed inrush current information;

[0010] Constructing a cloud-based knowledge graph for charging a warehouse robot based on the processed inrush current information;

[0011] Collect the inrush current information fed back by the current charging power source when it comes into contact with the warehouse robot, and determine the probability value of an abnormality in the contact line based on the inrush current information when the current charging power source and the warehouse robot come into contact and the warehouse robot charging cloud knowledge graph;

[0012] Based on the probability value of the abnormality generated in the contact line, the abnormally charged storage robot is identified, and the control strategy of the abnormally charged storage robot is optimized.

[0013] Furthermore, in the charging control method of the storage robot, the inrush current information when the charging power source and the storage robot come into contact is processed to obtain the processed inrush current information, which specifically includes:

[0014] Constructing a time collection window, collecting the impact current information of the charging power source and the storage robot when they come into contact in each time stamp within the time collection window, and calculating the change amplitude characteristic data of the impact current information in adjacent time stamps;

[0015] Setting a change amplitude threshold, introducing a local outlier detection algorithm, using the change amplitude threshold as a detection criterion for the local outlier detection algorithm, and calculating whether the change amplitude characteristic data of the inrush current information in adjacent timestamps is greater than the change amplitude threshold;

[0016] Detecting the change amplitude characteristic data of the inrush current information within the adjacent timestamps by using the local outlier detection algorithm, and marking the data nodes where the change amplitude characteristic data within the time acquisition window is greater than the change amplitude threshold;

[0017] The data nodes where the variation amplitude characteristic data is greater than the variation amplitude threshold are deleted from the time acquisition window to obtain processed inrush current information.

[0018] Furthermore, in the charging control method of the warehouse robot, a cloud-based knowledge graph for charging the warehouse robot is constructed based on the processed inrush current information, specifically including:

[0019] Obtaining the number of times the contact line generates an abnormality and the number of times the contact line does not generate an abnormality under the processed impulse current information, counting the number of times the contact line generates an abnormality and the number of times the contact line does not generate an abnormality, and calculating the probability value of the contact line generating an abnormality under the processed impulse current information;

[0020] Construct a cloud-based knowledge graph for charging a storage robot based on the probability value of the contact line generating anomalies under the processed inrush current information, using the processed inrush current information as the first graph node and the probability value of the contact line generating anomalies as the second graph node;

[0021] constructing an adjacency matrix based on the first graph nodes and the second graph nodes, and synchronously transmitting the adjacency matrix through a data link;

[0022] After data synchronization transmission, the adjacency matrix is ​​input into the warehouse robot charging cloud knowledge graph for storage.

[0023] Furthermore, in the charging control method of the warehouse robot, the probability value of the contact line abnormality is determined based on the inrush current information when the current charging power source and the warehouse robot come into contact and the warehouse robot charging cloud knowledge graph, specifically:

[0024] Inputting the impact current information of the current charging power source and the warehouse robot when they come into contact into the warehouse robot charging cloud knowledge graph for data matching;

[0025] Through data matching, the probability value of abnormal contact line under the impact current information when the current charging power supply and the storage robot come into contact is obtained.

[0026] Furthermore, in the charging control method for the storage robot, the abnormally charged storage robot is identified based on the probability value of the abnormality of the contact line, specifically:

[0027] Setting probability threshold evaluation index data, and determining whether the probability value of the contact line generating an abnormality is greater than the probability threshold evaluation index data;

[0028] When the probability value of the contact line generating an abnormality is greater than the probability threshold evaluation index data, the corresponding charging robot is regarded as an abnormally charged storage robot, and the abnormally charged storage robot is output;

[0029] When the probability value of the contact line generating an abnormality is not greater than the probability threshold evaluation index data, the corresponding robot is treated as a normally charged storage robot.

[0030] Furthermore, in the charging control method of the storage robot, the control strategy of the abnormally charged storage robot is optimized, specifically:

[0031] When a warehouse robot is abnormally charged, the low-power status information of the abnormally charged warehouse robot is obtained, and a search tag is constructed. Based on the search tag, a search is performed through big data to obtain the charging rate information of the abnormally charged warehouse robot under different charging data under the low-power status information;

[0032] Obtaining real-time charging data information of each charging device in the target area, and obtaining estimated charging rate information under different charging data based on the real-time charging data information of each charging device in the target area and the power-deficient state information of the abnormally charged storage machine;

[0033] When there is a charging device in the target area whose estimated charging rate information is greater than the preset charging rate information, the corresponding charging device is used as the preferred charging device for the current warehouse robot;

[0034] When there is no charging equipment in the target area whose estimated charging rate information is greater than the preset charging rate information, the charging operation of the abnormally charged warehouse robot is stopped.

