A dual-mode chip driving method and device for controlling electronic equipment

Abstract

The present invention discloses a dual-mode chip driving method and device for electronic equipment control, which relates to the field of communication technology. The method includes: collecting warehouse attribute characteristics, simulating and building a three-dimensional warehouse space, performing dual-mode communication simulation test, and obtaining a dual-mode signal simulation distribution map; dividing the warehouse area, deploying multiple Bluetooth monitoring points and wireless monitoring points, and integrating and constructing a dual-source signal monitoring array; obtaining a real-time Bluetooth feature set and a real-time wireless feature set, predicting signal characteristics, and constructing a Bluetooth prediction distribution map and a wireless prediction distribution map; controlling a warehouse pickup robot within a predetermined period according to a predetermined communication connection strategy. The technical problem that it is difficult to accurately control equipment communication due to unstable communication signals and uneven coverage in a warehouse environment is solved. By accurately simulating and predicting the distribution of dual-mode signals, the accuracy and flexibility of communication strategy settings are improved, thereby improving the reliability and stability of warehouse pickup robot control.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a dual-mode chip driving method and device for controlling electronic equipment. Background Art

[0002] With the rapid development of intelligent warehousing technology, various automated equipment are increasingly used in warehouse environments. The efficient operation of these equipment depends on a stable and reliable communication system. However, the complex structure inside the warehouse and the diverse ways of placing goods make the transmission of wireless signals face many challenges. For example, metal shelves, dense cargo stacking, and complex three-dimensional space layout in the warehouse will interfere with wireless signals, leading to communication instability, signal attenuation and other problems. The existing single communication mode is difficult to cope with the communication needs in these complex scenarios. Therefore, how to achieve stable and reliable signal transmission in a warehouse environment has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a dual-mode chip driving method and device for electronic device control, which solves the technical problem that it is difficult to accurately control device communication due to unstable communication signals and uneven coverage in a warehouse environment.

[0004] In view of the above problems, the present application provides a dual-mode chip driving method and device for controlling electronic equipment.

[0005] In a first aspect of the present application, a dual-mode chip driving method for controlling an electronic device is provided, the method comprising:

[0006] Collect warehouse attribute features, simulate and build a three-dimensional warehouse space based on the warehouse attribute features, use the three-dimensional warehouse space to perform a dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution map, wherein the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution map includes a Bluetooth simulation distribution map and a wireless simulation distribution map; divide the warehouse area based on the Bluetooth simulation distribution map and the wireless simulation distribution map, respectively, deploy multiple Bluetooth monitoring points and wireless monitoring points according to the area division results, and integrate and construct a dual-source signal monitoring array; use the dual-source signal monitoring array to obtain a real-time Bluetooth feature set and a real-time wireless feature set, predict signal features within a predetermined period based on the real-time Bluetooth interference factor, the real-time wireless interference factor, the real-time Bluetooth feature set and the real-time wireless feature set, and construct a Bluetooth prediction distribution map and a wireless prediction distribution map according to the feature prediction results; based on the Bluetooth prediction distribution map and the wireless prediction distribution map, control the warehouse picking robot within the predetermined period according to a predetermined communication connection strategy.

[0007] A second aspect of the present application provides a dual-mode chip driving device for controlling an electronic device, the device comprising:

