Industrial Internet of Things information push method, system and medium based on service cloud platform

Abstract

The present invention provides an industrial Internet of Things information push method, system and medium based on a service cloud platform, including: obtaining first sensor information through an industrial Internet of Things service platform corresponding to a first production line; determining a candidate push object and a push demand value of the candidate push object based on the first sensor information; determining a target push object based on the push demand value; determining reference information of the target push object based on the first sensor information, and sending the reference information to an industrial Internet of Things user platform corresponding to the target push object; in response to receiving a recommendation demand, generating recommended production parameters and / or recommended monitoring parameters based on the reference information, and sending them to the industrial Internet of Things user platform corresponding to the target push object to obtain confirmation information; generating adjustment instructions based on the confirmation information. Information intercommunication and effective use of data between production lines are achieved, and a reasonable reference is provided for the operation and information processing of each industrial Internet of Things.

Description

Technical Field

[0001] The present invention relates to the field of information interaction of the Internet of Things, and in particular to an industrial Internet of Things information push method, system and medium based on a service cloud platform. Background Art

[0002] At present, intelligent manufacturing technology based on the Internet of Things is developing rapidly and has become the core and foundation of the transformation of the manufacturing industry. However, when there are multiple production lines (or multiple processing workshops / factories), the industrial Internet of Things corresponding to each production line is independent of each other, resulting in the inability to interact with information from different production lines, low data utilization, and inability to fully realize the value of data.

[0003] Therefore, it is necessary to provide an industrial Internet of Things information push method and system based on a service cloud platform to achieve information exchange and effective use of data among various industrial Internet of Things, and provide a reasonable reference for the operation and information processing of various industrial Internet of Things. Summary of the invention

[0004] One or more embodiments of the present specification provide an industrial Internet of Things information push method based on a service cloud platform, the method being executed by a service cloud platform of an industrial Internet of Things information push system, the method comprising: obtaining first sensor information of the first production line through an industrial Internet of Things service platform corresponding to the first production line; determining candidate push objects based on the first sensor information, the candidate push objects including management users of at least one second production line; determining a push demand value of the candidate push objects based on the first sensor information and the candidate push objects; determining a target push object based on the push demand value, the target push object including management users of a target production line in the at least one second production line; determining reference information of the target push object based on the first sensor information, and The reference information is sent to the Industrial Internet of Things user platform corresponding to the target push object; in response to receiving the recommendation requirement, based on the reference information, recommended production parameters and / or recommended monitoring parameters are generated, the recommendation requirement is determined by the target push object based on the reference information, and the target push object issues the recommendation requirement through the Industrial Internet of Things user platform corresponding to the target push object; the recommended production parameters and / or the recommended monitoring parameters are sent to the Industrial Internet of Things user platform corresponding to the target push object, and confirmation information is obtained; and based on the confirmation information, an adjustment instruction is generated, and the adjustment instruction is sent to the Industrial Internet of Things service platform corresponding to the target production line, so as to adjust the production parameters of the production equipment and the monitoring parameters of the monitoring equipment on the target production line.

[0005] One or more embodiments of the present specification provide an industrial Internet of Things information push system, the system comprising a service cloud platform and multiple subsystems, the multiple subsystems comprising subsystems corresponding to multiple production lines, each of the multiple subsystems comprising an industrial Internet of Things user platform, an industrial Internet of Things service platform, an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform and an industrial Internet of Things perception control platform, the multiple production lines comprising a first production line and at least one second production line, the service cloud platform being configured to: obtain first sensor information of the first production line through the industrial Internet of Things service platform corresponding to the first production line; determine candidate push objects based on the first sensor information, the candidate push objects comprising management users of at least one second production line; determine push demand values ​​of the candidate push objects based on the first sensor information and the candidate push objects; determine target push objects based on the push demand values, the target The push object includes the management user of the target production line in the at least one second production line; based on the first sensor information, determining the reference information of the target push object, and sending the reference information to the industrial Internet of Things user platform corresponding to the target push object; in response to the target push object issuing a recommendation requirement through the industrial Internet of Things user platform corresponding to the target push object, generating recommended production parameters and / or recommended monitoring parameters based on the reference information, and the recommendation requirement is determined by the target push object based on the reference information; sending the recommended production parameters and / or the recommended monitoring parameters to the industrial Internet of Things user platform corresponding to the target push object, and obtaining confirmation information; and generating adjustment instructions based on the confirmation information, and sending the adjustment instructions to the industrial Internet of Things service platform corresponding to the target production line to adjust the production parameters of the production equipment and the monitoring parameters of the monitoring equipment on the target production line.

