Automatic detection method and device, readable storage medium and electronic equipment

Abstract

The invention provides an automatic detection method and device, a readable storage medium and electronic equipment, and the method comprises the steps: obtaining the network type information of a plurality of automatic detection modules and the historical detection service information of a previous detection period; determining a communication resource allocation strategy of the plurality of automatic detection modules in the current detection period according to the historical detection service information, the network type information and a pre-trained resource allocation model; performing communication connection with the plurality of automatic detection modules according to the communication resource allocation strategy, and performing detection control operation; and obtaining a detection result of the current detection period from the plurality of automatic detection modules. Thus, the automatic detection assembly line is established to be combined with the 5G private network, the test reliability and the communication transmission capability of the factory assembly line are improved, and through the communication resource allocation strategy of each detection period, resource preemption is reduced, and the flexibility of communication connection is improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an automated detection method, device, readable storage medium, and electronic equipment. Background Art

[0002] With the development of industrial Internet, more and more workshop equipment, such as machine tools, robots, and carts, have begun to be connected to the factory intranet. Due to the high precision required for workshop equipment, the latency, flexibility, and bandwidth of the factory intranet are increasingly required. Wired networks are difficult to meet large-scale connection requirements, and ordinary wireless networks connected to all workshop equipment in the workshop still have the disadvantages of being unable to carry large data streams for fast transmission, insufficient rate and bandwidth, communication resource preemption, and poor scalability, which in turn leads to low reliability of test results for products produced in the workshop, insufficient transmission capacity, and low flexibility. Summary of the invention

[0003] The embodiments of the present application provide an automated detection method, device, readable storage medium, and electronic device, which are conducive to reducing resource preemption and improving the test reliability, communication transmission capability, and flexibility of factory assembly lines.

[0004] In a first aspect, an embodiment of the present application provides an automated detection method, which is applied to a computing platform of a communication module automated detection system, wherein the communication module automated detection system further includes a plurality of automated detection modules, and the method includes:

[0005] Obtain network type information of the multiple automated detection modules and historical detection service information of the previous detection cycle;

[0006] Determine, according to the historical detection service information, the network type information and the pre-trained resource allocation model, a communication resource allocation strategy for the multiple automatic detection modules in the current detection cycle, wherein the communication resource allocation strategy is used to limit the amount and type of resources allocated to the multiple automatic detection modules, and the amount and type of resources are used to realize information transmission;

[0007] Establishing communication connections with the plurality of automated detection modules according to the communication resource allocation strategy, and performing detection control operations, wherein the detection control operations are used to enable the plurality of automated detection modules to respectively start executing corresponding module functions;

[0008] The detection results of the current detection cycle are obtained from the multiple automatic detection modules, and the detection results are used to indicate the qualification status of the communication module and the actual detection business volume of the current detection cycle, and the actual detection business volume is used to predict the detection business volume of the next detection cycle.

[0009] In a second aspect, an embodiment of the present application provides an automated detection device, which is applied to a production management system of a communication module automated detection system, wherein the communication module automated detection system further includes a plurality of automated detection modules, and the device includes:

[0010] A first acquisition module, used to acquire network type information of the plurality of automatic detection modules and historical detection service information of the previous detection cycle;

[0011] an allocation module, configured to determine, based on the historical detection service information, the network type information and a pre-trained resource allocation model, a communication resource allocation strategy for the multiple automated detection modules in a current detection cycle, wherein the communication resource allocation strategy is configured to limit the amount and type of resources allocated to the multiple automated detection modules, and the amount and type of resources are configured to implement information transmission;

[0012] a connection module, configured to establish communication connections with the plurality of automatic detection modules according to the communication resource allocation strategy, and to perform a detection control operation, wherein the detection control operation is configured to enable the plurality of automatic detection modules to respectively start executing corresponding module functions;

[0013] The second acquisition module is used to obtain the detection results of the current detection cycle from the multiple automatic detection modules, and the detection results are used to indicate the qualification status of the communication module and the actual detection business volume of the current detection cycle, and the actual detection business volume is used to predict the detection business volume of the next detection cycle.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs. The memory stores the one or more programs and is configured to be executed by the processor. When the processor executes the one or more programs stored in the memory, the processor executes instructions of some or all of the steps described in the first aspect of the embodiment of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which an automated detection program is stored. The automated detection program includes program instructions. When a processor of an electronic device executes the above-mentioned execution instructions, the processor executes some or all of the steps described in the first aspect.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer-readable storage medium storing an automated detection program, and the automated detection program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0017] By implementing the embodiment of the present application, the network type information of the multiple automated detection modules and the historical detection service information of the previous detection cycle are obtained; according to the historical detection service information, the network type information and the pre-trained resource allocation model, the communication resource allocation strategy of the multiple automated detection modules in the current detection cycle is determined, and the communication resource allocation strategy is used to limit the amount of resources and resource types allocated to the multiple automated detection modules, and the amount of resources and resource types are used to realize information transmission; according to the communication resource allocation strategy, the multiple automated detection modules are communicated and connected, and the detection control operation is performed, and the detection control operation is used to make the multiple automated detection modules start to execute the corresponding module functions respectively; the detection result of the current detection cycle is obtained from the multiple automated detection modules, and the detection result is used to indicate the qualified status of the communication module and the actual detection service volume of the current detection cycle, and the actual detection service volume is used to predict the detection service volume of the next detection cycle. In this way, the present application establishes a combination of an automated detection pipeline and a 5G private network, improves the test reliability and communication transmission capacity of the factory assembly line, and through the communication resource allocation strategy of each detection cycle, it is conducive to reducing resource preemption and improving the flexibility of communication connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0019] Figure 1 It is a schematic diagram of the architecture of a communication module automatic detection system provided in an embodiment of the present application;

[0020] Figure 2 It is a flow chart of an automated detection method provided in an embodiment of the present application;

[0021] Figure 3 It is a flow chart of an automated detection pipeline provided in an embodiment of the present application;

[0022] Figure 4 This is a system architecture diagram of a partial communication module automatic detection system provided by an embodiment of the present application;

[0023] Figure 5 It is a structural schematic diagram of an automated detection device proposed in an embodiment of the present application;

[0024] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an optional example also includes steps or units that are not listed, or in an optional example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0027] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] The automated detection method, device, readable storage medium, and electronic device provided in the embodiments of the present application can be applied to Figure 1 Communication module automation detection system shown, please refer to Figure 1 , Figure 1 It is a schematic diagram of the architecture of a communication module automatic detection system provided in an embodiment of the present application. The communication module automatic detection system 100 includes a computing platform 110, an automatic detection module 120, a hybrid gateway terminal device 130 and a base station 140.

