Workshop work order management method, device, terminal and medium based on industrial Internet of Things

Abstract

The present invention discloses a workshop work order management method, device, terminal and medium based on the industrial Internet of Things. The present invention relates to the field of workshop work order management technology and is applied to a workshop work order management system. The method comprises: obtaining personnel data corresponding to a target workshop, job data corresponding to a target workshop, equipment data corresponding to a target workshop and production line data corresponding to a target workshop; generating a target work order based on the personnel data, job data, equipment data and production line data through a work order prediction model, so as to maximize the production efficiency of the target workshop. The present invention aims to accurately obtain a workshop work order that can maximize the production efficiency of a workshop based on the industrial Internet of Things and the current information of the workshop through a work order prediction model, and effectively reduce the possibility of errors in manually formulating workshop production plans.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop work order management, and in particular to a workshop work order management method, device, terminal and medium based on industrial Internet of Things. Background Art

[0002] A shop work order is a management tool commonly used in manufacturing and other production environments. It is a document or electronic record used to indicate production activities. A shop work order usually contains specific production instructions to guide workers to complete specified tasks. Therefore, a shop work order is part of the production plan.

[0003] However, since the generation and management of workshop work orders at this stage rely on manual preparation by workshop managers, there are the following defects: due to the uncertainty of the production process, the relatively complex situation of production lines and production equipment, and due to the large number of personnel, the manually prepared workshop work orders cannot accurately reflect the current workshop production status, nor can they maximize the production efficiency of the workshop; at the same time, experienced managers will also bring higher labor costs to manufacturing companies. Summary of the invention

[0004] The main purpose of the present invention is to provide a workshop work order management method, device, terminal and medium based on the industrial Internet of Things, aiming to accurately obtain workshop work orders that can maximize the production efficiency of the workshop through a work order prediction model based on the industrial Internet of Things and the current information of the workshop, and effectively reduce the possibility of errors in manually formulating workshop production plans.

[0005] To achieve the above object, the present invention provides a workshop work order management method based on industrial Internet of Things, which is applied to a workshop work order management system, wherein the workshop work order management system is used to manage the generation and transmission of work orders of a target workshop, and the workshop work order management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes a production line, an operator's mobile terminal and production equipment, and the management platform is connected to the object platform through the sensor network platform. The method includes:

[0006] Acquire personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop;

[0007] Through the work order prediction model, a target work order is generated according to the personnel data, the job data, the equipment data and the production line data, so as to maximize the production efficiency of the target workshop.

[0008] Specifically, the work order prediction model includes a target optimization model, wherein the target optimization model takes maximizing the production efficiency of the target workshop as an optimization target;

[0009] The generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop includes:

[0010] Based on the personnel data, the job data, the equipment data, the production line data and the constraints of the target optimization model, the target optimization variable is obtained by solving the objective function of the target optimization model;

[0011] Based on the target optimization variable, the target work order is generated.

[0012] Specifically, the objective function of the target optimization model is expressed as:

[0013]

[0014] Wherein, Z represents the production efficiency of the target workshop, Indicates the time period t The operator i Assigned to positions j The optimization variables, Indicates the time period t Whether to enable production equipment during the period k The optimization variables, Indicates position j production efficiency, And production equipment k The performance parameters are related to Indicates operator i At work j Use production equipment k Working hours, Indicates production equipment k failure rate.

[0015] Specifically, the constraint conditions of the target optimization model are expressed as:

[0016]

[0017]

[0018]

[0019]

[0020] in, Indicates position j The number of people required, For operators i Available working time, F is the acceptable failure threshold of the target workshop.

[0021] Specifically, the work order prediction model includes an input layer, a hidden layer, and an output layer;

[0022] The generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop includes:

[0023] Generate an input feature vector through the input layer according to the personnel data, the position data, the equipment data and the production line data;

[0024] Generate a feature extraction vector according to the input feature vector through the hidden layer;

[0025] Generate an output vector through the output layer according to the feature extraction vector, wherein the output vector is used to represent the allocation of operators to each position in the target workshop, the usage status of each production equipment in the target workshop, the operation status of each production line in the target workshop, and the predicted production efficiency of the target workshop;

[0026] Based on the output vector, the target work order is generated.