[0035] A second aspect of the present invention provides a charging control system for a warehouse robot, the control system comprising a memory and a processor, the memory comprising a charging control method program for the warehouse robot, and when the charging control method program for the warehouse robot is executed by the processor, the steps of any one of the charging control methods for the warehouse robot are implemented.

[0036] A third aspect of the present invention provides a computer-readable storage medium, comprising a charging control method program for a warehouse robot. When the charging control method program for the warehouse robot is executed by a processor, the steps of any one of the charging control methods for the warehouse robot are implemented.

[0037] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0038] The present invention collects the impact current information of each time a charging power source contacts a warehouse robot, processes the impact current information of each time a charging power source contacts a warehouse robot, obtains the processed impact current information, and then constructs a warehouse robot charging cloud knowledge graph based on the processed impact current information, thereby collecting the impact current information fed back by the current charging power source when contacting the warehouse robot, and determining the probability value of an abnormality in the contact line based on the impact current information of the current charging power source when contacting the warehouse robot and the warehouse robot charging cloud knowledge graph, and finally identifies the abnormally charged warehouse robot based on the probability value of an abnormality in the contact line, and optimizes the control strategy for the abnormally charged warehouse robot. The present invention fully considers that a large impact circuit is generated at the moment when the robot's connector or electrode piece is inserted into the power supply, and that excessive impact current can easily cause corrosion on the contact surface of the robot's connector or electrode piece, thereby avoiding technical problems such as overheating of the circuit contact and fire, thereby improving the safety of the warehouse robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0040] Figure 1 The overall flow chart of the charging control method of the storage robot is shown;

[0041] Figure 2 Shows a system block diagram of the charging control system of the storage robot;

[0042] Figure 3 Shows a schematic diagram of the charging control circuit of the storage robot;

[0043] Figure 4 A schematic structural diagram of a charging pile is shown.

[0044] In the picture:

[0045] QF1: circuit breaker QF, KM1: contactor KM, KA1: relay 1, KA2: relay 2, SB1: forced charging button with light, H1: trolley in place indication, H2: charging brush power indication, H3: external power supply indication, SDR-75-24: switching power supply, P1241: photoelectric switch VD1. DETAILED DESCRIPTION

[0046] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0048] like Figure 1 As shown, the first aspect of the present invention provides a charging control method for a storage robot, comprising the following steps:

[0049] S102: collecting inrush current information each time the charging power source comes into contact with the storage robot, and processing the inrush current information when the charging power source comes into contact with the storage robot to obtain processed inrush current information;

[0050] S104: Constructing a cloud-based knowledge graph for warehouse robot charging based on the processed inrush current information;

[0051] S106: Collecting the inrush current information fed back when the current charging power source and the warehouse robot are in contact, and determining the probability value of an abnormality in the contact line based on the inrush current information when the current charging power source and the warehouse robot are in contact and the cloud-based knowledge graph of the warehouse robot charging;

[0052] S108: Identify abnormally charged storage robots based on the probability value of abnormality in the contact line, and optimize the control strategy of the abnormally charged storage robots.

[0053] It should be noted that the present invention fully considers that a large impact circuit is generated at the moment when the robot's connector or electrode is inserted into the power supply. Excessive impact current will easily cause corrosion of the contact surface of the robot's connector or electrode, thereby avoiding technical problems such as overheating of the circuit contacts and fire, and improving the safety of the use of the storage robot.

[0054] Furthermore, in the charging control method of the storage robot, the inrush current information when the charging power source and the storage robot come into contact is processed to obtain the processed inrush current information, which specifically includes:

[0055] Construct a time acquisition window, collect the impact current information of the charging power supply and the storage robot at each timestamp within the time acquisition window, and calculate the change amplitude characteristic data of the impact current information in adjacent timestamps;

[0056] A change amplitude threshold is set and a local outlier detection algorithm is introduced. The change amplitude threshold is used as the detection standard of the local outlier detection algorithm to calculate whether the change amplitude characteristic data of the surge current information in adjacent timestamps is greater than the change amplitude threshold.

