Workshop post allocation method and device based on industrial internet of things, terminal and medium

Abstract

The invention discloses a workshop post allocation method and device based on industrial Internet of Things, a terminal and a medium. The method comprises the following steps: acquiring work experience data of an operator in a workshop, work type information data of the operator, skill level data of the operator, skill requirement data of each post in the workshop and equipment parameters of each equipment in the workshop; obtaining a target allocation scheme through a workshop post allocation model according to the work experience data of the operator in the workshop, the work type information data of the operator, the skill level data of the operator, the skill requirement data of each post in the workshop and the equipment parameters of each equipment in the workshop, and the production efficiency of a workshop is optimized. Through the neural network model, the post allocation scheme of the operators in the workshop is accurately obtained, so that the production efficiency of the workshop is improved on the basis of reducing the probability of occurrence of production accidents.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a workshop job allocation method, device, terminal and medium based on industrial Internet of Things. Background Art

[0002] Job allocation in the workshop is a crucial management activity that directly affects the production efficiency, product quality, employee satisfaction and other aspects of the enterprise. Reasonable job allocation can ensure that each employee can maximize their effectiveness in the field they are good at. When employees are assigned to positions that match their skills, they can complete their work tasks faster and more accurately, thereby improving the production efficiency of the entire workshop.

[0003] However, the current manual job allocation method is often based on static rules or conventions, rather than dynamic adjustments based on current actual needs or employees' latest capabilities and performance. This method may lead to unreasonable resource allocation and failure to respond to changes in production plans in a timely manner; at the same time, it fails to make full use of existing data resources when allocating jobs, such as employee performance records, production history data, etc. The lack of data-driven decision-making may lead to inaccurate allocation, failure to fully tap employees' potential, and failure to truly improve the overall production efficiency of the workshop. Summary of the invention

[0004] The main purpose of this application is to provide a workshop job allocation method, device, terminal and medium based on the industrial Internet of Things, aiming to accurately obtain the job allocation plan of operators in the workshop through a neural network model, so as to improve the overall production efficiency of the workshop.

[0005] To achieve the above-mentioned purpose, the present application provides a workshop job allocation method based on industrial Internet of Things, which is applied to a job management system, wherein the job management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes an operator's mobile terminal, and the management platform and the object platform are communicatively connected via the sensor network platform;

[0006] The method comprises:

[0007] Obtaining the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop;

[0008] Through the workshop job allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop and the equipment parameters of each equipment in the workshop, a target allocation plan is obtained to optimize the production efficiency of the workshop.

[0009] Specifically, the workshop job allocation model includes a first input layer, a first embedding layer, a first concatenation layer, a first hidden layer and a first output layer;

[0010] The workshop post allocation model is used to obtain a target allocation plan based on the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each post in the workshop, and the equipment parameters of each equipment in the workshop, including:

[0011] Generate, through the first input layer, a work experience vector corresponding to the work experience data, a work type information vector corresponding to the work type information data, a skill level vector corresponding to the skill level data, a skill requirement vector corresponding to the skill requirement data, and an equipment parameter vector corresponding to the equipment parameter based on the work experience data of the operators in the workshop, the work type information data of the operators, the skill level data of the operators, the skill requirement data of the operators, the skill requirement data of the positions in the workshop, and the equipment parameters of the equipment in the workshop;

[0012] Generate a converted job type information vector according to the job type information vector through the first embedding layer;

[0013] Generate a first splicing vector through the first splicing layer according to the work experience vector, the converted job type information vector, the skill level vector, the skill requirement vector and the equipment parameter vector;

[0014] Generate a first output vector according to the first concatenated vector through the first hidden layer;

[0015] Generate an output value according to the first output vector through the first output layer;

[0016] Based on the output values, the target allocation solution is generated.

[0017] Specifically, the first hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the activation function of the first sub-hidden layer, the activation function of the second sub-hidden layer and the activation function of the third sub-hidden layer are all non-linear activation functions;

[0018] The step of generating a first output vector according to the first concatenated vector through the first hidden layer includes:

[0019] Generate a first intermediate vector according to the first concatenated vector through a first sub-hidden layer;

[0020] Generate a second intermediate vector according to the first intermediate vector through a second sub-hidden layer;

[0021] The first output vector is generated according to the second intermediate vector through the third sub-hidden layer.