[0029] Among them, the computing platform 110 can be a computing platform or computing device for processing a large number of computing tasks and storing data. In this solution, a pre-trained resource allocation model is deployed on the computing platform 110 to execute the allocation of communication resources; specifically, the computing platform 110 can be a MEC (Mobile Edge Computing) service platform, which can reduce network transmission delay and meet the key requirements of industry digitalization in terms of agile connection, real-time business, data optimization, application intelligence and security; the computing platform 110 can also be used to collect data. A hybrid gateway terminal device 130 and a base station 140 are also required between the computing platform 110 and the automated detection module 120 to forward communication data.

[0030] Among them, the hybrid gateway terminal device 130 is a device used to realize the automation detection module 120 and connect to the network. It can connect various sensors and actuators to realize data collection, transmission and processing; specifically, the hybrid gateway terminal device 130 can be an industrial Internet of Things gateway CPE (Customer Premises Equipment). The base station 140 refers to a network infrastructure deployed in an industrial environment, which is used to provide high-speed, low-latency and high-reliability wireless communication services. The base station 140 is between the computing platform 110 and the hybrid gateway terminal device 130, and can be used to forward data. The base station 140 can be a 5G base station. The automation detection module 120 may include multiple modules on the assembly line. For example, the automation detection module 120 may include but is not limited to one or more of a transportation module, a loading module, a detection module, and a unloading module.

[0031] Based on this, the present application provides an automated detection method, device, readable storage medium, and electronic device. The present application is described in detail below in conjunction with the accompanying drawings.

[0032] See also Figure 2 , Figure 2 1 is a flow chart of an automated detection method provided in an embodiment of the present application, the method is applied to a computing platform of a communication module automated detection system, the communication module automated detection system also includes multiple automated detection modules. Figure 2 As shown, the method comprises the following steps:

[0033] S210: Obtain network type information of the plurality of automatic detection modules and historical detection service information of the previous detection cycle.

[0034] Among them, the detection cycle can be the time required for a complete detection task, the previous detection cycle is the cycle of the previous detection task, and a detection task can include at least one detection. Network type information may include network communication mode and network slice type; specifically, the network communication mode may be one or more wireless network technologies, such as 5G network, 4G network, wifi, etc.; specifically, the network slice type may include but is not limited to ultra-reliable low latency communications (ultra-reliable low latency communications, uRLLC), enhanced mobile broadband (enhanced mobilebroadband, eMBB). The automated detection module is a module used for automated detection on the assembly line, such as a transportation module, a loading module, a detection module, an unloading module, etc. Each module may include at least one detection device, and the detection device is not limited to the device for detection, and may be a device on the assembly line. Historical detection service information may include the specific service volume in the previous detection cycle, and the historical detection service information may be used to predict the service information of the current detection cycle.

[0035] The network type information can be directly obtained or determined by obtaining other relevant information. If the network type information is directly obtained, it means that the network type information is pre-set and can be directly retrieved; if it is determined by obtaining other relevant information, it means that the network type information can be adjusted in each detection cycle.

[0036] In a possible embodiment, the obtaining of network type information of the multiple automated detection modules and historical detection service information of the previous detection cycle includes: obtaining multiple service feature information of the multiple automated detection modules, the multiple service feature information characterizing the working characteristics and requirements of the multiple automated detection modules; performing a network matching operation based on the multiple service feature information to determine the network type information matched by each automated detection module; and obtaining the historical detection service information of the previous detection cycle.

[0037] The service feature information represents the working characteristics and requirements of the automatic detection module, such as the need for high operating accuracy, real-time communication, large data transmission volume, high data transmission efficiency, etc. Then, a network matching operation is performed according to the service feature information, and the network matching operation is used to match the appropriate network type to the automatic detection module, that is, to match the network type information.

[0038] Specifically, multiple business feature information corresponding to multiple automated detection modules is obtained, each automated module can correspond to at least one business feature information, and then the business feature information is parsed to extract features to obtain multiple feature vectors, and clustering operations are performed based on the multiple feature vectors and multiple preset target feature centers, wherein the preset multiple target feature centers are feature centers corresponding to each network type allocation method, so that multiple feature vectors are clustered around the target feature center to obtain the network type corresponding to each automated detection module, and then the network type information is obtained. The specific method of the above clustering operation is not limited here, and can be but not limited to the K-means clustering method.

[0039] For example, if the automated detection module includes a transportation module, a loading module, an unloading module, and a detection module. The transportation module is used to transport the target object to be detected. For example, in the present application, the target object can be a communication module. The transportation module includes multiple transport carts. Each transport cart performs receiving and forwarding services and real-time forwarding requirements for positioning data. At the same time, emergencies may occur during the transportation of the communication module, and timely emergency processing is required, which places great demands on high reliability and low latency performance indicators. By performing the above method, a suitable network type is matched for the transportation module, and network type information is obtained. For example, the corresponding network type matched to the transportation module is uRLLC. Similarly, the loading module and the unloading module are used to bind or unbind the target object from the transportation module, which requires a large amount of binding and unbinding data to be generated and transmitted. The relevant binding data and unbinding data are continuously generated, which has a great demand for large bandwidth resource transmission. At the same time, it is also necessary to quickly forward to the MEC edge service platform, so as to prepare the production management system to achieve the overall goal of fast and accurate data analysis and processing. By performing the above method, the loading module and the unloading module are matched with a suitable network type, and the network type information is obtained. For example, the loading module and the unloading module are matched with the corresponding network type of eMBB. The unloading and loading modules may also have a robotic arm device for clamping and other operations, which requires low latency and high reliability of data transmission. By performing the above method, the loading module and the unloading module are matched with a suitable network type, and the network type information is obtained. For example, the loading module and the unloading module are matched with the corresponding network type of uRLLC. Similarly, the detection module is used to detect the target object, which requires uninterrupted communication and generates a large amount of detection data. Therefore, there is a great demand for large bandwidth resource transmission, and high transmission efficiency is also required. By performing the above method, the transportation module is matched with a suitable network type, and the network type information is obtained. For example, the transportation module is matched with the corresponding network type of eMBB.