[0057] The change amplitude characteristic data of the impulse current information in adjacent timestamps are detected by the local outlier detection algorithm, and the data nodes where the change amplitude characteristic data in the time acquisition window is greater than the change amplitude threshold are marked;

[0058] The data nodes where the characteristic data of the change amplitude is greater than the change amplitude threshold are deleted from the time acquisition window to obtain the processed inrush current information.

[0059] It should be noted that the change amplitude characteristic data of the impact current information in adjacent timestamps are detected through the local outlier detection algorithm, and the data nodes where the change amplitude characteristic data is greater than the change amplitude threshold in the time acquisition window are marked, and the data nodes where the change amplitude characteristic data is greater than the change amplitude threshold are deleted from the time acquisition window. In this way, abnormal data nodes can be processed and the accuracy of data acquisition can be improved.

[0060] Furthermore, in the charging control method of the warehouse robot, a cloud-based knowledge graph for charging the warehouse robot is constructed based on the processed inrush current information, specifically including:

[0061] Obtaining the number of times the contact line generates an abnormality and the number of times the contact line does not generate an abnormality under the processed impulse current information, counting the number of times the contact line generates an abnormality and the number of times the contact line does not generate an abnormality, and calculating the probability value of the contact line generating an abnormality under the processed impulse current information;

[0062] Based on the probability value of contact line abnormality under the processed inrush current information, a cloud-based knowledge graph for warehouse robot charging is constructed, with the processed inrush current information as the first graph node and the probability value of contact line abnormality as the second graph node.

[0063] Constructing an adjacency matrix according to the first graph node and the second graph node, and synchronously transmitting the adjacency matrix through a data link;

[0064] After data synchronization transmission, the adjacency matrix is ​​input into the warehouse robot charging cloud knowledge graph for storage.

[0065] It should be noted that since the power output end of the power supply is always energized, the robot's charging circuit will also have a certain capacitive load, which will cause a large impact circuit at the moment the robot's connector or electrode is inserted into the power supply. Excessive impact current can easily cause corrosion to the contact surface of the robot's connector or electrode. The charging process may cause safety accidents such as overheating of the circuit contacts and fire. By counting the number of times the contact line has an abnormality and the number of times the contact line has no abnormality, the probability value of the contact line having an abnormality under the processed impact current information is calculated, so as to better estimate the probability value of the contact line having an abnormality and optimize the charging process of the warehouse robot.

[0066] Furthermore, in the charging control method of the warehouse robot, the probability value of the contact line abnormality is determined based on the impact current information when the current charging power source and the warehouse robot come into contact and the cloud-based knowledge graph of the warehouse robot charging, specifically:

[0067] Input the impact current information of the current charging power source and the warehouse robot when they come into contact into the warehouse robot charging cloud knowledge graph for data matching;

[0068] Through data matching, the probability value of abnormal contact line under the impact current information when the current charging power supply and the storage robot come into contact is obtained.

[0069] Furthermore, in the charging control method for the storage robot, the abnormally charged storage robot is identified based on the probability value of the contact line generating an abnormality, specifically:

[0070] Setting probability threshold evaluation index data and determining whether the probability value of the contact line generating an abnormality is greater than the probability threshold evaluation index data;

[0071] When the probability value of the contact line generating an abnormality is greater than the probability threshold evaluation index data, the corresponding charging robot is regarded as an abnormally charged storage robot and the abnormally charged storage robot is output;

[0072] When the probability value of an abnormality in the contact line is not greater than the probability threshold evaluation index data, the corresponding robot will be treated as a normally charged storage robot.

[0073] Furthermore, in the charging control method of the storage robot, the control strategy of the storage robot with abnormal charging is optimized, specifically:

[0074] When charging an abnormal storage robot, obtain the power-deficient status information of the abnormally charged storage machine, and construct a retrieval tag. Based on the retrieval tag, search through big data to obtain the charging rate information of the abnormally charged storage machine under different charging data under the power-deficient status information;

[0075] Obtain real-time charging data information for each charging device in the target area, and obtain estimated charging rate information based on the real-time charging data information of each charging device in the target area and the charging rate information under different charging data under the power-deficient state information of the abnormally charged storage machine;

[0076] When there is a charging device in the target area whose estimated charging rate information is greater than the preset charging rate information, the corresponding charging device is used as the preferred charging device for the current warehouse robot;

[0077] When there is no charging device in the target area whose estimated charging rate information is greater than the preset charging rate information, the charging operation of the abnormally charged warehouse robot is stopped.