[0022] Specifically, the first output layer includes a dense layer, the number of neurons in the first output layer is the same as the number of positions in the workshop, each neuron in the first output layer corresponds to each position in the workshop one by one, the activation function corresponding to the first output layer is a Softmax function, and the output value is a position probability;

[0023] The step of generating an output value according to the first output vector through the first output layer includes:

[0024] The first output vector is input to the first output layer, and the first output vector is converted by the first output layer to obtain the position probability, wherein the position probability is used to characterize the suitability of the operator for each position.

[0025] Specifically, generating the target allocation scheme based on the output value includes:

[0026] Selecting the job probability with the largest value from the job probabilities, and confirming the job corresponding to the job probability with the largest value as the target job;

[0027] Traverse the operating staff in the workshop and generate the target allocation plan based on the target positions.

[0028] Specifically, after obtaining the target allocation scheme through the workshop job allocation model according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, the method further includes:

[0029] sending the target allocation scheme to the mobile terminal;

[0030] In response to the allocation scheme modification instruction sent by the mobile terminal, updating the target allocation scheme to obtain an updated allocation scheme;

[0031] Using the allocation scheme evaluation model, according to the updated allocation scheme, an evaluation score corresponding to the updated allocation scheme is obtained;

[0032] Based on the comparison result between the evaluation score and the preset evaluation threshold, the updated allocation plan is processed. If the evaluation score is not less than the preset evaluation threshold, the updated allocation plan is confirmed as the final implemented workshop position allocation plan; if the evaluation score is less than the preset evaluation threshold, the updated allocation plan is deleted, and the target allocation plan is confirmed as the final implemented workshop position allocation plan.

[0033] Specifically, the allocation scheme evaluation model includes a second input layer, a second embedding layer, a second concatenation layer, a second hidden layer and a second output layer, the number of neurons in the second output layer is 1, and the activation function corresponding to the second output layer is a linear activation function;

[0034] The method of obtaining an evaluation score corresponding to the updated allocation scheme according to the updated allocation scheme through the allocation scheme evaluation model includes:

[0035] Generate an allocation plan input vector according to the updated allocation plan through the second input layer, wherein the allocation plan input vector includes at least one job number, and each job number corresponds to a job in the workshop one by one;

[0036] Generate a job dense vector according to the job number through the second embedding layer;

[0037] Generate a second splicing vector according to the allocation scheme input vector and the job dense vector through the second splicing layer;

[0038] Generate a second output vector according to the second concatenated vector through the second hidden layer;

[0039] The evaluation score is obtained through the second output layer according to the second output vector.

[0040] To achieve the above-mentioned purpose, the present application also provides a workshop job allocation device based on the industrial Internet of Things, which is applied to a job management system, wherein the job management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes an operator's mobile terminal, and the management platform and the object platform are communicatively connected via the sensor network platform;

[0041] The device comprises:

[0042] The first unit is used to obtain the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop;

[0043] The second unit is used to obtain a target allocation plan through a workshop job allocation model according to the work experience data of the operators in the workshop, the job information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, so as to optimize the production efficiency of the workshop.

[0044] To achieve the above-mentioned purpose, the present application also provides a terminal, including a memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any one of the methods provided in the present application.

[0045] To achieve the above objectives, the present application also provides a medium, wherein the medium stores a plurality of instructions, wherein the instructions are suitable for a processor to load to execute the steps in any one of the methods provided in the present application.

[0046] The present application provides a workshop job allocation method, device, terminal and medium based on industrial Internet of Things, which can first obtain the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop; then, through the workshop job allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, an accurate target allocation plan is obtained to optimize the production efficiency of the workshop.

[0047] This application can accurately obtain the job allocation plan for operators in the workshop through a neural network model, so that the workshop can improve production efficiency while reducing the probability of production accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a process flow provided for an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a job management system provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of the structure of a workshop post allocation device based on the industrial Internet of Things provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] 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, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0053] The current manual job allocation method is often based on static rules or conventions, rather than dynamic adjustments based on current actual needs or employees' latest capabilities and performance. This method may lead to unreasonable resource allocation and failure to respond to changes in production plans in a timely manner; at the same time, it fails to make full use of existing data resources when allocating jobs, such as employee performance records, production history data, etc. The lack of data-driven decision-making may lead to inaccurate allocation, inability to fully tap employees' potential, and thus inability to truly improve the overall production efficiency of the workshop.