[0006] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method described in the above embodiments.

[0007] The beneficial effects of one or more embodiments of the present specification include but are not limited to: (1) Sharing data from different production lines enables information exchange between various industrial Internet of Things, effectively improves data utilization, and provides a reasonable reference for the operation and information processing of various industrial Internet of Things; (2) Before sending reference information, preview reference information is sent to candidate push objects, saving the time for management users to confirm, and based on feedback operations, obtains information reference that is closer to the actual needs of management users; (3) The preview reference information determined according to the reference requirements is more in line with the needs of candidate push objects. At the same time, the amount of data of the preview reference information after screening by the reference requirements is smaller, which can reduce the data processing volume of the service cloud platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0009] Figure 1 It is a schematic diagram of the platform structure of the industrial Internet of Things information push system according to some embodiments of this specification;

[0010] Figure 2 is an exemplary flow chart of an industrial Internet of Things information push method based on a service cloud platform according to some embodiments of this specification;

[0011] Figure 3 is an exemplary schematic diagram of determining a push requirement value according to some embodiments of this specification;

[0012] Figure 4 is an exemplary schematic diagram of a demand model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0013] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly introduces the drawings required for describing the embodiments. The drawings do not represent all implementation methods.

[0014] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. If other words can achieve the same purpose, the words can be replaced by other expressions.

[0015] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0016] When the operations are performed according to the step description in the embodiments of this specification, unless otherwise specified, the order of the steps is interchangeable, the steps may be omitted, and other steps may be included in the operation process.

[0017] Figure 1 It is a schematic diagram of the platform structure of the industrial Internet of Things information push system shown in some embodiments of this specification.

[0018] like Figure 1As shown, since there may be multiple production lines (or multiple processing workshops / factories) during production, the industrial Internet of Things information push system 100 may include a service cloud platform 110 and multiple subsystems. One production line corresponds to one subsystem. Each subsystem includes an industrial Internet of Things user platform, an industrial Internet of Things service platform, an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform, and an industrial Internet of Things perception control platform. The multiple production lines include a first production line and at least one second production line. The multiple subsystems are respectively connected to the service cloud platform 110 for communication, and information sharing in each subsystem is achieved through the service cloud platform.

[0019] The service cloud platform refers to a platform for realizing information interaction between multiple subsystems. In some embodiments, the service cloud platform is configured as a server in the cloud and can interact with the industrial Internet of Things service platforms in different subsystems.

[0020] In some embodiments, the service cloud platform is configured to determine candidate push objects and push demand values ​​of the candidate push objects based on the first sensor information; determine the target push object and reference information of the target push object based on the first sensor information and the push demand value; generate recommended production parameters and / or recommended monitoring parameters based on the reference information in response to the target push object issuing a recommendation demand; and generate adjustment instructions based on the confirmation information.

[0021] In some embodiments, the service cloud platform is also configured to determine the information reference degree based on the first sensor information and the second sensor information, and determine the push demand value based on the information reference degree; determine the information reference degree based on the association value and the effective information density of the first sensor information; determine the information reference degree based on the reference information increase; and correct the information reference degree based on the feedback operation.