[0078] It should be noted that the robot battery may sometimes be abnormally depleted due to excessive energy consumption or battery aging. Due to the depletion, the charging rate information under different charging data (such as charging current, charging voltage, and charging power) under the current depletion status information is different. The depletion status information includes data such as the discharge rate per unit time, the depletion rate per unit time, and the amount of power loss per unit time. When there is a charging device in the target area whose estimated charging rate information is greater than the preset charging rate information, it indicates that the corresponding charging device is the preferred charging device for the current warehouse robot, thereby optimizing the charging process of the warehouse robot and improving the rationality of charging.

[0079] like Figure 3 As shown, the control strategy may include: mainly composed of a main charging circuit, a control circuit (1), and a control circuit (2). The main charging circuit mainly consists of an external power supply part, a circuit breaker, an external power indicator, a battery charger, a contactor contact group, a charging brush, etc. The control circuit (1) mainly consists of a switching power supply, a photoelectric switch, a trolley arrival indicator light, a relay KA1 coil, a relay KA2 coil, a group of relay KA1 contacts, a group of relay KA2 contacts, a forced charging light button, and a button light. The control circuit (2) mainly consists of a contactor coil, two groups of relay KA1 contacts, two groups of relay KA2 contacts, a charging brush power indicator light, etc. The control circuit (1) part A controls the detection of the trolley arrival situation and the relay closure contact after arrival for charging; the control circuit (1) part B is the forced charging operation part; and the control circuit (2) is the charging on / off control execution part.

[0080] In the main charging circuit of the present invention, the charging brush is not charged before and after charging.

[0081] During automatic charging, the warehouse robot reaches the charging station. Once the charging electrode reliably contacts the charging brush, the charging brush circuit receives power because the warehouse robot's battery still has charge. Once the photoelectric switch detects that the warehouse robot is fully charged, relay KA1 is energized, energizing both ends of the contactor. The contactor contacts close, and the main charging circuit begins charging. Once charging is complete, the warehouse robot disengages the charging station, de-energizing the photoelectric switch and the charging circuit. The charging brush also returns to a de-energized state, ready for the next charge.

[0082] During forced charging, relay KA1 operates in the same manner as automatic charging. Due to the robot's low battery, the contactor terminals remain de-energized. When a human intervenes and presses the illuminated charging button, relay KA2 energizes, closing the contacts. This energizes the contactor terminals, closing the contactor contacts and initiating charging. Once charging is complete, the robot disengages from the charging station, de-energizing the photoelectric switch and the charging circuit. The charging brushes also return to a de-energized state, ready for the next charge.

[0083] The structural diagram of the charging pile is as follows: Figure 4 As shown in FIG, the structure includes H1 indicator light, H2 indicator light, H3 indicator light, switching power supply, contactor, smoke temperature sensor alarm, fan, photoelectric switch, charging brush, battery charger, circuit breaker QF1, ventilation hole, shell, relay, button with light and other components. Among them, the smoke temperature sensor alarm is added to the charging pile, and the smoke and spontaneous combustion alarm function is added during the charging process; (2) The charging brush adopts a positive and negative dual-electrode elastic telescopic structure, and the telescopic distance is consistent with the detection distance of the photoelectric switch. When charging, the car presses the charging brush to the compression limit position, and the photoelectric detection also starts at this time. The brush compression mechanical physical position is coordinated with the photoelectric detection to ensure the correct and stable charging contact.

[0084] In summary, the main charging circuit maintains a neutral charging brush before and after charging, ensuring safety during the non-charging process. The overall control circuit enables automatic charging of the warehouse robot when the battery is low, as well as manual forced charging when the robot is low. The integrated charging circuit eliminates the need for a signal communication connection system between the warehouse robot and the charging station, making it adaptable to complex network noise environments and even disconnected connections, ensuring the warehouse robot can charge normally. The overall configuration is simple, easy to use and maintain, and highly secure.