[0008] A test module, the test module is used to collect warehouse attribute characteristics, simulate and build a warehouse three-dimensional space based on the warehouse attribute characteristics, use the warehouse three-dimensional space to perform dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution map, wherein the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution map includes a Bluetooth simulation distribution map and a wireless simulation distribution map; an area division module, the area division module is used to divide the warehouse area based on the Bluetooth simulation distribution map and the wireless simulation distribution map, respectively, and arrange multiple Bluetooth monitoring points and wireless monitoring points according to the area division results, and integrate and construct a dual-source signal monitoring array; a prediction module, the prediction module is used to use the dual-source signal monitoring array to obtain a real-time Bluetooth feature set and a real-time wireless feature set, predict signal characteristics within a predetermined period based on the real-time Bluetooth interference factor, the real-time wireless interference factor, the real-time Bluetooth feature set and the real-time wireless feature set, and construct a Bluetooth prediction distribution map and a wireless prediction distribution map according to the feature prediction results; a control module, the control module is used to control the warehouse pickup robot within the predetermined period according to a predetermined communication connection strategy based on the Bluetooth prediction distribution map and the wireless prediction distribution map.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, collect warehouse attribute features, build a warehouse three-dimensional space based on warehouse attribute feature simulation, use the warehouse three-dimensional space to perform dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution map, where the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution map includes a Bluetooth simulation distribution map and a wireless simulation distribution map. Then, divide the warehouse area based on the Bluetooth simulation distribution map and the wireless simulation distribution map, and deploy multiple Bluetooth monitoring points and wireless monitoring points according to the results of the area division, and integrate and construct a dual-source signal monitoring array. Then, use the dual-source signal monitoring array to obtain real-time Bluetooth feature sets and real-time wireless feature sets, predict signal features within a predetermined period based on real-time Bluetooth interference factors, real-time wireless interference factors, real-time Bluetooth feature sets, and real-time wireless feature sets, and construct Bluetooth prediction distribution maps and wireless prediction distribution maps based on feature prediction results. Finally, based on the Bluetooth prediction distribution map and the wireless prediction distribution map, control the warehouse pickup robot within the predetermined period according to the predetermined communication connection strategy. The technical problem of unstable communication signals and uneven coverage in warehouse environments making it difficult to accurately control equipment communications has been solved. By accurately simulating and predicting the distribution of dual-mode signals, the accuracy and flexibility of communication strategy settings have been improved, thereby improving the reliability and stability of warehouse picking robot control. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic flow chart of a dual-mode chip driving method for controlling an electronic device provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a dual-mode chip driving device for controlling an electronic device provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: testing module 11 , area division module 12 , prediction module 13 , control module 14 . DETAILED DESCRIPTION

[0015] The present application provides a dual-mode chip driving method and device for electronic device control, thereby solving the technical problem that it is difficult to accurately control device communication due to unstable communication signals and uneven coverage in a warehouse environment.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a dual-mode chip driving method for controlling an electronic device, wherein the method comprises:

[0019] Collect warehouse attribute characteristics, simulate and build a three-dimensional warehouse space based on the warehouse attribute characteristics, use the three-dimensional warehouse space to perform a dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution diagram, wherein the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution diagram includes a Bluetooth simulation distribution diagram and a wireless simulation distribution diagram.

[0020] Furthermore, the warehouse attribute characteristics include at least warehouse structure characteristics, shelf layout characteristics, Bluetooth receiver distribution and router distribution.

[0021] Warehouse attribute characteristics at least include warehouse structure characteristics, shelf layout characteristics, Bluetooth receiver distribution and router distribution, among which warehouse structure characteristics include geometric dimensions (length, width, height of the warehouse, and specific dimensions of each area, such as storage area, picking area, channel, etc.), building structure (material and thickness of load-bearing columns, walls, ceilings, floors); shelf layout characteristics include shelf types (heavy shelves, medium shelves, light shelves, etc., different types of shelves have different blocking effects on signal propagation), shelf arrangement (arrangement of shelves, such as straight line arrangement, grid arrangement, spacing); Bluetooth receiver distribution includes location information (specific location of Bluetooth receiver in the warehouse), coverage (expected coverage of each Bluetooth receiver), performance parameters (performance parameters such as sensitivity and transmission power of Bluetooth receiver); router distribution includes location information, channel allocation, performance parameters (transmission power of router, signal stability, coverage, etc.). By collecting warehouse attribute characteristics, using 3D modeling software or simulation platform, a 3D space model of the warehouse can be constructed. The model should accurately reflect the actual structure and layout of the warehouse, including shelves, goods, Bluetooth receivers, routers and other related elements. In the three-dimensional warehouse space constructed by simulation, dual-mode communication simulation tests of Bluetooth and wireless (such as Wi-Fi) are carried out according to the predetermined multi-dimensional communication scenarios (such as different time periods, cargo distribution status, personnel flow, etc.); during the test, the attenuation, reflection, refraction and other phenomena in the signal transmission process are simulated, as well as the interference between different devices; through the simulation test, a dual-mode signal simulation distribution map is generated, which includes a Bluetooth simulation distribution map and a wireless simulation distribution map. These distribution maps show the coverage and signal quality of Bluetooth and wireless signals in different areas and scenarios in graphical form. Among them, the simulated signal is the mode under the multi-dimensional simulation test, that is, the most representative signal strength is selected as the evaluation standard.

[0022] The warehouse area is divided based on the Bluetooth simulation distribution map and the wireless simulation distribution map, and multiple Bluetooth monitoring points and wireless monitoring points are arranged according to the area division results, and a dual-source signal monitoring array is constructed by integration.