[0040] For example, if the automated detection module includes a transportation module, a loading module, an unloading module, and a detection module. The transportation module is used to transport the target object to be detected. For example, in the present application, the target object can be a communication module. The transportation module includes multiple transport carts. Each transport cart performs receiving and forwarding services and real-time forwarding requirements for positioning data. At the same time, emergencies may occur during the transportation of the communication module, and timely emergency processing is required, which places great demands on high reliability and low latency performance indicators. By performing the above method, a suitable network type is matched for the transportation module, and network type information is obtained. For example, the corresponding network type matched to the transportation module is uRLLC. Similarly, the loading module and the unloading module are used to bind or unbind the target object from the transportation module, which requires a large amount of binding and unbinding data to be generated and transmitted. The relevant binding data and unbinding data are continuously generated, which has a great demand for large bandwidth resource transmission. At the same time, it is also necessary to quickly forward to the MEC edge service platform, so as to prepare the production management system to achieve the overall goal of fast and accurate data analysis and processing. By performing the above method, the loading module and the unloading module are matched with a suitable network type, and the network type information is obtained. For example, the loading module and the unloading module are matched with the corresponding network type of eMBB. The unloading and loading modules may also have a robotic arm device for clamping and other operations, which requires low latency and high reliability of data transmission. By performing the above method, the loading module and the unloading module are matched with a suitable network type, and the network type information is obtained. For example, the loading module and the unloading module are matched with the corresponding network type of uRLLC. Similarly, the detection module is used to detect the target object, which requires uninterrupted communication and generates a large amount of detection data. Therefore, there is a great demand for large bandwidth resource transmission, and high transmission efficiency is also required. By performing the above method, the transportation module is matched with a suitable network type, and the network type information is obtained. For example, the transportation module is matched with the corresponding network type of eMBB.

[0041] It can be seen that in this embodiment, by acquiring the business feature information of the automatic detection module and performing feature matching, a more suitable network type is provided for the automatic detection module, and network type information is obtained, which can improve the test reliability, communication transmission capability and flexibility of the factory assembly line.

[0042] In a possible embodiment, the business characteristic information includes service level requirement characteristics, and the service level requirement characteristics characterize the delay requirement characteristics and data volume characteristics of each of the automated detection modules; the network type information includes a network communication mode and a network slice type; the network matching operation is performed based on the multiple business characteristic information to determine the network type information matched by a single automated detection module, including: determining the network communication mode according to the delay requirement characteristics; performing a weight analysis on the delay requirement characteristics and the data volume characteristics to obtain a feature weight value; and determining the network slice type according to the feature weight value.

[0043] Among them, the service level requirement characteristics can be the delay requirement characteristics and data volume characteristics used to characterize the automated detection module, and specifically can be the data volume size, data transmission efficiency, reliability, etc. that the automated detection module needs to transmit data. Network communication modes can include 5G and wifi modes, and network slice types include eMBB and uRLLC. The network communication mode is decided based on the delay requirement characteristics, and the network slice type is decided based on the delay requirement characteristics and the data volume. Specifically, a weight analysis is performed through the delay requirement characteristics and the data volume characteristics, and then the network slice type is decided based on the feature weight value.

[0044] Among them, multiple preset data levels of multiple categories can be set, each data feature corresponds to at least one level, and the level is divided according to the data feature value and at least one level to obtain the level of business feature information of each automatic detection module. The higher the level, the greater the feature weight value, and the lower the level, the greater the feature weight value. It can be set according to actual needs and is not limited here. The type of network slice is determined according to the size of the feature weight value corresponding to each automatic detection module.

[0045] In a possible embodiment, the delay requirement feature includes a first delay requirement feature and a second delay requirement feature, the data volume feature includes a first data volume feature and a second data volume feature; the network type information includes a network communication mode and a network slice type, the network communication mode includes a first communication mode and a second communication mode, and the network slice type includes a first slice type and a second slice type;

[0046] The performing a network matching operation based on the multiple service feature information to determine the network type information matched by a single automation module includes:

[0047] If the delay requirement characteristic of the automation module is the first delay requirement characteristic, and the data volume characteristic is the first data volume characteristic, then the corresponding network communication mode is determined to be the first communication mode and the second communication mode; in other possibilities, the corresponding network communication mode may be the first communication mode or the second communication mode. If the delay requirement characteristic of the automation module is the first delay requirement characteristic or the second delay requirement characteristic, and the data volume characteristic is the first data volume characteristic, the network slice type is the first slice type; if the data volume characteristic is the first data volume characteristic and the delay requirement characteristic is the second delay requirement characteristic, the network slice type is the second slice type.

[0048] Among them, the first data volume characteristic represents a large amount of data, the second data volume characteristic represents a small amount of data, the first delay requirement characteristic may be a high degree of requiring a smaller delay, the second delay requirement characteristic may be a low degree of requiring a smaller delay, the first communication mode and the second communication mode may be 5G and WIFI, the first slice type may be uRLLC, and the second slice type may be eMBB.

[0049] It can be seen that in this embodiment, the characteristics are divided into multiple levels, and various business characteristics are comprehensively considered, which can improve the test reliability, communication transmission capability and flexibility of the factory assembly line.

[0050] S220, determining the communication resource allocation strategy of the multiple automated detection modules in the current detection cycle according to the historical detection service information, the network type information and the pre-trained resource allocation model, wherein the communication resource allocation strategy is used to limit the amount and type of resources allocated to the multiple automated detection modules, and the amount and type of resources are used to realize information transmission.

[0051] Among them, communication resources specifically refer to slice resources. The amount of resources contained in each slice may not be fixed, but dynamically allocated based on different needs and scenarios. In this embodiment, the pre-trained communication resource allocation model is used for analysis, and the adapted communication resource allocation strategy is obtained in combination with the network type information. The above communication resource allocation model can be a kernel reinforcement learning (KBRL) model, an A2C model, a NAF model, etc., which is not limited here.