[0027] Specifically, the hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the ratio of the number of neurons in the first sub-hidden layer, the number of neurons in the second sub-hidden layer and the number of neurons in the third sub-hidden layer is 4:2:1;

[0028] The step of generating a feature extraction vector through the hidden layer according to the input feature vector comprises:

[0029] Generate a first intermediate feature vector according to the input feature vector through the first sub-hidden layer;

[0030] Generate a second intermediate feature vector according to the first intermediate feature vector through the second sub-hidden layer;

[0031] A feature extraction vector is generated according to the second intermediate feature vector through the third sub-hidden layer.

[0032] Specifically, after generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop, the method further includes:

[0033] Sending the target work order to the mobile terminal;

[0034] In response to the work order modification request sent by the mobile terminal, the target work order is updated to obtain an updated target work order;

[0035] Based on the comparison result between the production efficiency corresponding to the updated target work order and the preset production efficiency threshold, the updated target work order is processed; if the production efficiency corresponding to the updated target work order is greater than or equal to the preset production efficiency threshold, the updated target work order is sent to the mobile terminal so that the operator can confirm the updated target work order; if the production efficiency corresponding to the updated target work order is less than the preset production efficiency threshold, the work order modification request is returned to the mobile terminal and the updated target work order is deleted.

[0036] To achieve the above object, the present invention also provides a workshop work order management device based on industrial Internet of Things, which is applied to a workshop work order management system, wherein the workshop work order management system is used to manage the generation and transmission of work orders of a target workshop, wherein the workshop work order management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes a production line, an operator's mobile terminal and production equipment, wherein the management platform is connected to the object platform through the sensor network platform, and the device includes:

[0037] The first unit is used to obtain personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop;

[0038] The second unit is used to generate a target work order according to the personnel data, the job data, the equipment data and the production line data through a work order prediction model, so as to maximize the production efficiency of the target workshop.

[0039] To achieve the above object, the present invention further provides a terminal, comprising a memory storing a plurality of instructions; the processor loads instructions from the memory to execute the steps in any one of the methods provided by the present invention.

[0040] To achieve the above objective, the present invention further provides a medium, wherein the medium stores a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute the steps in any one of the methods provided by the present invention.

[0041] The present invention provides a workshop work order management method, device, terminal and medium based on industrial Internet of Things, which can first obtain personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop and production line data corresponding to the target workshop; then, through the work order prediction model, generate the target work order according to the personnel data, the job data, the equipment data and the production line data, so as to maximize the production efficiency of the target workshop.

[0042] Based on the industrial Internet of Things and the current information of the workshop, the present invention can accurately obtain the workshop work order that can maximize the production efficiency of the workshop through the work order prediction model, and effectively reduce the possibility of errors in manually formulating workshop production plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a process flow provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a product processing system provided by an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of the structure of a product processing device provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0048] Due to the uncertainty of the production process, the relatively complex situation of the production line and production equipment, and the large number of personnel, the manually prepared workshop work orders cannot accurately reflect the current workshop production status, nor can they maximize the production efficiency of the workshop; at the same time, experienced managers will also bring higher labor costs to manufacturing companies.

[0049] Therefore, the embodiments of the present invention provide a workshop work order management method, device, terminal and medium based on industrial Internet of Things to solve practical technical problems.

[0050] In some embodiments, the device may be specifically integrated into an electronic device, which may be a terminal, a server or other device.

[0051] In some embodiments, the server may also be implemented in the form of a terminal.

[0052] 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.

[0053] 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 the present invention does not limit this.

[0054] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0055] An embodiment of the present invention provides a workshop work order management method based on the industrial Internet of Things. The method can accurately obtain workshop work orders that can maximize the production efficiency of the workshop through a work order prediction model based on the industrial Internet of Things and the current information of the workshop, thereby effectively reducing the possibility of errors in manually formulating workshop production plans.

[0056] like Figure 1 and Figure 2 As shown, it is applied to a workshop work order management system, which is used to manage the generation and transmission of work orders in a target workshop. The workshop work order management system includes a management platform, a sensor network platform and an object platform. The object platform includes a production line, an operator's mobile terminal and production equipment. The management platform and the object platform are communicatively connected via the sensor network platform.

[0057] In some embodiments, the workshop work order management system is a software solution used in manufacturing enterprises or maintenance service industries, etc., which can help enterprises more effectively manage and track various tasks in the production or service process.

[0058] The management platform is the core part of the workshop work order management system, providing enterprises with the ability to fully control and supervise production activities.

[0059] The object platform is the part that interacts with the physical objects in the actual production environment. The object platform may include production lines, mobile terminals used by operators, and production equipment.