[0023] According to the Bluetooth simulation distribution map and the wireless simulation distribution map, the signal strong area, weak area and blind area are identified, and the warehouse is divided into areas based on the characteristics of the signal distribution; according to the area division results, multiple Bluetooth monitoring points and wireless monitoring points are deployed in the warehouse to achieve comprehensive coverage and real-time monitoring of the signal environment in the warehouse; these monitoring points together constitute a dual-source signal monitoring array.

[0024] Furthermore, the warehouse area division includes:

[0025] The Bluetooth simulation distribution map is grid-divided to determine multiple Bluetooth grid areas and multiple Bluetooth signal strengths; a first grid and a second grid are randomly selected from the multiple Bluetooth grid areas to obtain a first signal strength of the first grid and a second signal strength of the second grid, wherein the first grid and the second grid are any two adjacent grid areas in the multiple Bluetooth grid areas; if the deviation value of the first signal strength and the second signal strength is less than a predetermined strength threshold, the first grid and the second grid are merged, wherein the signal strength of the merged area is the average of the first signal strength and the second signal strength; if the deviation value of the first signal strength and the second signal strength is greater than or equal to the predetermined strength threshold, a dividing line is set at the connection between the first grid and the second grid to obtain a first dividing line; iterative merging is performed until there is no grid area that meets the merging condition, the merging is stopped and multiple dividing lines are obtained, the multiple dividing lines are fused and connected to generate multiple area dividing lines, the multiple area dividing lines are used to divide the warehouse area, and the Bluetooth area division result is obtained.

[0026] Specifically, the Bluetooth simulation distribution map is divided into multiple small grids (Bluetooth grid areas), each grid area represents a specific area in the warehouse, and the Bluetooth signal strength in each grid area is calculated; in all Bluetooth grid areas, two adjacent grids are randomly selected as the first grid and the second grid, and the signal strengths of the first grid and the second grid, that is, the first signal strength and the second signal strength, are obtained; the deviation value of the first signal strength and the second signal strength is calculated, and if the deviation value is less than a predetermined strength threshold, it means that the signal strengths of the two grid areas are similar and can be merged. After the merger, the signal strength of the new area takes the average of the signal strengths of the two original grids; if the deviation value is greater than or equal to the strength threshold, the two grid areas are kept. The grid areas are independent, and the first dividing line is set between them; the above steps are repeated, and adjacent grids are continuously selected for signal strength comparison and merging until there is no grid area that meets the merging condition (that is, the signal strength deviations of all adjacent grids are greater than or equal to the strength threshold). In the iterative process, multiple dividing lines will be generated, and these dividing lines represent the boundaries of different signal strength areas; all generated dividing lines are fused and connected to form a complete area dividing line network, and these area dividing lines divide the warehouse into multiple areas with different Bluetooth signal strengths; the warehouse is divided into areas using the area dividing lines to obtain the Bluetooth area division result, and the wireless area division can adopt the same method as the Bluetooth area division.

[0027] Utilizing the dual-source signal monitoring array to obtain a real-time Bluetooth feature set and a real-time wireless feature set, predicting signal features within a predetermined period based on the real-time Bluetooth interference factor, the real-time wireless interference factor, the real-time Bluetooth feature set and the real-time wireless feature set, and constructing a Bluetooth prediction distribution map and a wireless prediction distribution map according to the feature prediction results;

[0028] Through the Bluetooth monitoring points in the dual-source signal monitoring array, the characteristic data of the Bluetooth signal, including but not limited to signal strength, signal-to-noise ratio, signal stability, etc., are collected in real time to form a real-time Bluetooth feature set; similarly, the characteristic data of the wireless signal, including signal strength, channel occupancy, data transmission rate, etc., are collected through the wireless monitoring points to form a real-time wireless feature set. According to the real-time Bluetooth feature set and the real-time wireless feature set, combined with the real-time Bluetooth interference factor and the real-time wireless interference factor, the signal characteristics within the predetermined period are predicted and analyzed, and the Bluetooth prediction distribution map and the wireless prediction distribution map are constructed based on the feature prediction results to reflect the signal distribution in the warehouse in the future. Among them, the Bluetooth prediction distribution map reflects the coverage and signal strength of the Bluetooth signal in the future; the wireless prediction distribution map reflects the coverage and channel usage of the wireless signal.