[0052] Among them, the communication resource allocation strategy includes a network type allocation strategy and an allocation strategy for the resource amount corresponding to the network type. Specifically, the network type allocation strategy is determined according to the network type information, the resource allocation amount strategy is determined according to the resource allocation amount determined by the resource allocation model, and then the communication resource allocation strategy is obtained by synthesizing the network type allocation strategy and the resource allocation amount strategy.

[0053] In each detection cycle, it is necessary to apply for and determine the communication resource allocation strategy.

[0054] Specifically, the automated detection module includes at least one automated detection device, and the communication resource allocation strategy of the multiple automated detection modules in the current detection cycle is determined based on the historical detection service information, the network type information and a pre-trained resource allocation model, including: determining the corresponding multiple communication resource allocation amounts of the at least one automated detection device of the multiple automated detection modules based on the historical detection service information, the network type information and the pre-trained resource allocation model, the communication resource allocation amount representing the communication capability of the corresponding at least one automated detection device; determining the communication resource allocation strategy for each of the automated detection devices based on the multiple communication resource allocation amounts and the network type information.

[0055] In a possible embodiment, the pre-trained resource allocation model includes a pre-trained core reinforcement learning model, the service feature information also includes a target service level feature, and the determining of the corresponding multiple communication resource allocation amounts of the at least one automated detection device of the multiple automated detection modules based on historical detection service information, network type information and the pre-trained resource allocation model includes: determining historical resource allocation information and historical data quality information based on the historical detection service information; determining the communication resource allocation amount corresponding to each of the automated detection devices based on the historical resource allocation information, the historical data quality information, the target service level feature and the core reinforcement learning model.

[0056] Among them, the pre-trained kernel reinforcement learning model, the target service level feature can specifically be a service level agreement (Service Level Agreement, SLA), the historical detection business information includes the historical detection business volume, and the target service level feature can be the target business feature information, that is, the business feature information that needs to be achieved. The historical detection business information can extract resource allocation information and specific data. Specifically, the historical resource allocation information and the historical data quality information are determined according to the historical detection business information, and then the historical resource allocation information, the historical data quality information, and the target service level feature are input into the pre-trained resource allocation model to determine the resource allocation amount of multiple automated detection modules.

[0057] For example, the state space and action space definitions in the pre-trained kernel reinforcement learning model include the following: Let K be the set of k network slices coexisting in the RF interface, let K = 5 slices system scenario, including 3 eMBB slices and 2 uRLLC slices, C is the total number of time-frequency resource blocks RBs allocated between these slices, that is, the historical resource allocation information. In each decision stage n = 1, 2..., each slice i∈K, It is defined as the observation value of each slice received by the agent between stages n-1 and n (including traffic, average signal-to-interference-noise ratio, etc., collectively referred to as observation value). It represents the number of time-frequency resource blocks (RBs) allocated to each slice by the n agents in the decision stage, which is a specific value. It is expressed as system observations, that is, the set of observations of each slice in the nth stage, that is, the historical data quality information. represents the set of resources allocated to each slice at stage n. Since the decision is made by the random variable Determined, so the slice assigns the value is also a random variable. (i) It is an indicator function that contains the target service level characteristics, that is, all the QoS (quality of service) targets of each slice, and notifies the SLA implementation of the slice at each stage. Specifically Indicates that the SLA of the i-th slice is violated between decision stages n and n+1. If I (i) If s is 0, it means that there is no SLA violation. Strategy π is defined as receiving system observation s n-1 , providing control action a n And allocate resources to each slice.

[0058] Will Defined as: the observations received at the end of the given previous phase To predict the next stage Whether the SLA is met, that is, is the prediction result. Inaccurate prediction may lead to Too many SLA violations, so we also need to set the error probability δ. (i) As by In the observation-action pair The probability that the predicted value given above is a false negative is Probability of misjudgment: Then, using the error function And the reward function The model is trained to approximate the dynamics of the environment. Then a series of samples to build a sample pool. This data can be used to train the environment model and improve the policy. The problem is transformed into a one-step forward control problem, where each control action a n should be allowed, and a n Each of It should be bounded by the error probability δ based on compliance with the SLA:

[0059]

[0060] Among them, the environment model is used to generate a large number of virtual trajectories under the current strategy And generate samples through simulation to evaluate the effects of different strategies. Update the strategy based on the generated trajectory First, estimate the value function for each state-action pair Through dynamic programming, the value function update formula is as follows:

[0061]

[0062] Among them, α and γ are different attenuation factors. When updating the strategy, the operation is selected according to the maximum Q value, and the optimal strategy is extracted. After multiple model learning and strategy updates, the agent can test the effect of the current strategy in the real environment, accumulate new data to update the model for continuous optimization. Repeating the above steps, the agent alternately trains in the real environment and the model environment, and optimizes the strategy until convergence is reached.

[0063] It can be seen that in this embodiment, the use of the kernel reinforcement learning model to predict the allocation amount of communication resources can avoid the waste of resources caused by allocating too many resources at the beginning, can allocate resources more reasonably, show higher efficiency in resource allocation, and have a better resource allocation optimization effect.

[0064] S230, establishing communication connections with the multiple automatic detection modules according to the communication resource allocation strategy, and performing a detection control operation, wherein the detection control operation is used to enable the multiple automatic detection modules to respectively start executing corresponding module functions.

[0065] Among them, resource allocation is performed according to the communication resource allocation strategy, so that each automatic detection module performs communication connection according to the resource allocation strategy, and then performs subsequent detection and communication. Specifically, after the communication connection is established, a detection control operation is performed to control each automatic detection module to perform automatic detection.

[0066] In a possible embodiment, the automated detection module includes a transport module, a loading module, a detection module, and an unloading module in a sequential relationship, and the communication connection with the multiple automated detection modules is established according to the communication resource allocation strategy, and a detection control operation is performed, including: configuring a communication connection channel with the automated detection module according to the communication resource allocation strategy; sending a start detection instruction to the transport module, the loading module, the unloading module, and the detection module based on the communication connection channel, so that the transport module is used to transport the communication module, the loading module is used to bind information to the communication module, the detection module is used to perform appearance detection and / or functional detection on the communication module, and the unloading module is used to unbind the communication module and send the detection result to the production management system.