[0054] Therefore, the embodiments of the present application provide a workshop job allocation method, device, terminal and medium based on industrial Internet of Things to solve practical technical problems.

[0055] In some embodiments, the device may be specifically integrated into an electronic device, which may be a terminal, a server or other device.

[0056] In some embodiments, the server may also be implemented in the form of a terminal.

[0057] Among them, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0058] The terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected via wired or wireless communication, and this application does not limit this.

[0059] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0060] An embodiment of the present application provides a workshop job allocation method based on the industrial Internet of Things. The method can accurately obtain the job allocation plan of operators in the workshop through a neural network model, so that the overall production efficiency of the workshop is improved.

[0061] like Figure 1 and Figure 2 As shown, it is applied to a job management system, which includes a management platform, a sensor network platform and an object platform. The object platform includes an operator's mobile terminal, and the management platform and the object platform are communicatively connected via the sensor network platform.

[0062] In some embodiments, the management platform is responsible for the management and control of the entire system, including task scheduling, data processing, etc.

[0063] The object platform may refer to an object-oriented design framework for managing and processing various data objects in the system, and the object platform provides a structured way to organize, store and manipulate data, making the system more flexible and scalable. Specifically, the object platform in the job management system includes terminal devices, such as at least one mobile terminal, which may be a mobile phone, a communication device, etc.

[0064] The sensor network platform can be used as the middle layer of the post management system to collect data information from the object platform and is responsible for transmitting the input instructions of the operator from the management platform to the object platform.

[0065] The Industrial Internet of Things (IIoT) plays a vital role in modern manufacturing, especially in the operation and management of production lines. The Industrial Internet of Things involves connecting various types of sensors, devices, machines and other objects through a network, enabling them to collect and exchange data. The goal of the Industrial Internet of Things is to improve the efficiency, productivity and reliability of industrial production processes through intelligence, automation and data analysis.

[0066] In some embodiments, the workshop job allocation system based on the industrial Internet of Things connects various mobile terminals through a management platform.

[0067] The specific process of the method can be as follows:

[0068] S110, obtaining work experience data of operators in the workshop, job type information data of the operators, skill level data of the operators, skill requirement data of each position in the workshop, and equipment parameters of each equipment in the workshop.

[0069] In some embodiments, the work experience data may be data reflecting the operator's years of work, positions he has worked in, and other information content. The job information data may be data reflecting the operator's job information. The skill level data may be data reflecting the operator's skill level, which may be represented in a numerical form. The skill requirement data may be a multidimensional vector, each dimension representing a different type of skill requirement. The equipment parameters may include data reflecting information such as equipment type and maintenance cycle, which may be represented in a numerical form.

[0070] S120. Through the workshop job allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop and the equipment parameters of each equipment in the workshop, a target allocation plan is obtained to optimize the production efficiency of the workshop.

[0071] In some embodiments, the shop floor job allocation model includes a first input layer, a first embedding layer, a first concatenation layer, a first hidden layer, and a first output layer.

[0072] The first embedding layer may be a network layer based on an embedding layer, where the embedding layer is mainly used to convert symbols or indexes (usually words or tags in a vocabulary) into dense vector representations.

[0073] The first concatenation layer may be a network layer based on a concatenation layer. The concatenation layer does not perform any complex mathematical operations, but simply combines two or more tensors or vectors together to form a new tensor or vector.

[0074] The first hidden layer may be a network layer based on a hidden layer. In a neural network, a hidden layer refers to a layer located between an input layer and an output layer. Hidden layers are key to a neural network's ability to perform complex calculations, because hidden layers allow the network to learn a nonlinear mapping from input data to output results.

[0075] In some embodiments, the target allocation scheme is obtained by using the workshop job allocation model according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, including the steps S121 to S126 as shown below:

[0076] S121. Through the first input layer, based on the work experience data of the operators in the workshop, the job information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, generate a work experience vector corresponding to the work experience data, a job information vector corresponding to the job information data, a skill level vector corresponding to the skill level data, a skill requirement vector corresponding to the skill requirement data, and an equipment parameter vector corresponding to the equipment parameters.