[0029] Furthermore, signal feature prediction is performed, including:

[0030] Configure a Bluetooth interference index and a wireless interference index, and acquire a real-time Bluetooth interference factor and a real-time wireless interference factor based on the Bluetooth interference index and the wireless interference index monitoring, wherein the real-time wireless interference factor also includes a spectrum congestion prediction coefficient; input the real-time Bluetooth interference factor and the real-time Bluetooth feature set into a first signal prediction unit, perform Bluetooth signal feature prediction within a predetermined period, and output a Bluetooth feature prediction result, wherein the first signal prediction unit is constructed based on a BP neural network and a random decision forest integration; input the real-time wireless interference factor and the real-time wireless feature set into a second signal prediction unit, perform wireless signal feature prediction within a predetermined period, and output a wireless feature prediction result, wherein the second signal prediction unit is constructed based on a BP neural network and a random decision forest integration; generate the feature prediction result based on the Bluetooth feature prediction result and the wireless feature prediction result.

[0031] Specifically, a series of indicators for evaluating Bluetooth signal interference are defined, which may include signal strength fluctuation, signal-to-noise ratio decrease, packet loss rate increase, etc. Similarly, a series of indicators for evaluating wireless signal (such as Wi-Fi) interference are defined, including signal strength fluctuation, signal-to-noise ratio decrease, packet loss rate increase, channel occupancy, spectrum congestion, etc. Based on the Bluetooth interference indicator, the interference of Bluetooth signals is monitored in real time through the Bluetooth monitoring points in the dual-source signal monitoring array to obtain the real-time Bluetooth interference factor. Similarly, the interference of wireless signals is monitored based on the wireless interference indicator to obtain the real-time wireless interference factor, wherein the real-time wireless interference factor also includes the spectrum congestion prediction coefficient, which is an indicator for predicting future spectrum congestion. The first signal prediction unit is constructed based on the integration of BP neural network and random decision forest. BP neural network is good at processing nonlinear relationships, while random decision forest can improve the accuracy and stability of prediction by integrating multiple decision trees. The real-time Bluetooth interference factor and the real-time Bluetooth feature set are used as inputs, and the Bluetooth signal feature prediction within a predetermined period is performed through the first signal prediction unit, and the Bluetooth feature prediction result is output. The second signal prediction unit is similar to the first signal prediction unit, but is specifically used for the prediction of wireless signals. It is also based on the integration of BP neural network and random decision forest. The real-time wireless interference factor (including spectrum congestion prediction coefficient) and the real-time wireless feature set are used as inputs. The second signal prediction unit predicts the wireless signal features within a predetermined period and outputs the wireless feature prediction results. The output results of the first signal prediction unit and the second signal prediction unit (i.e., the Bluetooth feature prediction results and the wireless feature prediction results) are summarized and integrated to generate the final feature prediction results. This result will include the predicted features of Bluetooth and wireless signals within the future predetermined period, such as the trend of signal strength changes, the prediction of interference conditions, etc.

[0032] Furthermore, obtaining the spectrum congestion prediction coefficient includes:

[0033] Read multiple target task sets of multiple warehouse picking robots within a predetermined period; perform simultaneous and regional analysis based on the multiple target task sets, and determine multiple spectrum congestion prediction coefficients for multiple warehouse areas based on the analysis results.

[0034] Specifically, multiple target task sets of multiple warehouse picking robots within a predetermined period are read, and these target task sets contain all tasks that the robots need to perform within the period, including information such as picking up goods, delivering goods, and path planning; based on the read target task sets, simultaneous analysis and same-area analysis are performed to identify which warehouse areas will have multiple robots performing tasks simultaneously within the same time period; simultaneous analysis is to sort the tasks in the target task set according to timestamps, and find out the robots that have tasks to perform within the same time window (such as one minute, five minutes, etc.); same-area analysis is to further analyze these simultaneous tasks to determine whether they occur in the same warehouse area; based on factors such as the number of tasks at the same time and in the same area, equipment type, communication frequency band, and historical spectrum usage, a deep learning model can be trained based on historical data to calculate a spectrum congestion prediction coefficient, which can be a value between 0 and 1, indicating the severity of spectrum congestion, where 0 indicates no congestion and 1 indicates severe congestion.

[0035] Furthermore, obtaining the Bluetooth feature prediction results also includes:

[0036] Randomly select a first Bluetooth predicted strength of the first warehouse area from the Bluetooth feature prediction results; retrieve a historical signal strength sequence of the first warehouse area within a predetermined time window, perform a signal fluctuation trend analysis based on the historical signal strength sequence, and determine a first Bluetooth signal fluctuation trend; compensate the first Bluetooth predicted strength according to the first Bluetooth signal fluctuation trend, and update the first Bluetooth predicted strength based on the compensation result.