[0060] The communication technology used by the sensor network platform may include Wi-Fi, Bluetooth, Zigbee, LoRaWAN, NB-IoT, etc. The technology used by the sensor network platform depends on factors such as coverage, power consumption requirements and data transmission rate.

[0061] The specific process of the method can be as follows:

[0062] S110. Obtain personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop.

[0063] In some embodiments, the personnel data may include the operating schedule of operators (shifts, vacations, skill levels, etc.) and the availability of operators at various levels; the equipment data may include the operating status of production equipment (normal, faulty, maintained, etc.), the failure history of production equipment (failure rate, repair time, etc.) and the performance parameters of production equipment (production capacity, efficiency, etc.); the production line data may include data corresponding to the layout of the production line, data corresponding to the process of the production line, time consumption of each production link and load data of the production line; the position data may include data corresponding to the position type, operator demand data for each position, etc.

[0064] S120. Generate a target work order based on the personnel data, the job data, the equipment data, and the production line data through a work order prediction model, so as to maximize the production efficiency of the target workshop.

[0065] In some embodiments, the work order prediction model includes a target optimization model, and the target optimization model takes maximizing the production efficiency of the target workshop as an optimization goal.

[0066] In some embodiments, data analysis may be performed on the acquired data first:

[0067] Operator availability analysis:

[0068] Calculate the number of available personnel for each team in a specific time period; consider skill matching to ensure that there are suitable personnel for each position;

[0069] Production equipment status analysis:

[0070] Evaluate the operating status of production equipment and identify production equipment with high failure rates; predict the failure probability of production equipment and develop maintenance plans;

[0071] Production line efficiency analysis:

[0072] Identify production bottlenecks and analyze the efficiency of each link; calculate the output capacity under different production line configurations;

[0073] Job Analysis:

[0074] Determine the range of the number of operators required for each position; judge the rationality of the allocation of operators to each position.

[0075] Based on the results of the above data analysis, a target optimization model is established. Linear programming, integer programming or other optimization algorithms can be used, and production efficiency indicators are defined, for example, the production efficiency can be the output per unit time; and constraints of the target optimization model are set, including personnel availability, equipment status, production line load, and job requirements.

[0076] Specifically, the work order prediction model generates a target work order according to the personnel data, the job data, the equipment data and the production line data, so as to maximize the production efficiency of the target workshop, including the steps A110 to A120 as shown below:

[0077] A110. Based on the personnel data, the job data, the equipment data, the production line data and the constraints of the target optimization model, the target optimization variables are obtained by solving the objective function of the target optimization model.

[0078] In some embodiments, the objective function of the target optimization model can be expressed as:

[0079]

[0080] Wherein, Z represents the production efficiency of the target workshop, Indicates the time period t The operator i Assigned to positions j The optimization variables, Indicates the time period t Whether to enable production equipment during the period k The optimization variables, Indicates position j production efficiency, And production equipment k The performance parameters are related to Indicates operator i At work j Use production equipment k Working hours, Indicates production equipment k failure rate.

[0081] Continuing with the above embodiment, the constraint conditions of the target optimization model can be expressed as:

[0082]

[0083]

[0084]

[0085]

[0086] in, Indicates position j The number of people required, For operators i Available working time, F is the acceptable failure threshold of the target workshop.

[0087] Specifically, the allocation of operators to each position must meet the personnel requirements of the position, which can be expressed as:

[0088]

[0089] The operation of each position must be performed on workable equipment, which can be expressed as:

[0090]

[0091] Ensuring that each operator can only work at one position during his / her available time can be expressed as:

[0092]

[0093] Considering the failure rate of production equipment, the opening and closing of production equipment is determined by the failure rate, which can be expressed as:

[0094]

[0095] In some embodiments, all decision variables should be non-negative and can only take the values ​​of 0 or 1, i.e.:

[0096]

[0097] Continuing with the above-mentioned embodiment, the target optimization variables may include data for allocating operators to various positions, data for adjusting the operation sequence of the production line, and data for selecting production equipment for production.

[0098] A120. Generate the target work order based on the target optimization variable.

[0099] Continuing with the above-mentioned embodiment, the target work order may include the personnel allocation of each position, the list of each production equipment required on the production line and its status, a detailed production line operation schedule and a production line maintenance time plan, etc.

[0100] In some embodiments, the work order prediction model includes an input layer, a hidden layer, and an output layer.