[0022] In some embodiments, the service cloud platform is also configured to determine reference information based on reference demand and first sensor information; determine reference demand through a demand model based on target sensor information and first sensor information; train the demand model based on a preference training set to obtain a target demand model for a target push object; and use associated sensor information with a reference identifier as reference information.

[0023] The industrial Internet of Things user platform refers to a platform for interacting with management users. In some embodiments, the industrial Internet of Things user platform is configured as a terminal device and / or a terminal interface. The terminal device may include a mobile device, a tablet computer, and a laptop computer. The terminal interface may include a terminal device interface and a web page.

[0024] The Industrial Internet of Things service platform refers to a platform that provides information and services for managing users. In some embodiments, the Industrial Internet of Things service platform is configured on a local server. The Industrial Internet of Things service platform can interact with the Industrial Internet of Things user platform and the Industrial Internet of Things management platform.

[0025] The industrial Internet of Things management platform refers to a platform for managing information and / or data related to the subsystem. In some embodiments, the industrial Internet of Things management platform includes a processor and a storage device, etc. The storage device can store a database. The database refers to a database management system that supports high concurrent access. The processor may include one or more processing engines (e.g., a single-chip processing engine or a multi-chip processing engine). As an example only, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), etc., or any combination thereof.

[0026] The IIoT sensor network platform refers to a platform for integrated management of sensor information. In some embodiments, the IIoT sensor network platform is configured as a communication network or a gateway and a network interface, etc. The IIoT sensor network platform can interact with the IIoT management platform and the IIoT perception control platform.

[0027] The industrial Internet of Things perception control platform refers to a functional platform for generating perception information and executing control information. In some embodiments, the industrial Internet of Things perception control platform includes production equipment and monitoring equipment.

[0028] Production equipment may include storage equipment, transmission equipment, processing equipment and other equipment used for product production. Monitoring equipment refers to equipment used to monitor the production status and production environment of the production line. For example, temperature sensors, humidity sensors and quality detectors. Quality monitoring instruments may include quality detectors for detecting product quality and quality detectors for detecting middleware quality. In some embodiments, each process on the production line may be equipped with a set of production equipment and monitoring equipment.

[0029] For detailed description of the above, please refer to Figures 2 to 4 Related description.

[0030] In some embodiments of the present specification, based on the industrial Internet of Things information push system 100, an information operation closed loop can be formed between various functional platforms to coordinate and operate regularly, thereby realizing the intelligence of industrial Internet of Things information push.

[0031] Figure 2 200 is an exemplary flow chart of an industrial Internet of Things information push method based on a service cloud platform according to some embodiments of this specification. In some embodiments, process 200 is executed by a service cloud platform of an industrial Internet of Things information push system. Figure 2 As shown, process 200 includes the following steps:

[0032] In some embodiments, the service cloud platform obtains first sensor information through the industrial Internet of Things service platform corresponding to the first production line; based on the first sensor information, determines the candidate push object and the push demand value of the candidate push object; based on the push demand value, determines the target push object; based on the first sensor information, determines the reference information of the target push object, and sends the reference information to the industrial Internet of Things user platform corresponding to the target push object; in response to receiving the recommendation requirement, generates recommended production parameters and / or recommended monitoring parameters based on the reference information, and sends them to the industrial Internet of Things user platform corresponding to the target push object to obtain confirmation information; based on the confirmation information, generates adjustment instructions.

[0033] For relevant content about each platform of the Industrial Internet of Things information push system, please refer to Figure 1 The corresponding description.

[0034] Step 210: Acquire first sensor information of the first production line through the industrial Internet of Things service platform corresponding to the first production line.

[0035] The first production line refers to the production line that needs to share information with other production lines.

[0036] The first sensor information refers to sensor information related to the first production line. In some embodiments, the sensor information includes production data and monitoring data of the production line. The sensor information includes multiple groups of data, each group of data corresponds to a process in the production line.

[0037] In some embodiments, the sensor information corresponding to the process is obtained through the production equipment and monitoring equipment corresponding to the process. The sensor information may carry the identification of the source equipment of the sensor information. For the description of the production equipment and monitoring equipment, please refer to Figure 1 and its related description.