[0037] Preferably, in the Bluetooth feature prediction results, a warehouse area is randomly selected as the first warehouse area, and the first Bluetooth prediction strength of the first warehouse area is obtained, and the prediction strength is an estimated value of the Bluetooth signal strength of the area at a certain moment in the future; for the selected first warehouse area, the historical signal strength sequence within a predetermined time window (such as the past hour, day or week) is retrieved, the historical signal strength sequence contains the actual record of the Bluetooth signal strength of the area over time, and is arranged from far to near according to time; based on the retrieved historical signal strength sequence, a signal fluctuation trend analysis is performed, including identifying characteristics such as periodic changes and random fluctuations in signal strength, and evaluating the possible impact of these changes on future signal strength; through the signal fluctuation trend analysis, the fluctuation trend of the first Bluetooth signal in the first warehouse area in the future prediction time period, that is, the first Bluetooth signal fluctuation trend, is determined; according to the determined first Bluetooth signal fluctuation trend, the original Bluetooth prediction strength is compensated, and the purpose of the compensation is to adjust the prediction value to more accurately reflect the actual situation of the future signal strength; based on the compensation result, the first Bluetooth prediction strength of the first warehouse area is updated, and this updated prediction value will be closer to the actual situation of the future signal strength, thereby improving the accuracy and reliability of the prediction. In addition, the compensation for the wireless feature prediction results may be performed using the same compensation method as the Bluetooth feature prediction results.

[0038] Based on the Bluetooth prediction distribution map and the wireless prediction distribution map, the warehouse picking robot within the predetermined period is controlled according to a predetermined communication connection strategy.

[0039] According to the Bluetooth prediction distribution map and the wireless prediction distribution map, combined with the predetermined communication connection strategy, the warehouse picking robot is precisely controlled. By optimizing the communication path and adjusting the communication parameters, the picking robot is ensured to be able to perform the picking task stably and efficiently in a complex warehouse environment.

[0040] Furthermore, controlling the warehouse picking robot within the predetermined period includes:

[0041] The target task set of the warehouse picking robot within the predetermined period is read, and a plurality of target task points and a plurality of expected communication requirements are determined based on the target task set; based on the Bluetooth prediction distribution map and the wireless prediction distribution map, with the plurality of expected communication requirements as a constraint, matching is performed according to the plurality of target task points to determine an adaptation communication mode set; based on the adaptation communication mode set, the warehouse picking robot within the predetermined period is controlled according to the predetermined communication connection strategy, wherein the predetermined communication connection strategy is that if the adaptation communication mode includes Bluetooth communication and wireless communication, Bluetooth communication is used preferentially; if the adaptation communication mode is one, that communication mode is used; if the adaptation communication mode is zero, Bluetooth communication and wireless communication are used for redundant communication.

[0042] Specifically, the target task set of the warehouse picking robot within a predetermined period is read, and the target task set covers the tasks that the robot needs to perform, including the target task point (i.e., the specific location of picking up or delivering goods); based on the target task set, the target task point of each task and the corresponding expected communication demand are determined to obtain multiple expected communication demands; according to the Bluetooth prediction distribution map and the wireless prediction distribution map, the communication environment around each target task point is analyzed, including the coverage, strength and stability of the Bluetooth signal, as well as the channel congestion and signal quality of the wireless signal; with the constraint of meeting multiple expected communication demands, the communication mode is matched for each target task point, so as to determine the set of adapted communication modes that can be used at the task point; if the adapted communication mode set includes both Bluetooth communication and wireless communication, Bluetooth communication is used preferentially according to the predetermined strategy to reduce power consumption and usage cost; if there is only one communication mode in the adapted communication mode set, the communication mode is used directly; if the adapted communication mode set is zero, that is, there is no suitable communication mode that can meet the expected communication demand, a redundant communication scheme of Bluetooth communication and wireless communication is adopted to improve communication stability.