[0101] The work order prediction model is a neural network-based model and may include an input layer, a hidden layer, and an output layer.

[0102] The input layer is the first component of the work order prediction model. Its main function is to receive raw data and pass it to the subsequent hidden layer for processing. The design of the input layer directly affects how the neural network understands and processes the input data. The external data that the input layer can receive can be image pixel values, text feature vectors, audio signals, etc.

[0103] The hidden layer is located between the input layer and the output layer and is the core component of the work order prediction model. The hidden layer is responsible for extracting useful features from the input data and passing these features to the next layer through a series of nonlinear transformations, ultimately forming an understanding and abstract representation of the input data.

[0104] The output layer is the last processing layer of the work order prediction model, which is responsible for converting the features extracted by the hidden layer or the previous layer into the final prediction results.

[0105] The work order prediction model generates a target work order according to the personnel data, the job data, the equipment data and the production line data so as to maximize the production efficiency of the target workshop, including steps B110 to B140 as shown below:

[0106] B110. Generate an input feature vector through the input layer according to the personnel data, the job data, the equipment data and the production line data.

[0107] In some embodiments, the input layer is used to receive the acquired data, including the personnel data, the job data, the equipment data and the production line data, and form the above data into an input feature vector of fixed length.

[0108] For example, the input feature vector may be:

[0109] [1,0,1,3,0.05,0.1,1,0,500,5,2]

[0110] The input feature vector is a feature vector with a shape of (1, 10).

[0111] The features in the input feature vector include operator availability, operator skill 1, operator qualifications, number of production equipment, production equipment failure rate 1, production equipment failure rate 2, production line status, production line load, number of positions, and number of people required for each position.

[0112] B120. Generate a feature extraction vector through the hidden layer according to the input feature vector.

[0113] Continuing with the above embodiment, the hidden layer may include multiple sub-hidden layers, each of which may have a corresponding number of neurons. The hidden layer is used to extract implicit features in the input data, transform the data through a nonlinear activation function (such as ReLU), and improve the expressiveness of the work order prediction model. The activation function of the hidden layer may be ReLU, LeakyReLU, Sigmoid, or Tanh.

[0114] In some embodiments, the hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the ratio of the number of neurons in the first sub-hidden layer, the number of neurons in the second sub-hidden layer and the number of neurons in the third sub-hidden layer is 4:2:1.

[0115] In some embodiments, the step of generating a feature extraction vector through the hidden layer according to the input feature vector includes steps B121 to B123 as shown below:

[0116] B121. Generate a first intermediate feature vector according to the input feature vector through the first sub-hidden layer.

[0117] In some embodiments, the input feature vector is input to the first sub-hidden layer, and the first sub-hidden layer generates a first intermediate feature vector by weighted summation (i.e., calculating the sum of the product of the input feature vector and the weight of each neuron in the layer), adding a bias term, and then through an activation function (such as ReLU, Sigmoid, etc.); at the same time, assuming that the dimension of the first intermediate feature vector is 4n, each element corresponds to the output of a neuron.

[0118] Among them, weight matrix: Each hidden layer has a corresponding weight matrix, which is used to define the connection strength between the input feature vector and the hidden layer neurons;

[0119] Bias term: Each neuron has a bias term that adjusts the neuron's activation threshold.

[0120] B122. Generate a second intermediate feature vector according to the first intermediate feature vector through the second sub-hidden layer.

[0121] Continuing with the above embodiment, the first intermediate feature vector is input into the second sub-hidden layer, and a second intermediate feature vector is generated through weighted summation and activation function; at the same time, the dimension of the second intermediate feature vector is 2n, and each element corresponds to the output of a neuron.

[0122] B123. Generate a feature extraction vector according to the second intermediate feature vector through the third sub-hidden layer.

[0123] Continuing with the above embodiment, the second intermediate feature vector is input into the third sub-hidden layer, and a feature extraction vector is generated through weighted summation and activation function; at the same time, the dimension of the feature extraction vector is n, and each element corresponds to the output of a neuron.

[0124] B130. Generate an output vector through the output layer according to the feature extraction vector, wherein the output vector is used to characterize the allocation of operators for each position in the target workshop, the usage status of each production equipment in the target workshop, the operating status of each production line in the target workshop and the predicted production efficiency of the target workshop.