[0038] Production data refers to data related to production on a production line. In some embodiments, the production data includes production parameters and operation data of production equipment. For example, the production parameters include the real-time material balance in the storage device, the transmission speed of the transmission device, the tool feed rate of the cutting machine, etc. The operation data includes the working time of the production equipment, etc.

[0039] Monitoring data refers to data obtained by monitoring a production line. In some embodiments, the monitoring data includes monitoring results of a monitoring device. For example, the monitoring data includes processing temperature monitored by a temperature sensor, humidity of the processing environment monitored by a humidity sensor, and quality data of intermediates or products monitored by a quality detector. Quality data is represented by a qualified rate, a high-quality rate, or a defective rate. This specification does not limit how to determine whether an intermediate / product is qualified, a high-quality product, or a defective product.

[0040] In some embodiments, the service cloud platform obtains the first sensor information through the industrial Internet of Things service platform corresponding to the first production line. The industrial Internet of Things service platform obtains the first sensor information through the industrial Internet of Things management platform, the industrial Internet of Things sensor network platform and the industrial Internet of Things perception control platform.

[0041] Step 220: Determine a candidate push object based on the first sensor information.

[0042] The candidate push object refers to the information push object to be determined. In some embodiments, the candidate push object includes a management user of at least one second production line.

[0043] The second production line refers to other production lines other than the first production line. It is understandable that the industrial Internet of Things information push system can realize data interaction between different production lines, so the relationship between the first production line and the second production line is relative. When the service cloud platform wants to share the sensor information of a production line with other production lines, the production line is the first production line, and the other production lines are the second production lines.

[0044] Management users refer to the personnel who manage the production line. Management users can include the person in charge of the production line and the person in charge of each process on the production line.

[0045] In some embodiments, the service cloud platform determines candidate push objects in a variety of ways based on the first sensor information. For example, the service cloud platform constructs multiple first production vectors based on the production parameters of each production equipment in the first sensor information, and constructs multiple second production vectors based on the production parameters of each production equipment in the second sensor information of each second production line. For each first production vector among the multiple first production vectors, the service cloud platform matches the second production vector that meets the preset matching conditions among the multiple second production vectors based on the first production vector, determines the process where the production equipment corresponding to the second production vector that meets the preset matching conditions is located, and takes the person in charge of the process and the person in charge of the second production line where the process is located as candidate push objects. Among them, the first production vector refers to a feature vector constructed based on the production parameters of the production equipment in the first sensor information. The second production vector refers to a feature vector constructed based on the production parameters of the production equipment in the second sensor information. For an explanation of the second sensor information, please refer to the relevant description below.

[0046] The preset matching condition may include that the similarity is greater than a similarity threshold. The similarity threshold may be preset based on historical experience. The similarity of vectors is negatively correlated to the distance of vectors. The vector distance includes the Euclidean distance, etc.

[0047] Step 230: Determine a push requirement value of the candidate push object based on the first sensor information and the candidate push object.

[0048] The push demand value refers to a value used to characterize the degree of demand of the candidate push object for the first sensor information. The push demand value can be represented by a numerical value or the like. The larger the numerical value, the larger the push demand value, indicating that the candidate push object has a higher degree of demand for the first sensor information.

[0049] In some embodiments, the service cloud platform determines the push demand value based on the first sensor information and the candidate push object in a variety of ways. For example, the service cloud platform determines the reference attribute of the first sensor information based on the monitoring data in the first sensor information, and determines the recommended demand value of the candidate push object based on the reference attribute of the first sensor information and the similarity between the second production vector and the first production vector. The second production vector here is a second production vector that meets the preset matching condition.

[0050] The reference attribute refers to the type of reference that the sensing information can provide. The reference attribute can include "recommended reference", "recommended prevention" or "none".