[0043] In summary, the embodiments of the present application have at least the following technical effects:

[0044] First, collect warehouse attribute features, build a warehouse three-dimensional space based on warehouse attribute feature simulation, use the warehouse three-dimensional space to perform dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution map, where the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution map includes a Bluetooth simulation distribution map and a wireless simulation distribution map. Then, divide the warehouse area based on the Bluetooth simulation distribution map and the wireless simulation distribution map, and deploy multiple Bluetooth monitoring points and wireless monitoring points according to the results of the area division, and integrate and construct a dual-source signal monitoring array. Then, use the dual-source signal monitoring array to obtain real-time Bluetooth feature sets and real-time wireless feature sets, predict signal features within a predetermined period based on real-time Bluetooth interference factors, real-time wireless interference factors, real-time Bluetooth feature sets, and real-time wireless feature sets, and construct Bluetooth prediction distribution maps and wireless prediction distribution maps based on feature prediction results. Finally, based on the Bluetooth prediction distribution map and the wireless prediction distribution map, control the warehouse pickup robot within the predetermined period according to the predetermined communication connection strategy. The technical problem of unstable communication signals and uneven coverage in warehouse environments making it difficult to accurately control equipment communications has been solved. By accurately simulating and predicting the distribution of dual-mode signals, the accuracy and flexibility of communication strategy settings have been improved, thereby improving the reliability and stability of warehouse picking robot control.

[0045] Embodiment 2 is based on the same inventive concept as the dual-mode chip driving method for controlling an electronic device in the above embodiment. Figure 2As shown, the present application provides a dual-mode chip driving device for controlling an electronic device, wherein the device comprises:

[0046] A test module 11, the test module 11 is used to collect warehouse attribute characteristics, simulate and build a warehouse three-dimensional space based on the warehouse attribute characteristics, use the warehouse three-dimensional space to perform a dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution map, wherein the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution map includes a Bluetooth simulation distribution map and a wireless simulation distribution map; an area division module 12, the area division module 12 is used to divide the warehouse area based on the Bluetooth simulation distribution map and the wireless simulation distribution map, and deploy multiple Bluetooth monitoring points and wireless according to the area division results. Monitoring points, integrating and constructing a dual-source signal monitoring array; a prediction module 13, the prediction module 13 is used to use the dual-source signal monitoring array to obtain a real-time Bluetooth feature set and a real-time wireless feature set, and predict the signal characteristics within a predetermined period based on the real-time Bluetooth interference factor, the real-time wireless interference factor, the real-time Bluetooth feature set and the real-time wireless feature set, and construct a Bluetooth prediction distribution map and a wireless prediction distribution map according to the feature prediction results; a control module 14, the control module 14 is used to control the warehouse picking robot within the predetermined period according to a predetermined communication connection strategy based on the Bluetooth prediction distribution map and the wireless prediction distribution map.

[0047] Furthermore, the control module 14 is used to execute the following method:

[0048] The target task set of the warehouse picking robot within the predetermined period is read, and a plurality of target task points and a plurality of expected communication requirements are determined based on the target task set; based on the Bluetooth prediction distribution map and the wireless prediction distribution map, with the plurality of expected communication requirements as a constraint, matching is performed according to the plurality of target task points to determine an adaptation communication mode set; based on the adaptation communication mode set, the warehouse picking robot within the predetermined period is controlled according to the predetermined communication connection strategy, wherein the predetermined communication connection strategy is that if the adaptation communication mode includes Bluetooth communication and wireless communication, Bluetooth communication is used preferentially; if the adaptation communication mode is one, that communication mode is used; if the adaptation communication mode is zero, Bluetooth communication and wireless communication are used for redundant communication.

[0049] Furthermore, the test module 11 is used to execute the following method:

[0050] The warehouse attribute characteristics include at least warehouse structure characteristics, shelf layout characteristics, Bluetooth receiver distribution and router distribution.

[0051] Furthermore, the region division module 12 is used to perform the following method:

[0052] The Bluetooth simulation distribution map is grid-divided to determine multiple Bluetooth grid areas and multiple Bluetooth signal strengths; a first grid and a second grid are randomly selected from the multiple Bluetooth grid areas to obtain a first signal strength of the first grid and a second signal strength of the second grid, wherein the first grid and the second grid are any two adjacent grid areas in the multiple Bluetooth grid areas; if the deviation value of the first signal strength and the second signal strength is less than a predetermined strength threshold, the first grid and the second grid are merged, wherein the signal strength of the merged area is the average of the first signal strength and the second signal strength; if the deviation value of the first signal strength and the second signal strength is greater than or equal to the predetermined strength threshold, a dividing line is set at the connection between the first grid and the second grid to obtain a first dividing line; iterative merging is performed until there is no grid area that meets the merging condition, the merging is stopped and multiple dividing lines are obtained, the multiple dividing lines are fused and connected to generate multiple area dividing lines, the multiple area dividing lines are used to divide the warehouse area, and the Bluetooth area division result is obtained.