[0077] S122. Generate a converted job type information vector based on the job type information vector through the first embedding layer.

[0078] In some embodiments, the job type information vector may be a one-hot vector, and the converted job type information vector is obtained through the conversion processing of the first embedding layer. The converted job type information vector may be a dense vector converted by the embedding layer.

[0079] S123. Generate a first splicing vector through the first splicing layer according to the work experience vector, the converted job information vector, the skill level vector, the skill requirement vector and the equipment parameter vector.

[0080] In some implementations, the work experience vector, the converted job information vector, the skill level vector, the skill requirement vector, and the equipment parameter vector are spliced ​​into the first splicing vector along the channel dimension through the first splicing layer.

[0081] S124. Generate a first output vector through the first hidden layer according to the first concatenated vector.

[0082] In some embodiments, the first hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the activation function of the first sub-hidden layer, the activation function of the second sub-hidden layer and the activation function of the third sub-hidden layer are all non-linear activation functions.

[0083] The nonlinear activation function introduces nonlinear factors into the neural network model, enabling the model to approximate and learn complex function mappings and solve nonlinear problems.

[0084] In some embodiments, the nonlinear activation function may be a ReLU function.

[0085] In some embodiments, generating the first output vector according to the first concatenated vector through the first hidden layer includes the following steps S1241 to S1243:

[0086] S1241. Generate a first intermediate vector according to the first concatenated vector through a first sub-hidden layer.

[0087] S1242. Generate a second intermediate vector according to the first intermediate vector through a second sub-hidden layer.

[0088] S1243. Generate the first output vector according to the second intermediate vector through the third sub-hidden layer.

[0089] S125. Generate an output value according to the first output vector through the first output layer.

[0090] In some embodiments, the first output layer includes a dense layer, the number of neurons in the first output layer is the same as the number of positions in the workshop, each neuron in the first output layer corresponds one-to-one to each position in the workshop, the activation function corresponding to the first output layer is a Softmax function, and the output value is a position probability.

[0091] In some embodiments, the first output vector is input into the first output layer, and a multidimensional vector can be output. The numerical value corresponding to each dimension in the multidimensional vector can be the job probability. For example, 0.4, 0.3, 0.2 and 0.1 in (0.4, 0.3, 0.2, 0.1) are the job probabilities of 4 positions in the workshop corresponding to an operator respectively. The larger the job probability value, the more suitable the operator is for this position.

[0092] In some embodiments, generating an output value according to the first output vector through the first output layer includes the following specific implementation process:

[0093] The first output vector is input to the first output layer, and the first output vector is converted by the first output layer to obtain the position probability, wherein the position probability is used to characterize the suitability of the operator for each position.

[0094] S126. Generate the target allocation plan based on the output value.

[0095] In some embodiments, the generating of the target allocation scheme based on the output value includes the following steps S1261 to S1262:

[0096] S1261. Filter out the job probability with the largest value from the job probabilities, and confirm the job corresponding to the job probability with the largest value as the target job, wherein the target job corresponds one-to-one to the operator in the workshop.

[0097] Continuing with the above embodiment, the operator's job probability is (0.4, 0.3, 0.2, 0.1), among which 0.4 is the largest. If 0.4 corresponds to job a, then the operator's target job is job a.

[0098] S1262. Generate the target allocation plan based on the target position.

[0099] Continuing with the above-mentioned embodiment, the operators in the workshop are traversed to find the target position corresponding to each operator, and the target allocation plan is generated based on each target position.

[0100] In some embodiments, after obtaining the target allocation scheme through the workshop job allocation model according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, the method further includes steps T110 to T140 as shown below:

[0101] T110. Send the target allocation plan to the mobile terminal.

[0102] In some embodiments, a communication connection is established between the management platform and the object platform through a sensor network platform, and the target allocation plan is sent to the mobile terminal of each operator.

[0103] T120. In response to the allocation scheme modification instruction sent by the mobile terminal, update the target allocation scheme to obtain an updated allocation scheme.

[0104] In some embodiments, each operator can modify the target allocation plan on the software interface on the mobile terminal according to his or her personal wishes to obtain the updated allocation plan; and then send the updated allocation plan to the management platform.

[0105] T130. Using the allocation scheme evaluation model, according to the updated allocation scheme, obtain an evaluation score corresponding to the updated allocation scheme.