[0051] The reference attribute of "recommended reference" indicates that the quality of the intermediate parts or products produced by the process is better, and can be used as a reference for the corresponding ones in the second production line to improve the product quality of other production lines.

[0052] The reference attribute "prevention recommended" means that the quality of the intermediates or products produced by the process is poor, and the second production line needs to be reminded to prevent similar situations.

[0053] The reference attribute is that the intermediate parts or products produced by the unexplained process are of medium quality and have no reference value.

[0054] In some embodiments, the service cloud platform determines the reference attribute of the first sensor information based on the quality data in the first sensor information corresponding to the process and the reference quality range. For example, in response to the quality data of the process being greater than the upper limit of the reference quality range, the service cloud platform determines the reference attribute of the first sensor information corresponding to the process as "recommended reference"; in response to the quality data of the process being less than the lower limit of the reference quality range, the service cloud platform determines the reference attribute of the first sensor information corresponding to the process as "recommended prevention"; in response to the quality data of the process being within the reference quality range, the service cloud platform determines the reference attribute of the first sensor information corresponding to the process as none. The reference quality range refers to the range of quality data that meets production requirements.

[0055] The reference quality range is synchronized with the way the quality data is expressed. If the quality data is expressed by the qualified rate, the reference quality range is the qualified rate range that meets the production requirements. If the quality data is expressed by the good product rate or the defective product rate, the reference quality range can be adjusted accordingly.

[0056] In some embodiments, the service cloud platform calculates the mean and variance of the historical quality data based on the historical quality data in the historical data, takes the sum of the mean and the variance as the upper limit of the reference quality range, and takes the difference between the mean and the variance as the lower limit of the reference quality range.

[0057] In some embodiments, in response to the reference attribute of the first sensor information being "recommended reference" or "recommended prevention", the higher the similarity between the second production vector and the first production vector, the higher the recommended demand value determined by the service cloud platform. If the reference attribute of the first sensor information is none, the service cloud platform sets the recommended demand value to 0.

[0058] In some embodiments, the service cloud platform determines the push demand value based on the information reference degree. For more information, see Figure 3 and its related description.

[0059] Step 240: Determine the target push object based on the push demand value.

[0060] The target push object refers to a determined information push object, and the target push object includes a management user of a target production line in at least one second production line.

[0061] The target production line refers to a second production line that is determined to receive information of the first production line.

[0062] In some embodiments, the service cloud platform determines the target push object based on the push demand value. For example, the service cloud platform uses the candidate push objects whose recommended demand value is greater than the recommended demand threshold as the target push object. For another example, if the management user does not set the recommended demand threshold, the service cloud platform determines all candidate push objects whose recommended demand value is greater than 0 as the target push object. Among them, the recommended demand threshold can be pre-set by the management user based on historical experience.

[0063] Step 250: Determine reference information of the target push object based on the first sensor information, and send the reference information to the industrial Internet of Things user platform corresponding to the target push object.

[0064] Reference information refers to information in the first sensing information that has reference value to the target push object.

[0065] In some embodiments, the service cloud platform determines the reference information of the target push object based on the first sensor information in a variety of ways. For example, the service cloud platform selects the first production vector that can match the second production vector that meets the preset matching condition in step 220, and uses the first sensor information corresponding to the first production vector as the reference information.

[0066] In some embodiments, the service cloud platform obtains reference requirements of the target push object; and determines reference information based on the reference requirements and the first sensor information.

[0067] Reference requirements refer to the type of information that the target push object wants to obtain. For example, if the management user needs reference information that can prevent defective products, the reference requirement can be "prevent defective products"; if the management user needs reference information that can improve production quality, the reference requirement can be "improve quality".

[0068] In some embodiments, the reference requirements may be determined by a management user based on problems encountered in production and uploaded to the service cloud platform.

[0069] In some embodiments, the service cloud platform determines the reference demand through the demand model based on the target sensor information and the first sensor information corresponding to the target push object within the preset time period. For more information, please refer to Figure 4 and its related description.