[0067] Among them, the transport module, loading module, detection module, and unloading module have a sequential relationship. The target detection object, i.e., the communication module, needs the transport module to transport, the loading module to bind, the detection module to detect, and the unloading module to unbind. The transport module, loading module, detection module, and unloading module in the above-mentioned automated detection module all establish communication connections with other parts of the automated detection system according to the content allocated by the communication resource allocation strategy.

[0068] Specifically, the detection module also includes an appearance detection module, a function detection module, a re-inspection module, and a waste disposal module. Among them, the appearance detection module is used to test whether the appearance of the communication module is qualified, the function detection module is used to test whether the function of the communication module is qualified, and the re-inspection module is used to perform re-inspection in the case of unqualified appearance detection and unqualified function detection. If the re-inspection module still determines that it is unqualified, the waste disposal is carried out through the waste disposal module.

[0069] Please refer to Figure 3 , Figure 3 is a flow chart of an automated detection pipeline provided in an embodiment of the present application, such as Figure 3 As shown, the automated inspection line includes a loading module, an appearance inspection module, a function inspection module, a re-inspection module, an unloading module, a transportation module, and a computing platform.

[0070] The computing platform uses edge computing to quickly process and analyze real-time data, receives and forwards the inspection tasks issued by the production management system, and locally stores the positioning information of the transport module, the pallet RFID information bound to the communication module, the appearance function test results of each communication module, and the qualified identification information, and uploads them to the production management system at the same time. The above pallet is an object that is loaded on the equipment to be inspected when transporting the equipment to be inspected, and its form includes but is not limited to a pallet, and can also be other objects. The above computing platform can be a MEC edge service platform.

[0071] The transport module includes but is not limited to AGV, conveyor tracks, unmanned forklifts, etc., which are used to connect the modules and are responsible for transmitting the communication module and the pallet and objects carrying the communication module. The AGV sends positioning information to the MEC edge service platform in real time for auxiliary positioning.

[0072] Among them, the loading module and the unloading module both include at least one information collection device and at least one robotic arm device. The robotic arm is used to transfer the target detection object, i.e., the communication module, from the transportation module to the detection module, or to transfer the target detection object from the detection module to the transportation module. The information collection device performs information binding or information unbinding of the target detection device.

[0073] The detection control operation is to issue or forward a control command.

[0074] Specifically, the specific inspection process includes: the MEC edge service platform sends the transport instruction to the AGV car group, and the AGV car starts to transport the communication module between the warehouse and the automated inspection assembly line; the loading robot arm of the loading module takes the AGV car out of the warehouse and transports the communication module to be inspected there and clamps it into the pallet; the information collection equipment automatically performs RFID scanning on the pallet with the communication module to be inspected to achieve information binding, and sends the binding data to the MEC edge service platform; then the pallet with the communication module to be inspected is transported to the appearance inspection module through the transportation module; the appearance inspection The testing module receives the pallet with the communication modules to be tested, starts to perform appearance testing on the communication modules and sends the appearance testing results to the MEC edge service platform, and then transports the pallet with the communication modules that have been visually tested to the function testing module through the transportation module; the function testing module receives the pallet with the communication modules that have been visually tested, starts to perform function testing on the communication modules and sends the function testing results to the MEC edge service platform; if the function testing result of the communication module is qualified, the pallet with the communication modules that have been qualified is transported to the unloading module through the transportation module; if the communication module is not qualified, the pallet is transported to the unloading module through the transportation module; If the functional test result of the communication module is unqualified, the pallet with the communication module with the unqualified functional test result is transported to the re-inspection module through the transportation module; the re-inspection module receives the pallet with the communication module with the unqualified functional test result, starts a new round of functional test on the communication module and sends the functional test result to the MEC edge service platform; if the functional test result of the communication module is qualified, the pallet with the communication module with the qualified functional test result is transported to the unloading module through the transportation module; if the functional test result of the communication module is unqualified, the corresponding communication module is updated to the MEC edge service platform The unqualified test results are obtained and the pallets with communication modules with unqualified functional test results are transported to the idle area for waste treatment through the transportation module; the unloading module receives the pallets with communication modules that have been functionally tested, and starts to classify the qualified communication modules by laser engraving. The information collection equipment automatically performs RFID scanning on the pallets with qualified communication modules to unbind the information and upload the classification results to the MEC edge service platform; the unloading robot arm automatically sorts and packs the communication modules with laser engraving marks, and the AGV car then transports the communication modules with qualified marks to the warehouse.

[0075] It can be seen that in this embodiment, the communication resource allocation strategy is combined with the automated detection module, so that the various modules included in the automated module have different communication resource contents, thereby improving the test reliability and communication transmission capacity of the factory assembly line. The communication resource allocation strategy of each detection cycle is helpful to reduce resource preemption and improve the flexibility of communication connections.

[0076] In a possible embodiment, the communication module automatic detection system also includes a hybrid gateway terminal device, a production management system and a base station; the hybrid gateway terminal device includes multiple communication modules corresponding to the multiple network communication modes, and the hybrid gateway terminal is used to access the multiple automatic detection devices of the multiple automatic detection modules, and establish a communication connection with the computing platform through the base station; the base station is used to establish a connection between the hybrid gateway terminal device and the edge computing platform, and the production management system is used to generate and issue control instructions, and the control instructions are used to control the multiple automatic detection modules to perform the corresponding module functions.

[0077] Among them, the production management system is used to send inspection tasks to the computing platform, receive and process the transportation module positioning information uploaded by the computing platform, the pallet RFID information bound to the communication module, the appearance function test results of each communication module, and the qualified identification information, and optimize the analysis of the test results in combination with the machine learning algorithm to achieve efficient operation. The base station is located between the computing platform and the hybrid gateway terminal device and is used to forward data. The hybrid gateway terminal device can be a 5G industrial Internet of Things gateway customer front-end device (Customer Premise Equipment, CPE). The base station can be a 5G base station.