[0106] In some embodiments, the allocation scheme evaluation model includes a second input layer, a second embedding layer, a second concatenation layer, a second hidden layer and a second output layer. The number of neurons in the second output layer is 1, and the activation function corresponding to the second output layer is a linear activation function.

[0107] The linear activation function is usually used as the activation function of the last layer in linear regression problems, because the goal is to predict a continuous value rather than a classification probability; moreover, in tasks where the output layer outputs some predicted continuous values ​​(such as regression), the output layer can use a linear activation function to directly output the predicted value.

[0108] In some embodiments, obtaining the evaluation score corresponding to the updated allocation scheme according to the updated allocation scheme through the allocation scheme evaluation model includes the following steps T131 to T134:

[0109] T131. Generate an allocation plan input vector through the second input layer according to the updated allocation plan, wherein the allocation plan input vector includes at least one job number, and each job number corresponds one-to-one to a job in the workshop.

[0110] T132. Generate a job dense vector according to the job number through the second embedding layer.

[0111] In some embodiments, each job number is mapped to a fixed-length dense vector through the second embedding layer. This vector contains the potential features and relationships of the job represented by the job number. For example, different jobs have different skill requirements, etc., and this information can be encoded into the job dense vector.

[0112] T133. Generate a second splicing vector through the second splicing layer according to the allocation scheme input vector and the position dense vector.

[0113] In some embodiments, the second splicing vector contains comprehensive information of the allocation scheme and job characteristics, and can be used for subsequent model processing.

[0114] T134. Generate a second output vector through the second hidden layer according to the second concatenated vector.

[0115] In some embodiments, the second concatenated vector is input to the second hidden layer, and the second output vector is output.

[0116] T135. Obtain the evaluation score according to the second output vector through the second output layer.

[0117] In some embodiments, the evaluation score may be a continuous value, and the continuous value is used to represent the evaluation of the updated allocation scheme.

[0118] T140. Based on the comparison result between the evaluation score and the preset evaluation threshold, process the updated allocation plan. If the evaluation score is not less than the preset evaluation threshold, confirm the updated allocation plan as the final implemented workshop position allocation plan; if the evaluation score is less than the preset evaluation threshold, delete the updated allocation plan and confirm the target allocation plan as the final implemented workshop position allocation plan.

[0119] The embodiment of the present application first obtains the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop; then, through the workshop position allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, an accurate target allocation plan is obtained to optimize the production efficiency of the workshop.

[0120] In summary, this application provides a workshop job allocation method based on industrial Internet of Things. Through a neural network model, the job allocation plan of operators in the workshop is accurately obtained, so that the workshop can improve production efficiency while reducing the probability of production accidents.

[0121] In order to better implement the above method, the embodiment of the present application also provides a workshop job allocation device based on the industrial Internet of Things, which can be integrated in an electronic device, and the electronic device can be a terminal, a server, etc. Among them, the terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers.

[0122] For example, in this embodiment, the method of the embodiment of the present application will be described in detail by taking the specific integration of a workshop position allocation device based on the industrial Internet of Things in a terminal as an example.

[0123] For example, Figure 3 As shown, the workshop post allocation device 300 based on the industrial Internet of Things may include a first unit 301 and a second unit 302, which are applied to a post management system, wherein the post management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes a mobile terminal of an operator, and the management platform is communicatively connected with the object platform via the sensor network platform;

[0124] The device comprises:

[0125] The first unit 301 is used to obtain the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop;

[0126] The second unit 302 is used to obtain a target allocation plan through a workshop job allocation model according to the work experience data of the operators in the workshop, the job information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, so as to optimize the production efficiency of the workshop.

[0127] In specific implementation, the above units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units can refer to the previous method embodiments, which will not be repeated here.

[0128] It can be seen from the above that the embodiment of the present application can accurately obtain the job allocation plan of operators in the workshop through the neural network model, so that the workshop can improve production efficiency while reducing the probability of production accidents.

[0129] The embodiment of the present application also provides an electronic device, which can be a terminal, a server, etc. The terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc. The server can be a single server or a server cluster composed of multiple servers, etc.

[0130] In some embodiments, the product processing device can also be integrated into multiple electronic devices. For example, the product processing device can be integrated into multiple servers, and the workshop job allocation method based on industrial Internet of Things of this application is implemented by multiple servers.