[0085] In addition, the method further comprises:

[0086] Obtaining the geographical location information of the storage robot where the probability value of each contact line generating an abnormality is greater than the probability threshold evaluation index data, and obtaining the layout drawing data information of the location of the storage robot;

[0087] Constructing virtual warehouse scene information based on the layout drawing data information of the warehouse robot's location, and initializing the location nodes that the warehouse robot passes through when escaping from the virtual warehouse scene information, connecting each location node to construct the charging escape route information of the warehouse robot;

[0088] Determine the geographical location of the warehouse robot where the probability value of an abnormality caused by passing through other contact lines in the charging escape route information of the current warehouse robot is greater than the probability threshold evaluation index data;

[0089] When the probability value of an abnormality occurring without passing through other contact lines in the charging escape route information of the current warehouse robot is greater than the geographical location information of the warehouse robot in the probability threshold evaluation index data, output the charging escape route information of the current warehouse robot;

[0090] When the probability value of anomalies caused by passing through other contact lines in the current charging escape route information of the warehouse robot is greater than the geographical location information of the warehouse robot in the probability threshold evaluation index data, the charging escape route information of the warehouse robot is replanned.

[0091] It should be noted that when there is a warehouse robot whose contact line has a probability value greater than the probability threshold evaluation index data, it indicates that a fire may occur in the target area. This method can formulate an escape route for the warehouse robot whose contact line has a probability value greater than the probability threshold evaluation index data to avoid losses.

[0092] In addition, the method further comprises:

[0093] Acquire real-time charging factor characteristic information of the current warehouse robot charging device, construct a retrieval tag based on the real-time charging factor characteristic information of the current warehouse robot charging device, and perform a search through big data based on the retrieval tag;

[0094] Through big data retrieval, the probability value of the impact current when each charging device contacts the storage robot under the current real-time charging factor characteristic information is obtained, and the probability threshold of the impact current is set;

[0095] Determine whether a probability value of an inrush current occurring when each charging device contacts the storage robot under the current real-time charging factor characteristic information is greater than a probability threshold of an inrush current occurring;

[0096] A charging device whose probability value of the occurrence of an inrush current is not greater than the probability threshold of the occurrence of an inrush current is selected as the priority charging device of the storage robot.

[0097] It should be noted that real-time charging factor characteristics include factors such as lightning, switching operations, and sudden load changes. The presence of inrush current can cause localized high temperatures within the equipment, burning winding wires and increasing the failure rate of electrical equipment. For example, the inrush current generated when a motor starts can overload the motor cable and the electronic components within the motor, damaging the cables and motor. The inrush current generated when a switching power supply starts can damage circuit boards or batteries. This method can further improve the rationality of charging for warehouse robots and further prevent charging anomalies.

[0098] like Figure 2 As shown, the second aspect of the present invention provides a charging control system 4 for a warehouse robot. The control system 4 includes a memory 41 and a processor 42. The memory 41 includes a charging control method program for the warehouse robot. When the charging control method program for the warehouse robot is executed by the processor 42, any step of the charging control method for the warehouse robot is implemented.

[0099] A third aspect of the present invention provides a computer-readable storage medium, comprising a charging control method program for a warehouse robot. When the charging control method program for a warehouse robot is executed by a processor, the steps of any one of the charging control methods for a warehouse robot are implemented.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0101] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0102] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0103] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0104] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods of the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

[0105] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A charging control method for a storage robot, characterized in that: The following steps are involved: collecting inrush current information each time a charging power source comes into contact with a storage robot, and processing the inrush current information when the charging power source comes into contact with the storage robot to obtain processed inrush current information; Constructing a cloud-based knowledge graph for charging a warehouse robot based on the processed inrush current information; Collect the inrush current information fed back by the current charging power source when it comes into contact with the warehouse robot, and determine the probability value of an abnormality in the contact line based on the inrush current information when the current charging power source and the warehouse robot come into contact and the warehouse robot charging cloud knowledge graph; Based on the probability value of the abnormality generated in the contact line, the abnormally charged storage robot is identified, and the control strategy of the abnormally charged storage robot is optimized.