[0053] Furthermore, the prediction module 13 is used to perform the following method:

[0054] Configure a Bluetooth interference index and a wireless interference index, and acquire a real-time Bluetooth interference factor and a real-time wireless interference factor based on the Bluetooth interference index and the wireless interference index monitoring, wherein the real-time wireless interference factor also includes a spectrum congestion prediction coefficient; input the real-time Bluetooth interference factor and the real-time Bluetooth feature set into a first signal prediction unit, perform Bluetooth signal feature prediction within a predetermined period, and output a Bluetooth feature prediction result, wherein the first signal prediction unit is constructed based on a BP neural network and a random decision forest integration; input the real-time wireless interference factor and the real-time wireless feature set into a second signal prediction unit, perform wireless signal feature prediction within a predetermined period, and output a wireless feature prediction result, wherein the second signal prediction unit is constructed based on a BP neural network and a random decision forest integration; generate the feature prediction result based on the Bluetooth feature prediction result and the wireless feature prediction result.

[0055] Furthermore, the prediction module 13 is used to perform the following method:

[0056] Read multiple target task sets of multiple warehouse picking robots within a predetermined period; perform simultaneous and regional analysis based on the multiple target task sets, and determine multiple spectrum congestion prediction coefficients for multiple warehouse areas based on the analysis results.

[0057] Furthermore, the prediction module 13 is used to perform the following method:

[0058] Randomly select a first Bluetooth predicted strength of the first warehouse area from the Bluetooth feature prediction results; retrieve a historical signal strength sequence of the first warehouse area within a predetermined time window, perform a signal fluctuation trend analysis based on the historical signal strength sequence, and determine a first Bluetooth signal fluctuation trend; compensate the first Bluetooth predicted strength according to the first Bluetooth signal fluctuation trend, and update the first Bluetooth predicted strength based on the compensation result.

[0059] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0061] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A dual-mode chip driving method for electronic equipment control, characterized in that: The method comprises: Collect warehouse attribute characteristics, simulate and build a three-dimensional warehouse space based on the warehouse attribute characteristics, use the three-dimensional warehouse space to perform a dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution map, wherein the simulation signal is the mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution map includes a Bluetooth simulation distribution map and a WIFI simulation distribution map; Based on the Bluetooth simulation distribution map and the WIFI simulation distribution map, warehouse areas are divided respectively, and multiple Bluetooth monitoring points and WIFI monitoring points are arranged according to the area division results, and a dual-source signal monitoring array is constructed by integration; Using the dual-source signal monitoring array to obtain a real-time Bluetooth feature set and a real-time WIFI feature set, predicting signal features within a predetermined period based on the real-time Bluetooth interference factor, the real-time WIFI interference factor, the real-time Bluetooth feature set and the real-time WIFI feature set, and constructing a Bluetooth prediction distribution map and a WIFI prediction distribution map according to the feature prediction results; Based on the Bluetooth prediction distribution map and the WIFI prediction distribution map, the warehouse picking robot within the predetermined period is controlled according to a predetermined communication connection strategy.

2. A dual-mode chip driving method for electronic device control according to claim 1, characterized in that: Controlling the warehouse picking robot within the predetermined period includes: Reading a target task set of the warehouse picking robot within the predetermined period, and determining a plurality of target task points and a plurality of expected communication requirements based on the target task set; Based on the Bluetooth prediction distribution map and the WIFI prediction distribution map, with satisfying the multiple expected communication requirements as a constraint, matching is performed according to the multiple target task points to determine an adapted communication mode set; Based on the adapted communication mode set, the warehouse picking robot within the predetermined period is controlled according to the predetermined communication connection strategy, wherein the predetermined communication connection strategy is that if the adapted communication mode includes Bluetooth communication and WIFI communication, Bluetooth communication is used preferentially; if the adapted communication mode is one, that communication mode is used; if the adapted communication mode is zero, Bluetooth communication and WIFI communication are used for redundant communication.

3. The dual-mode chip driving method for electronic device control according to claim 1, characterized in that: The warehouse attribute characteristics include at least warehouse structure characteristics, shelf layout characteristics, Bluetooth receiver distribution and router distribution.