[0131] In this embodiment, the electronic device of this embodiment is a terminal as an example for detailed description, for example, Figure 4 As shown, it shows a schematic diagram of the structure of the terminal 400 involved in the embodiment of the present application, specifically:

[0132] The terminal 400 may include one or more processing core processors 401, one or more medium storages 402, a power supply 403, an input module 404, and a communication module 405. Those skilled in the art will appreciate that Figure 4 The structure of the terminal 400 shown in the figure does not constitute a limitation on the terminal 400, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0133] The processor 401 is the control center of the terminal 400. It uses various interfaces and lines to connect various parts of the entire terminal 400. It executes various functions of the terminal 400 and processes data by running or executing software programs and / or modules stored in the memory 402, and calling data stored in the memory 402, so as to monitor the terminal 400 as a whole. In some embodiments, the processor 401 may include one or more processing cores; in some embodiments, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 401.

[0134] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal 400, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0135] The terminal 400 also includes a power supply 403 for supplying power to various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 403 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0136] The terminal 400 may further include an input module 404, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0137] The terminal 400 may also include a communication module 405. In some embodiments, the communication module 405 may include a wireless module. The terminal 400 may perform short-range wireless transmission through the wireless module of the communication module 405, thereby providing the user with wireless broadband Internet access. For example, the communication module 405 may be used to help the user send and receive emails, browse web pages, and access streaming media.

[0138] Although not shown, the terminal 400 may also include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 401 in the terminal 400 will load the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 will run the applications stored in the memory 402, thereby realizing various functions, as follows:

[0139] Obtaining the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop;

[0140] Through the workshop job allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop and the equipment parameters of each equipment in the workshop, a target allocation plan is obtained to optimize the production efficiency of the workshop.

[0141] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0142] It can be seen from the above that the embodiment of the present application can accurately obtain the job allocation plan of operators in the workshop through the neural network model, so that the workshop can improve production efficiency while reducing the probability of production accidents.

[0143] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a medium and loaded and executed by a processor.

[0144] To this end, an embodiment of the present application provides a medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any one of the workshop job allocation methods based on the industrial Internet of Things provided in the embodiment of the present application. For example, the instructions can execute the following steps:

[0145] Obtaining the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop;

[0146] Through the workshop job allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop and the equipment parameters of each equipment in the workshop, a target allocation plan is obtained to optimize the production efficiency of the workshop.

[0147] The medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0148] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes a computer instruction, the computer instruction is stored in a medium. A processor of a computer device reads the computer instruction from the medium, and the processor executes the computer instruction, so that the computer device executes the method provided in various optional implementations provided in the above embodiments.

[0149] Since the instructions stored in the medium can execute the steps in any one of the methods for workshop job allocation based on the industrial Internet of Things provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the methods for workshop job allocation based on the industrial Internet of Things provided in the embodiments of the present application can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0150] The above is a detailed introduction to a workshop job allocation method, device, terminal and medium based on industrial Internet of Things provided in the embodiments of the present application. 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 of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A workshop job allocation method based on industrial Internet of Things, characterized in that: Applied to a job management system, the job management system includes a management platform, a sensor network platform and an object platform, the object platform includes an operator's mobile terminal, and the management platform and the object platform are communicatively connected via the sensor network platform; The method comprises: Obtaining the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop; Through the workshop job allocation model, according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop and the equipment parameters of each equipment in the workshop, a target allocation plan is obtained to optimize the production efficiency of the workshop.

2. The method according to claim 1, characterized in that The workshop job allocation model includes a first input layer, a first embedding layer, a first concatenation layer, a first hidden layer and a first output layer; The workshop post allocation model is used to obtain a target allocation plan based on the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each post in the workshop, and the equipment parameters of each equipment in the workshop, including: Generate, through the first input layer, a work experience vector corresponding to the work experience data, a work type information vector corresponding to the work type information data, a skill level vector corresponding to the skill level data, a skill requirement vector corresponding to the skill requirement data, and an equipment parameter vector corresponding to the equipment parameter based on the work experience data of the operators in the workshop, the work type information data of the operators, the skill level data of the operators, the skill requirement data of the operators, the skill requirement data of the positions in the workshop, and the equipment parameters of the equipment in the workshop; Generate a converted job type information vector according to the job type information vector through the first embedding layer; Generate a first splicing vector through the first splicing layer according to the work experience vector, the converted job type information vector, the skill level vector, the skill requirement vector and the equipment parameter vector; Generate a first output vector according to the first concatenated vector through the first hidden layer; Generate an output value according to the first output vector through the first output layer; Based on the output values, the target allocation solution is generated.