[0078] Please refer to Figure 4 , Figure 4 is a system architecture diagram of a partial communication module automatic detection system provided in an embodiment of the present application, such as Figure 4 As shown, the automatic detection system of some communication modules includes a hybrid gateway terminal device, a production management system and a base station, and an automatic detection module. The automatic detection module includes a transportation module, a loading module, a detection module, and an unloading module.

[0079] Specifically, a heterogeneous converged network is formed at the access network, and the 5G service terminal and the Wi-Fi service terminal are connected to the hybrid gateway terminal device. As a wireless terminal high-speed and low-latency access device, the hybrid gateway terminal device receives wireless signals from each terminal of the communication module detection pipeline, uses edge computing capabilities to pre-process and analyze the detection data, reduces the amount of data transmitted to the Internet, and improves the response speed of real-time monitoring of the terminal device operation status. It has a built-in 5G modem and transmits it to the core network in real time through a 5G base station, and finally connects to the Internet network; at the same time, the hybrid gateway terminal device CPE also provides a Wi-Fi wireless communication interface, converts 5G signals into Wi-Fi signals and provides them to each detection stand-alone device, which can support multiple detection stand-alone devices to access the Internet at the same time, realize automatic control and adjustment of the production process, and greatly improve production efficiency and spectrum efficiency. After the information collection equipment in the loading module and the unloading module obtains the binding information and unbinding information of the communication module and the pallet through RFID scanning, the binding and unbinding data are sent to the hybrid gateway terminal device CPE, and the hybrid gateway terminal device CPE then forwards the binding and unbinding data to the MEC edge service platform through the 5G base station, and finally the production management system performs unified scheduling optimization. Similarly, there are also robotic arms, appearance inspection stand-alone machines, function inspection stand-alone machines, and transportation modules. In view of the business requirements that the robotic arm needs to accurately grasp each communication module and has high standards for high-reliability and low-latency transmission, it is considered that the robotic arm is connected to the 5G network and Wi-Fi network at the same time, and dual connections are used to achieve stable, safe, and accurate grasping of communication modules. After the robotic arms in the loading and unloading module and the re-inspection module are connected to the 5G network and Wi-Fi network inside the communication module automated inspection assembly line, the MEC edge service platform sends task instructions to them through the 5G base station and the hybrid gateway terminal device CPE, and the robotic arms start assembly line operations. After the appearance inspection stand-alone machine and the function inspection stand-alone machine perform relevant inspections on the communication module, the inspection data is sent to the hybrid gateway terminal device CPE, and the hybrid gateway terminal device CPE then forwards the inspection data to the MEC edge service platform through the 5G base station. When performing the transportation task, the transportation module sends its own positioning data to the hybrid gateway terminal device CPE in real time, and the hybrid gateway terminal device CPE then forwards the positioning data to the MEC edge service platform through the 5G base station. After the various re-inspection test machines in the re-inspection module and the terminal equipment such as the manual re-inspection computer are connected to the internal Wi-Fi network of the communication module automated inspection line, the communication module is subjected to relevant inspections, and the inspection data is sent to the 5G base station through the 5G industrial Internet of Things gateway CPE, and the 5G base station then forwards the inspection data to the MEC edge service platform.

[0080] It can be seen that in this embodiment, setting up a hybrid gateway terminal device can support multiple stand-alone detection devices to access the Internet at the same time, realize automatic control and adjustment of the production process, and greatly improve production efficiency and spectrum efficiency.

[0081] S240, obtaining the detection results of the current detection cycle from the multiple automatic detection modules, the detection results are used to indicate the qualification status of the communication module and the actual detection business volume of the current detection cycle, and the actual detection business volume is used to predict the detection business volume of the next detection cycle.

[0082] Among them, after the detection is started, the detection results transmitted by multiple automated detection modules are obtained, and the detection results include the detection results of each automated detection module, as well as the generated data information, specifically including the binding information and unbinding information of the communication module and the pallet obtained by RFID scanning, etc. The information also included in the detection results is not limited here.

[0083] The actual detection traffic volume obtained according to the detection result can be used to predict the communication resource allocation volume based on the pre-trained resource allocation model in the next detection cycle.

[0084] It can be seen that by implementing the embodiment of the present application, the network type information of the multiple automated detection modules and the historical detection service information of the previous detection cycle are obtained; according to the historical detection service information, the network type information and the pre-trained resource allocation model, the communication resource allocation strategy of the multiple automated detection modules in the current detection cycle is determined, and the communication resource allocation strategy is used to limit the amount of resources and resource types allocated to the multiple automated detection modules, and the amount of resources and resource types are used to realize information transmission; according to the communication resource allocation strategy, the multiple automated detection modules are communicated and connected, and the detection control operation is performed, and the detection control operation is used to make the multiple automated detection modules start to execute the corresponding module functions respectively; the detection result of the current detection cycle is obtained from the multiple automated detection modules, and the detection result is used to indicate the qualified status of the communication module and the actual detection service volume of the current detection cycle, and the actual detection service volume is used to predict the detection service volume of the next detection cycle. In this way, the present application establishes a combination of an automated detection pipeline and a 5G private network, improves the test reliability and communication transmission capacity of the factory assembly line, and through the communication resource allocation strategy of each detection cycle, it is conducive to reducing resource preemption and improving the flexibility of communication connection.

[0085] See also Figure 5 , Figure 5 : is a structural diagram of an automated detection device proposed in an embodiment of the present application, which is applied to a production management system of a communication module automated detection system, wherein the communication module automated detection system also includes multiple automated detection modules, and the automated detection device 500 includes: a first acquisition module 510, an allocation module 520, a connection module 530, and a second acquisition module 540. Among them,

[0086] A first acquisition module 510 is used to acquire network type information of the plurality of automatic detection modules and historical detection service information of the previous detection cycle;

[0087] an allocation module 520, configured to determine a communication resource allocation strategy for the multiple automatic detection modules in a current detection cycle according to the historical detection service information, the network type information, and a pre-trained resource allocation model, wherein the communication resource allocation strategy is used to limit the amount and type of resources allocated to the multiple automatic detection modules, and the amount and type of resources are used to implement information transmission;

[0088] A connection module 530, configured to establish communication connections with the plurality of automated detection modules according to the communication resource allocation strategy, and to perform a detection control operation, wherein the detection control operation is configured to enable the plurality of automated detection modules to respectively start executing corresponding module functions;

[0089] The second acquisition module 540 is used to obtain the detection results of the current detection cycle from the multiple automatic detection modules, and the detection results are used to indicate the qualification status of the communication module and the actual detection business volume of the current detection cycle. The actual detection business volume is used to predict the detection business volume of the next detection cycle.