2. A charging control method for a storage robot according to claim 1, characterized in that: Processing the inrush current information when the charging power source and the storage robot come into contact to obtain the processed inrush current information specifically includes: Constructing a time collection window, collecting the impact current information of the charging power source and the storage robot when they come into contact in each timestamp within the time collection window, and calculating the change amplitude characteristic data of the impact current information in adjacent timestamps; Setting a change amplitude threshold, introducing a local outlier detection algorithm, using the change amplitude threshold as a detection criterion for the local outlier detection algorithm, and calculating whether the change amplitude characteristic data of the inrush current information in adjacent timestamps is greater than the change amplitude threshold; Detecting the change amplitude characteristic data of the inrush current information within the adjacent timestamps by using the local outlier detection algorithm, and marking the data nodes where the change amplitude characteristic data within the time acquisition window is greater than the change amplitude threshold; The data nodes where the variation amplitude characteristic data is greater than the variation amplitude threshold are deleted from the time acquisition window to obtain processed inrush current information.

3. The charging control method for a storage robot according to claim 1, characterized in that: Constructing a cloud-based knowledge graph for charging a warehouse robot based on the processed inrush current information specifically includes: Obtaining the number of times the contact line generates an abnormality and the number of times the contact line does not generate an abnormality under the processed impulse current information, counting the number of times the contact line generates an abnormality and the number of times the contact line does not generate an abnormality, and calculating the probability value of the contact line generating an abnormality under the processed impulse current information; Construct a cloud-based knowledge graph for charging a storage robot based on the probability value of the contact line generating anomalies under the processed inrush current information, using the processed inrush current information as a first graph node and the probability value of the contact line generating anomalies as a second graph node; constructing an adjacency matrix based on the first graph nodes and the second graph nodes, and synchronously transmitting the adjacency matrix through a data link; After data synchronization transmission, the adjacency matrix is ​​input into the warehouse robot charging cloud knowledge graph for storage.

4. The charging control method for a storage robot according to claim 1, characterized in that: The probability value of the contact line abnormality is determined based on the impact current information of the current charging power source and the warehouse robot when they are in contact and the warehouse robot charging cloud knowledge graph, specifically: Inputting the impact current information of the current charging power source and the warehouse robot when they come into contact into the warehouse robot charging cloud knowledge graph for data matching; Through data matching, the probability value of abnormal contact line under the impact current information when the current charging power supply and the storage robot come into contact is obtained.

5. The charging control method for a storage robot according to claim 1, characterized in that: The abnormally charged storage robot is identified based on the probability value of the abnormality of the contact line, specifically: Setting probability threshold evaluation index data, and determining whether the probability value of the contact line generating an abnormality is greater than the probability threshold evaluation index data; When the probability value of the contact line generating an abnormality is greater than the probability threshold evaluation index data, the corresponding charging robot is regarded as an abnormally charged storage robot, and the abnormally charged storage robot is output; When the probability value of the contact line generating an abnormality is not greater than the probability threshold evaluation index data, the corresponding robot is treated as a normally charged storage robot.

6. The charging control method for a storage robot according to claim 1, characterized in that: By optimizing the control strategy of the abnormally charged storage robot, specifically: When a warehouse robot is abnormally charged, the low-power status information of the abnormally charged warehouse robot is obtained, and a search tag is constructed. Based on the search tag, a search is performed through big data to obtain the charging rate information of the abnormally charged warehouse robot under different charging data under the low-power status information; Obtaining real-time charging data information of each charging device in the target area, and obtaining estimated charging rate information under different charging data based on the real-time charging data information of each charging device in the target area and the power-deficient state information of the abnormally charged storage machine; When there is a charging device in the target area whose estimated charging rate information is greater than the preset charging rate information, the corresponding charging device is used as the preferred charging device for the current warehouse robot; When there is no charging equipment in the target area whose estimated charging rate information is greater than the preset charging rate information, the charging operation of the abnormally charged warehouse robot is stopped.

7. A charging control system for a storage robot, characterized in that: The control system includes a memory and a processor, and the memory includes a charging control method program for the warehouse robot. When the charging control method program for the warehouse robot is executed by the processor, the steps of the charging control method for the warehouse robot as described in any one of claims 1-6 are implemented.

8. A computer-readable storage medium, characterized in that It includes a charging control method program for a warehouse robot. When the charging control method program for the warehouse robot is executed by a processor, the steps of the charging control method for the warehouse robot as described in any one of claims 1 to 6 are implemented.

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