4. The dual-mode chip driving method for controlling an electronic device according to claim 1, characterized in that: Divide the warehouse area, including: Divide the Bluetooth simulation distribution map into grids to determine a plurality of Bluetooth grid areas and a plurality of Bluetooth signal strengths; Randomly selecting a first grid and a second grid from the multiple Bluetooth grid areas, and obtaining a first signal strength of the first grid and a second signal strength of the second grid, wherein the first grid and the second grid are any two adjacent grid areas in the multiple Bluetooth grid areas; If the deviation value between the first signal strength and the second signal strength is less than a predetermined strength threshold, merging the first grid and the second grid, wherein the signal strength of the merged area is the average of the first signal strength and the second signal strength; If the deviation value between the first signal strength and the second signal strength is greater than or equal to a predetermined strength threshold, setting a dividing line at a connection between the first grid and the second grid to obtain a first dividing line; Iterate the merging until there is no grid area that meets the merging conditions, stop merging and obtain multiple dividing lines, merge and connect the multiple dividing lines to generate multiple area dividing lines, use the multiple area dividing lines to divide the warehouse area, and obtain the Bluetooth area division result.

5. The dual-mode chip driving method for controlling an electronic device according to claim 1, characterized in that: Perform signal feature prediction, including: Configure a Bluetooth interference index and a WIFI interference index, and monitor and obtain a real-time Bluetooth interference factor and a real-time WIFI interference factor based on the Bluetooth interference index and the WIFI interference index, wherein the real-time WIFI interference factor also includes a spectrum congestion prediction coefficient; Inputting the real-time Bluetooth interference factor and the real-time Bluetooth feature set into a first signal prediction unit, performing Bluetooth signal feature prediction within a predetermined period, and outputting a Bluetooth feature prediction result, wherein the first signal prediction unit is constructed based on a BP neural network and a random decision forest integration; Input the real-time WIFI interference factor and the real-time WIFI feature set into a second signal prediction unit, perform WIFI signal feature prediction within a predetermined period, and output a WIFI feature prediction result, wherein the second signal prediction unit is constructed based on a BP neural network and a random decision forest integration; The feature prediction result is generated based on the Bluetooth feature prediction result and the WIFI feature prediction result.

6. A dual-mode chip driving method for controlling electronic equipment according to claim 5, characterized in that: Obtain spectrum congestion prediction coefficients, including: Read multiple target task sets of multiple warehouse picking robots within a predetermined period; Based on the multiple target task sets, simultaneous and same-area analysis is performed, and multiple spectrum congestion prediction coefficients for multiple warehouse areas are determined according to the analysis results.

7. The dual-mode chip driving method for controlling an electronic device according to claim 1, characterized in that: Get the Bluetooth feature prediction results, followed by: Randomly selecting a first Bluetooth prediction strength of a first warehouse area from the Bluetooth feature prediction results; Retrieving a historical signal strength sequence of the first warehouse area within a predetermined time window, performing a signal fluctuation trend analysis based on the historical signal strength sequence, and determining a first Bluetooth signal fluctuation trend; The first Bluetooth predicted strength is compensated according to the fluctuation trend of the first Bluetooth signal, and the first Bluetooth predicted strength is updated based on the compensation result.

8. A dual-mode chip driving device for controlling electronic equipment, characterized in that: The device is used to implement a dual-mode chip driving method for controlling an electronic device according to any one of claims 1 to 7, the device comprising: A test module, the test module is used to collect warehouse attribute characteristics, simulate and build a three-dimensional warehouse space based on the warehouse attribute characteristics, use the three-dimensional warehouse space to perform a dual-mode communication simulation test in a predetermined multi-dimensional communication scenario, and obtain a dual-mode signal simulation distribution diagram, wherein the simulation signal is a mode under the multi-dimensional simulation test, and the dual-mode signal simulation distribution diagram includes a Bluetooth simulation distribution diagram and a WIFI simulation distribution diagram; A region division module, the region division module is used to divide the warehouse region based on the Bluetooth simulation distribution map and the WIFI simulation distribution map, and to deploy multiple Bluetooth monitoring points and WIFI monitoring points according to the region division results, and to integrate and construct a dual-source signal monitoring array; A prediction module, the prediction module is used to obtain a real-time Bluetooth feature set and a real-time WIFI feature set using the dual-source signal monitoring array, perform signal feature prediction within a predetermined period based on the real-time Bluetooth interference factor, the real-time WIFI interference factor, the real-time Bluetooth feature set and the real-time WIFI feature set, and construct a Bluetooth prediction distribution map and a WIFI prediction distribution map according to the feature prediction results; A control module is used to control the warehouse picking robot within the predetermined period according to a predetermined communication connection strategy based on the Bluetooth prediction distribution map and the WIFI prediction distribution map.

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