[0125] Continuing with the above-mentioned embodiment, the output vector may correspond to a plurality of prediction targets, and the neurons of the output layer may correspond one-to-one to the prediction targets, that is, the output layer may include a plurality of decision nodes, and each decision node may use a different activation function according to the nature of the output prediction target.

[0126] For example, there are 5 positions and 3 types of production equipment, and the output is a continuous value:

[0127] Then the output vector can be an output vector with a shape of (1, 8):

[0128] [ x Position 1, x Position 2, x Position 3, x Position 4, x Position 5, y Production equipment 1, y Production equipment 2, production volume]

[0129] For example:

[0130] [1,0,1,0,1,1,0,450]

[0131] The production volume refers to the production volume per unit time, that is, the production efficiency, and the production efficiency refers to the production volume per unit time reaching 450 units.

[0132] B140. Generate the target work order based on the output vector.

[0133] Continuing with the above embodiment, the dimension of the output vector is the sum of the number of features corresponding to the predicted target and the dimension of the predicted production efficiency of the target workshop.

[0134] Through the work order prediction model, the optimal operator allocation and production equipment configuration combination can be output based on the input operator, production equipment, position, and production line data, and the overall production efficiency can be predicted. This automated work order generation method can improve production efficiency in practical applications and reduce the cumbersome process of human decision-making.

[0135] In some embodiments, after generating a target work order based on the personnel data, the job data, the equipment data, and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop, the method further includes steps T110 to T130 as shown below:

[0136] T110. Send the target work order to the mobile terminal.

[0137] In some embodiments, the target work order can be distributed to the operator's mobile terminal through the sensor network platform so that the operator can read it or make a subsequent modification request.

[0138] T120. In response to the work order modification request sent by the mobile terminal, update the target work order to obtain an updated target work order.

[0139] In some embodiments, the operator can modify the specific items in the target work order according to the actual situation or his own authority. After the modification, a work order modification request is generated on the mobile terminal and sent to the management platform. The management platform responds to the work order modification request, updates the target work order, and obtains an updated target work order.

[0140] T130. Based on the comparison result between the production efficiency corresponding to the updated target work order and the preset production efficiency threshold, process the updated target work order. If the production efficiency corresponding to the updated target work order is greater than or equal to the preset production efficiency threshold, send the updated target work order to the mobile terminal so that the operator can confirm the updated target work order. If the production efficiency corresponding to the updated target work order is less than the preset production efficiency threshold, return the work order modification request to the mobile terminal and delete the updated target work order.

[0141] In some embodiments, if it is finally determined that the updated target work order is the final work order, the final work order is sent to the object platform through the sensor network platform, and based on the final work order, the production line and production equipment in the object platform are controlled; if the target work order is determined to be the final work order, the target work order is sent to the object platform through the sensor network platform, and based on the target work order, the production line and production equipment in the object platform are controlled.

[0142] The embodiment of the present invention can first obtain the personnel data corresponding to the target workshop, the job data corresponding to the target workshop, the equipment data corresponding to the target workshop, and the production line data corresponding to the target workshop; then, through the work order prediction model, generate the target work order according to the personnel data, the job data, the equipment data and the production line data, so as to maximize the production efficiency of the target workshop.

[0143] In summary, the present invention provides a workshop work order management method based on the industrial Internet of Things. Based on the industrial Internet of Things and the current information of the workshop, through the work order prediction model, it can accurately obtain the workshop work order that can maximize the production efficiency of the workshop. It can improve production efficiency in practical applications, reduce the tedious process of human decision-making, and effectively reduce the possibility of errors in manually formulating workshop production plans.

[0144] In order to better implement the above method, the embodiment of the present invention also provides a workshop work order management 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.

[0145] For example, in this embodiment, the method of the embodiment of the present invention is described in detail by taking the specific integration of a workshop work order management device based on the industrial Internet of Things in a terminal as an example.

[0146] For example, Figure 3As shown, the workshop work order management 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 workshop work order management system, wherein the workshop work order management system is used to manage the generation and transmission of work orders of a target workshop, wherein the workshop work order management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes a production line, an operator's mobile terminal and production equipment, wherein the management platform is connected to the object platform through the sensor network platform, and the device includes:

[0147] The first unit 301 is used to obtain personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop;

[0148] The second unit 302 is used to generate a target work order according to the personnel data, the job data, the equipment data and the production line data through a work order prediction model, so as to maximize the production efficiency of the target workshop.