[0070] In some embodiments, the service cloud platform uses the first sensor information that can meet the reference requirement as reference information based on the reference requirement. For example, if the reference requirement is "preventing defective products", the service cloud platform uses the first sensor information corresponding to the process with lower quality data as reference information, and uses the reference requirement as an identifier of the reference information.

[0071] In some embodiments, the service cloud platform determines the associated sensor information in the first sensor information based on the reference requirement; sets a reference identifier for the associated sensor information based on the reference requirement; and uses the associated sensor information with the reference identifier as reference information.

[0072] Associated sensor information refers to sensor information related to the reference requirements of the target push object. In some embodiments, the service cloud platform can analyze the production quality, production efficiency, equipment loss and other aspects of the target process based on the first sensor information corresponding to the target process, and determine whether the first sensor information can meet the reference requirements. If the first sensor information can meet the reference requirements, the first sensor information is used as associated sensor information. The target process refers to the process in the first production line that meets the preset screening conditions. The preset screening conditions include the existence of a process chain that affects the same type of process in the target production line. For instructions on affecting the process chain, please refer to Figure 3 and its related description.

[0073] Exemplarily, the reference requirement may include "improving quality". If the quality data corresponding to the target process is greater than the upper limit of the reference quality range, the service cloud platform determines that the first sensor information corresponding to the target process can meet the reference requirement of "improving quality".

[0074] The reference identifier refers to a mark related to the associated sensor information. In some embodiments, the reference identifier includes an identifier indicating the target process corresponding to the associated sensor information, an identifier indicating the process chain affecting the target process, and an identifier indicating the reference requirement that the associated sensor information can meet.

[0075] In some embodiments, the service cloud platform uses the associated sensor information with the reference identifier as reference information.

[0076] In some embodiments of the present specification, by determining associated sensor information that is more associated with the reference requirement and setting a reference identifier for the associated sensor information, it is possible to quickly determine the reference type that the associated sensor information can provide to the user, thereby providing the reference information that the user needs more.

[0077] In some embodiments of the present specification, the first sensing information is subdivided based on the reference requirements to determine reference information that better meets the user's requirements.

[0078] Step 260 , in response to receiving the recommendation request, generating recommended production parameters and / or recommended monitoring parameters based on the reference information.

[0079] A recommendation demand refers to a request for information from a target push object. In some embodiments, after browsing the reference information and determining that the management user wants to obtain the information, the management user sends a recommendation demand by interacting with the industrial Internet of Things user platform. For example, after browsing the reference information, the management user determines that the management user wants to obtain the reference information, and can send a recommendation demand by clicking a relevant button on the page of the terminal device (such as a "get recommended information" button).

[0080] Recommended production parameters refer to the production parameters recommended by the service cloud platform.

[0081] Recommended monitoring parameters refer to the monitoring parameters recommended by the service cloud platform. Monitoring parameters include the monitoring frequency of the monitoring equipment, etc.

[0082] In some embodiments, the service cloud platform generates recommended production parameters and recommended monitoring parameters based on the reference information. For example, for reference information whose reference attribute is a recommended reference, the service cloud platform can use the production parameters in the reference information as a benchmark to adjust the production parameters of the production equipment in the target production line toward the benchmark direction to obtain recommended production parameters. The adjustment range can be preset based on historical experience.

[0083] For another example, for reference information whose reference attribute is recommended prevention, the service cloud platform may increase the monitoring parameters based on the similarity between the first production vector and the second production vector. The higher the similarity, the greater the increase in the monitoring parameters.

[0084] Step 270: Send the recommended production parameters and / or recommended monitoring parameters to the industrial Internet of Things user platform corresponding to the target push object, and obtain confirmation information.

[0085] Confirmation information refers to the information sent by the management user to confirm the recommended production parameters and recommended monitoring parameters.