[0090] In a possible embodiment, the first acquisition module 510, in acquiring the network type information of the plurality of automatic detection modules and the historical detection service information of the previous detection cycle, is specifically used to:

[0091] Acquire multiple business feature information of the multiple automatic detection modules, where the multiple business feature information represents the working characteristics and requirements of the multiple automatic detection modules;

[0092] Performing a network matching operation based on the multiple service feature information to determine the network type information matched by each automated detection module;

[0093] Obtain historical detection service information of the previous detection cycle.

[0094] In a possible embodiment, the service feature information includes a service level requirement feature, and the service level requirement feature characterizes a delay requirement feature and a data volume feature of each of the automated detection modules; the network type information includes a network communication mode and a network slice type; and the allocation module 520, in performing a network matching operation based on the multiple service feature information and determining the network type information matched by a single automated detection module, is specifically used to:

[0095] Determining the network communication mode according to the delay requirement characteristics;

[0096] Performing a weight analysis on the delay demand feature and the data volume feature to obtain a feature weight value;

[0097] The network slice type is determined according to the characteristic weight value.

[0098] In a possible embodiment, the automated detection module includes at least one automated detection device; the allocation module 520 is specifically used to determine the communication resource allocation strategies of the multiple automated detection modules in the current detection cycle according to the historical detection service information, the network type information and the pre-trained resource allocation model:

[0099] Determine, according to historical detection service information, network type information and a pre-trained resource allocation model, a plurality of communication resource allocation amounts corresponding to the at least one automated detection device of the plurality of automated detection modules, wherein the communication resource allocation amount represents the communication capability of the corresponding at least one automated detection device;

[0100] The communication resource allocation strategy for each type of the automated detection equipment is determined according to the multiple communication resource allocation amounts and the network type information.

[0101] In a possible embodiment, the pre-trained resource allocation model includes a pre-trained kernel reinforcement learning model, and the service feature information also includes a target service level feature; the allocation module 520 determines the corresponding multiple communication resource allocation amounts of the at least one automated detection device of the multiple automated detection modules according to the historical detection service information, the network type information and the pre-trained resource allocation model, specifically for:

[0102] Determine historical resource allocation information and historical data quality information according to the historical detection service information;

[0103] The communication resource allocation amount corresponding to each of the automated detection devices is determined according to the historical resource allocation information, the historical data quality information, the target service level characteristics, and the core reinforcement learning model.

[0104] In a possible embodiment, the automated detection module includes a transport module, a loading module, a detection module, and a unloading module in a sequential relationship; the connection module 530 is specifically used to communicate with the multiple automated detection modules according to the communication resource allocation strategy and perform detection control operations:

[0105] configuring a communication connection channel with the automated detection module according to the communication resource allocation strategy;

[0106] Based on the communication connection channel, a start detection instruction is sent to the transport module, loading module, unloading module, and detection module, so that the transport module is used to transport the communication module, the loading module is used to bind information to the communication module, the detection module is used to perform appearance detection and / or functional detection on the communication module, and the unloading module is used to unbind the communication module and send the detection result to the production management system.

[0107] In a possible embodiment, the communication module automatic detection system also includes a hybrid gateway terminal device, a production management system and a base station; the hybrid gateway terminal device includes multiple communication modules corresponding to the multiple network communication modes, and the hybrid gateway terminal is used to access the multiple automatic detection devices of the multiple automatic detection modules, and establish a communication connection with the computing platform through the base station; the base station is used to establish a connection between the hybrid gateway terminal device and the edge computing platform, and the production management system is used to generate and issue control instructions, and the control instructions are used to control the multiple automatic detection modules to perform the corresponding module functions.

[0108] It is worth noting that the specific functional implementation of the automatic detection device 500 is shown in the above Figures 2 to 4 The description of the automated detection method shown, for example, the first acquisition module 510 is used to implement the relevant content of executing S210. Each unit or module in the automated detection device 500 can be separately or completely combined into one or more other units or modules to constitute, or one (some) of the units or modules can also be divided into multiple smaller units or modules in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In practical applications, the function of a unit (or module) is implemented by multiple units (or modules), or the function of multiple units (or modules) is implemented by one unit (or module).

[0109] It can be seen that the automated detection device described in the embodiment of the present application, by implementing Figure 2Embodiment, obtain the network type information of the multiple automated detection modules and the historical detection service information of the previous detection cycle; determine the communication resource allocation strategy of the multiple automated detection modules in the current detection cycle according to the historical detection service information, the network type information and the pre-trained resource allocation model, the communication resource allocation strategy is used to limit the amount of resources and resource types allocated to the multiple automated detection modules, and the amount of resources and resource types are used to realize information transmission; communicate with the multiple automated detection modules according to the communication resource allocation strategy, and perform detection control operations, the detection control operations are used to make the multiple automated detection modules start to execute corresponding module functions respectively; obtain the detection results of the current detection cycle from the multiple automated detection modules, the detection results are used to indicate the qualified status of the communication module and the actual detection business volume of the current detection cycle, and the actual detection business volume is used to predict the detection business volume of the next detection cycle. In this way, the present application establishes a combination of automated detection pipeline and 5G private network, improves the test reliability and communication transmission capacity of the factory assembly line, and through the communication resource allocation strategy of each detection cycle, it is conducive to reducing resource preemption and improving the flexibility of communication connection.

[0110] See also Figure 6 , Figure 6 It is a structural diagram of an electronic device proposed in an embodiment of the present application. As shown in the figure, the electronic device 600 includes a processor 610, a memory 620, a communication interface 630 and one or more programs 621. The one or more programs 621 are stored in the memory 620 and are configured to be executed by the processor 610.

[0111] The processor 610, the memory 620, and the communication interface 630 are interconnected and perform communication work with each other;

[0112] The memory 620 may be a volatile memory such as a dynamic random access memory DRAM, or a non-volatile memory such as a mechanical hard disk. The memory 620 is used to store a set of executable program codes, and the processor 610 is used to call one or more programs 621 stored in the memory 620, and may execute any part or all of the steps recorded in the above-mentioned automated detection method embodiment.