3. The method according to claim 2, characterized in that The first hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the activation function of the first sub-hidden layer, the activation function of the second sub-hidden layer and the activation function of the third sub-hidden layer are all non-linear activation functions; The step of generating a first output vector according to the first concatenated vector through the first hidden layer includes: Generate a first intermediate vector according to the first concatenated vector through a first sub-hidden layer; Generate a second intermediate vector according to the first intermediate vector through a second sub-hidden layer; The first output vector is generated according to the second intermediate vector through the third sub-hidden layer.

4. The method according to claim 2, characterized in that The first output layer includes a dense layer, the number of neurons in the first output layer is the same as the number of positions in the workshop, each neuron in the first output layer corresponds to each position in the workshop one by one, the activation function corresponding to the first output layer is a Softmax function, and the output value is a position probability; The step of generating an output value according to the first output vector through the first output layer includes: The first output vector is input to the first output layer, and the first output vector is converted by the first output layer to obtain the position probability, wherein the position probability is used to characterize the suitability of the operator for each position.

5. The method according to claim 4, characterized in that The generating the target allocation scheme based on the output value comprises: Selecting the job probability with the largest value from the job probabilities, and confirming the job corresponding to the job probability with the largest value as the target job, wherein the target job corresponds to the operator in the workshop one by one; Based on the target position, the target allocation plan is generated.

6. The method according to claim 1, characterized in that After obtaining the target allocation scheme through the workshop job allocation model according to the work experience data of the operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, the method further includes: sending the target allocation scheme to the mobile terminal; In response to the allocation scheme modification instruction sent by the mobile terminal, updating the target allocation scheme to obtain an updated allocation scheme; Using the allocation scheme evaluation model, according to the updated allocation scheme, an evaluation score corresponding to the updated allocation scheme is obtained; Based on the comparison result between the evaluation score and the preset evaluation threshold, the updated allocation plan is processed. If the evaluation score is not less than the preset evaluation threshold, the updated allocation plan is confirmed as the final implemented workshop position allocation plan; if the evaluation score is less than the preset evaluation threshold, the updated allocation plan is deleted, and the target allocation plan is confirmed as the final implemented workshop position allocation plan.

7. The method according to claim 6, characterized in that The allocation scheme evaluation model includes a second input layer, a second embedding layer, a second concatenation layer, a second hidden layer and a second output layer, the number of neurons in the second output layer is 1, and the activation function corresponding to the second output layer is a linear activation function; The method of obtaining an evaluation score corresponding to the updated allocation scheme according to the updated allocation scheme through the allocation scheme evaluation model includes: Generate an allocation plan input vector according to the updated allocation plan through the second input layer, wherein the allocation plan input vector includes at least one job number, and each job number corresponds to a job in the workshop one by one; Generate a job dense vector according to the job number through the second embedding layer; Generate a second splicing vector according to the allocation scheme input vector and the job dense vector through the second splicing layer; Generate a second output vector according to the second concatenated vector through the second hidden layer; The evaluation score is obtained through the second output layer according to the second output vector.

8. A workshop job allocation device based on industrial Internet of Things, characterized in that: Applied to a job management system, the job management system includes a management platform, a sensor network platform and an object platform, the object platform includes an operator's mobile terminal, and the management platform and the object platform are communicatively connected via the sensor network platform; The device comprises: The first unit is used to obtain the work experience data of operators in the workshop, the type of work information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop; The second unit is used to obtain a target allocation plan through a workshop job allocation model according to the work experience data of the operators in the workshop, the job information data of the operators, the skill level data of the operators, the skill requirement data of each position in the workshop, and the equipment parameters of each equipment in the workshop, so as to optimize the production efficiency of the workshop.

9. A terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps in the method according to any one of claims 1 to 7.

10. A medium, characterized in that The medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the method according to any one of claims 1 to 7.

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