[0149] From the above, it can be seen that the embodiment of the present invention can be based on the industrial Internet of Things and the current information of the workshop, through the work order prediction model, accurately obtain the workshop work order that can maximize the production efficiency of the workshop, which can improve production efficiency in practical applications, reduce the tedious process of human decision-making, and effectively reduce the possibility of errors in manually formulating workshop production plans.

[0150] The embodiment of the present invention further provides an electronic device, which may be a terminal, a server, or the like. The terminal may be a mobile phone, a tablet computer, a smart Bluetooth device, a notebook computer, a personal computer, or the like; the server may be a single server or a server cluster composed of multiple servers, or the like.

[0151] 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 work order management method based on industrial Internet of Things of the present invention is implemented by multiple servers.

[0152] 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 invention, specifically:

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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:

[0160] Acquire personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop;

[0161] Through the work order prediction model, a target work order is generated according to the personnel data, the job data, the equipment data and the production line data, so as to maximize the production efficiency of the target workshop.

[0162] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0163] From the above, it can be seen that the embodiment of the present invention can be based on the industrial Internet of Things and the current information of the workshop, through the work order prediction model, accurately obtain the workshop work order that can maximize the production efficiency of the workshop, which can improve production efficiency in practical applications, reduce the tedious process of human decision-making, and effectively reduce the possibility of errors in manually formulating workshop production plans.

[0164] 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.

[0165] To this end, an embodiment of the present invention 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 work order management methods based on the industrial Internet of Things provided by the embodiment of the present invention. For example, the instructions can execute the following steps:

[0166] Acquire personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop;

[0167] Through the work order prediction model, a target work order is generated according to the personnel data, the job data, the equipment data and the production line data, so as to maximize the production efficiency of the target workshop.

[0168] The medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0169] According to one aspect of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a medium. A processor of a computer device reads the computer instructions from the medium, and the processor executes the computer instructions, so that the computer device executes the method provided in the various optional implementations provided in the above embodiments.

[0170] Since the instructions stored in the medium can execute the steps in any one of the workshop work order management methods based on the industrial Internet of Things provided in the embodiments of the present invention, the beneficial effects that can be achieved by any one of the workshop work order management methods based on the industrial Internet of Things provided in the embodiments of the present invention can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0171] The above is a detailed introduction to a workshop work order management method, device, terminal and medium based on industrial Internet of Things provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, 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 invention.

Claims

1. A workshop work order management method based on industrial Internet of Things, characterized in that: Applied to a workshop work order management system, the workshop work order management system is used to manage the generation and transmission of work orders of a target workshop, the workshop work order management system includes a management platform, a sensor network platform and an object platform, the object platform includes a production line, an operator's mobile terminal and production equipment, the management platform and the object platform are communicatively connected through the sensor network platform, and the method includes: Acquire personnel data corresponding to the target workshop, job data corresponding to the target workshop, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the job data is used to characterize the job status of the target workshop; Generate a target work order based on the personnel data, the job data, the equipment data and the production line data through a work order prediction model, so as to maximize the production efficiency of the target workshop; The work order prediction model includes a target optimization model, wherein the target optimization model takes maximizing the production efficiency of the target workshop as an optimization target; The generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop includes: Based on the personnel data, the job data, the equipment data, the production line data and the constraints of the target optimization model, the target optimization variable is obtained by solving the objective function of the target optimization model; Based on the target optimization variable, generating the target work order; The objective function of the target optimization model is expressed as: Where Z represents the production efficiency of the target workshop, x ijk represents the optimization variable for assigning operator i to position j in time period t, y k Indicates whether to enable the optimization variable of production equipment k in time period t, p j represents the production efficiency of job j, p j Associated with the performance parameters of production equipment k, d ijk represents the working time of operator i using production equipment k at position j, f k represents the failure rate of production equipment k; The constraints of the target optimization model are expressed as: Among them, c j represents the number of people required for position j, W i is the available working time of operator i, and F is the acceptable failure threshold of the target workshop; The work order prediction model includes an input layer, a hidden layer and an output layer; The generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop includes: Generate an input feature vector through the input layer according to the personnel data, the position data, the equipment data and the production line data; Generate a feature extraction vector according to the input feature vector through the hidden layer; Generate an output vector through the output layer according to the feature extraction vector, wherein the output vector is used to represent the allocation of operators to each position in the target workshop, the use status of each production equipment in the target workshop, the operation status of each production line in the target workshop, and the predicted production efficiency of the target workshop; Based on the output vector, generating the target work order; The hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the ratio of the number of neurons in the first sub-hidden layer, the number of neurons in the second sub-hidden layer and the number of neurons in the third sub-hidden layer is 4:2:1; The step of generating a feature extraction vector through the hidden layer according to the input feature vector comprises: Generate a first intermediate feature vector according to the input feature vector through the first sub-hidden layer; Generate a second intermediate feature vector according to the first intermediate feature vector through the second sub-hidden layer; A feature extraction vector is generated according to the second intermediate feature vector through the third sub-hidden layer.