[0113] Among them, the electronic device 600 may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, vehicle-mounted electronic devices, servers, laptops, mobile Internet electronic devices (MID, Mobile Internet Devices) or wearable electronic devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices.

[0114] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for data exchange, the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.

[0115] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0116] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0117] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0119] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0121] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer electronic device (which can be a personal computer, electronic device or network electronic device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0122] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0123] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An automated detection method, characterized in that: A computing platform applied to a communication module automatic detection system, wherein the communication module automatic detection system further comprises a plurality of automatic detection modules, and the method comprises: Obtain network type information of the multiple automated detection modules and historical detection service information of the previous detection cycle; Determine, according to the historical detection service information, the network type information and the pre-trained resource allocation model, a communication resource allocation strategy for the multiple automatic detection modules in the current detection cycle, wherein the communication resource allocation strategy is used to limit the amount and type of resources allocated to the multiple automatic detection modules, and the amount and type of resources are used to realize information transmission; Establishing communication connections with the plurality of automated detection modules according to the communication resource allocation strategy, and performing detection control operations, wherein the detection control operations are used to enable the plurality of automated detection modules to respectively start executing corresponding module functions; The detection results of the current detection cycle are obtained from the multiple automatic detection modules, and the detection results are used to indicate the qualification status of the communication module and the actual detection business volume of the current detection cycle, and the actual detection business volume is used to predict the detection business volume of the next detection cycle.

2. The method according to claim 1, characterized in that The obtaining of the network type information of the plurality of automatic detection modules and the historical detection service information of the previous detection cycle includes: Acquire multiple business feature information of the multiple automatic detection modules, where the multiple business feature information represents the working characteristics and requirements of the multiple automatic detection modules; Performing a network matching operation based on the multiple service feature information to determine the network type information matched by each automated detection module; Obtain historical detection service information of the previous detection cycle.

3. The method according to claim 2, characterized in that The business characteristic information includes service level requirement characteristics, and the service level requirement characteristics represent delay requirement characteristics and data volume characteristics of each of the automated detection modules; The network type information includes a network communication mode and a network slice type; The performing of a network matching operation based on the plurality of service feature information to determine the network type information matched by a single automatic detection module comprises: Determining the network communication mode according to the delay requirement characteristics; Performing a weight analysis on the delay demand feature and the data volume feature to obtain a feature weight value; The network slice type is determined according to the characteristic weight value.

4. The method according to claim 3, characterized in that The automatic detection module includes at least one automatic detection device, and the communication resource allocation strategies of the multiple automatic detection modules in the current detection cycle are determined according to the historical detection service information, the network type information and the pre-trained resource allocation model, including: Determine, according to historical detection service information, network type information and a pre-trained resource allocation model, a plurality of communication resource allocation amounts corresponding to the at least one automated detection device of the plurality of automated detection modules, wherein the communication resource allocation amount represents the communication capability of the corresponding at least one automated detection device; The communication resource allocation strategy for each type of the automated detection equipment is determined according to the multiple communication resource allocation amounts and the network type information.

5. The method according to claim 4, characterized in that The pre-trained resource allocation model includes a pre-trained kernel reinforcement learning model, the service feature information also includes a target service level feature, and the determining, according to the historical detection service information, the network type information and the pre-trained resource allocation model, a plurality of communication resource allocation amounts corresponding to the at least one automated detection device of the plurality of automated detection modules includes: Determine historical resource allocation information and historical data quality information according to the historical detection service information; The communication resource allocation amount corresponding to each of the automated detection devices is determined according to the historical resource allocation information, the historical data quality information, the target service level characteristics, and the core reinforcement learning model.

6. The method according to any one of claims 1 to 4, characterized in that: The automated detection module includes a transport module, a loading module, a detection module, and a unloading module in a sequential relationship. The communication resource allocation strategy is used to communicate with the plurality of automated detection modules and perform detection control operations, including: configuring a communication connection channel with the automated detection module according to the communication resource allocation strategy; Based on the communication connection channel, a start detection instruction is sent to the transport module, loading module, unloading module, and detection module, so that the transport module is used to transport the communication module, the loading module is used to bind information to the communication module, the detection module is used to perform appearance detection and / or functional detection on the communication module, and the unloading module is used to unbind the communication module and send the detection result to the production management system.

7. The method according to claim 6, characterized in that The communication module automatic detection system also includes a hybrid gateway terminal device, a production management system and a base station; The hybrid gateway terminal device includes a plurality of communication modules corresponding to the plurality of network communication modes, and the hybrid gateway terminal is used to access the plurality of automated detection devices of the plurality of automated detection modules, and establish a communication connection with the computing platform through the base station; The base station is used to establish a connection between the hybrid gateway terminal device and the edge computing platform, the production management system is used to generate and issue control instructions, and the control instructions are used to control the multiple automated detection modules to perform the corresponding module functions.

8. An automated detection device, characterized in that: A production management system applied to a communication module automatic detection system, wherein the communication module automatic detection system further comprises a plurality of automatic detection modules, and the device comprises: A first acquisition module, used to acquire network type information of the plurality of automatic detection modules and historical detection service information of the previous detection cycle; an allocation module, configured to determine, based on the historical detection service information, the network type information and a pre-trained resource allocation model, a communication resource allocation strategy for the multiple automated detection modules in a current detection cycle, wherein the communication resource allocation strategy is configured to limit the amount and type of resources allocated to the multiple automated detection modules, and the amount and type of resources are configured to implement information transmission; a connection module, configured to establish communication connections with the plurality of automatic detection modules according to the communication resource allocation strategy, and to perform a detection control operation, wherein the detection control operation is configured to enable the plurality of automatic detection modules to respectively start executing corresponding module functions; The second acquisition module is used to obtain the detection results of the current detection cycle from the multiple automatic detection modules, and the detection results are used to indicate the qualification status of the communication module and the actual detection business volume of the current detection cycle, and the actual detection business volume is used to predict the detection business volume of the next detection cycle.

9. A computer-readable storage medium, characterized in that: An automated detection program is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 7.

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