2. The method according to claim 1, characterized in that After generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop, the method further includes: Sending the target work order to the mobile terminal; In response to the work order modification request sent by the mobile terminal, the target work order is updated to obtain an updated target work order; Based on the comparison result between the production efficiency corresponding to the updated target work order and the preset production efficiency threshold, the updated target work order is processed; if the production efficiency corresponding to the updated target work order is greater than or equal to the preset production efficiency threshold, the updated target work order is sent to the mobile terminal so that the operator can confirm the updated target work order; if the production efficiency corresponding to the updated target work order is less than the preset production efficiency threshold, the work order modification request is returned to the mobile terminal and the updated target work order is deleted.

3. A workshop work order management device based on industrial Internet of Things, characterized in that: Applied to a workshop work order management system, the workshop work order management system is used to manage the generation and transmission of work orders of a target workshop, the workshop work order management system includes a management platform, a sensor network platform and an object platform, the object platform includes a production line, an operator's mobile terminal and production equipment, the management platform and the object platform are connected in communication through the sensor network platform, and the device includes: The first unit is used to obtain the personnel data corresponding to the target workshop and the corresponding Position data, equipment data corresponding to the target workshop, and production line data corresponding to the target workshop, wherein the personnel data is used to characterize the status of the operators of the target workshop, the equipment data is used to characterize the equipment status of the target workshop, the production line data is used to characterize the production line status of the target workshop, and the position data is used to characterize the position status of the target workshop; The second unit is used to generate a target work order according to the personnel data, the position data, the equipment data and the production line data through a work order prediction model, so as to maximize the production efficiency of the target workshop; The work order prediction model includes a target optimization model, wherein the target optimization model takes maximizing the production efficiency of the target workshop as an optimization target; The generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop includes: Based on the personnel data, the job data, the equipment data, the production line data and the constraints of the target optimization model, the target optimization variable is obtained by solving the objective function of the target optimization model; Based on the target optimization variable, generating the target work order; The objective function of the target optimization model is expressed as: Where Z represents the production efficiency of the target workshop, x ijk represents the optimization variable for assigning operator i to position j in time period t, y k Indicates whether to enable the optimization variable of production equipment k in time period t, p j represents the production efficiency of job j, p j Associated with the performance parameters of production equipment k, d ijk represents the working time of operator i using production equipment k at position j, f k represents the failure rate of production equipment k; The constraints of the target optimization model are expressed as: Among them, c j represents the number of people required for position j, W i is the available working time of operator i, and F is the acceptable failure threshold of the target workshop; The work order prediction model includes an input layer, a hidden layer and an output layer; The generating a target work order according to the personnel data, the position data, the equipment data and the production line data through the work order prediction model so as to maximize the production efficiency of the target workshop includes: Generate an input feature vector through the input layer according to the personnel data, the position data, the equipment data and the production line data; Generate a feature extraction vector according to the input feature vector through the hidden layer; Generate an output vector through the output layer according to the feature extraction vector, wherein the output vector is used to represent the allocation of operators to each position in the target workshop, the use status of each production equipment in the target workshop, the operation status of each production line in the target workshop, and the predicted production efficiency of the target workshop; Based on the output vector, generating the target work order; The hidden layer includes a first sub-hidden layer, a second sub-hidden layer and a third sub-hidden layer, and the ratio of the number of neurons in the first sub-hidden layer, the number of neurons in the second sub-hidden layer and the number of neurons in the third sub-hidden layer is 4:2:1; The step of generating a feature extraction vector through the hidden layer according to the input feature vector comprises: Generate a first intermediate feature vector according to the input feature vector through the first sub-hidden layer; Generate a second intermediate feature vector according to the first intermediate feature vector through the second sub-hidden layer; A feature extraction vector is generated according to the second intermediate feature vector through the third sub-hidden layer.

4. 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 2.

5. 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 2.

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