[0086] Exemplarily, the confirmation information may include recommended production parameters and recommended monitoring parameters, indicating that the management user chooses to adopt the recommended production parameters and recommended monitoring parameters; the confirmation information may also include the recommended production parameters and recommended monitoring parameters modified by the management user; the confirmation information may also include not adjusting the current production parameters and monitoring parameters.

[0087] Step 280, based on the confirmation information, generates an adjustment instruction, and sends the adjustment instruction to the industrial Internet of Things service platform corresponding to the target production line to adjust the production parameters of the production equipment and the monitoring parameters of the monitoring equipment on the target production line.

[0088] Adjustment instructions are instructions used to instruct the industrial Internet of Things perception control platform to adjust relevant parameters.

[0089] In some embodiments, the service cloud platform generates adjustment instructions based on the recommended production parameters and recommended monitoring parameters in the confirmation information to adjust the production parameters of the production equipment and the monitoring parameters of the monitoring equipment on the target production line to be consistent with the recommended production parameters and recommended monitoring parameters.

[0090] In some embodiments of this specification, by sharing data from different production lines, information exchange is achieved between various industrial Internet of Things, and the utilization rate of data is effectively improved, providing a reasonable reference for the operation and information processing of various industrial Internet of Things.

[0091] It should be noted that the above description of process 200 is only for illustration and description, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process hand-eye calibration under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0092] Figure 3 is an exemplary flow chart for determining a push demand value according to some embodiments of this specification. In some embodiments, process 300 is executed by a service cloud platform of an industrial Internet of Things information push system. Figure 3 As shown, process 300 includes the following steps:

[0093] In some embodiments, the service cloud platform obtains second sensor information of at least one second production line through the industrial Internet of Things service platform corresponding to the at least one second production line; determines the information reference degree of the first sensor information for the candidate push object based on the first sensor information and the second sensor information; and determines the push demand value based on the information reference degree.

[0094] For details about the first sensor information, candidate push objects, and the second production line, please refer to Figure 2 and its related description.

[0095] Step 310: Acquire second sensor information of at least one second production line through the industrial Internet of Things service platform corresponding to at least one second production line.

[0096] The second sensor information refers to the sensor information related to the second production line. For a description of the sensor information, see Figure 2 and its related description.

[0097] In some embodiments, the service cloud platform obtains the second sensor information through the industrial Internet of Things service platform corresponding to the second production line. The industrial Internet of Things service platform obtains the second sensor information through the industrial Internet of Things management platform, the industrial Internet of Things sensor network platform and the industrial Internet of Things perception control platform.

[0098] Step 320: Determine the information reference degree of the first sensor information to the candidate push object based on the first sensor information and the second sensor information.

[0099] The information reference degree can be used to characterize the degree to which the first sensor information can be used as a reference for the candidate push object. In some embodiments, the information reference degree is represented by a numerical value or the like. The larger the numerical value, the higher the information reference degree.

[0100] In some embodiments, the service cloud platform determines the information reference degree in a variety of ways based on the first sensor information and the second sensor information. For example, the service cloud platform determines the first quality level and the second quality level based on the first sensor information and the second sensor information, and determines the information reference degree based on the first quality level and the second quality level.

[0101] The quality grade can indicate the overall quality of the products on the production line. In some embodiments, the quality grade is indicated by grades (such as grade 1, grade 2, and grade 3, etc.). The higher the grade, the higher the quality grade, indicating that the overall quality of the product is better.

[0102] In some embodiments, in response to the quality data of each process in the first sensing information being greater than the upper limit of the reference quality range, the service cloud platform determines the first quality level as level 3; in response to the quality data of each process in the first sensing information being less than the lower limit of the reference quality range, the service cloud platform determines the first quality level as level 1; in response to the quality data of more than a preset number of processes in the first sensing information being within the reference quality range, the service cloud platform determines the first quality level as level 2. The preset number can be pre-set based on historical experience. For more information about the reference quality range and quality data, please refer to Figure 2 and its related description.

[0103] The service cloud platform may determine the second quality level based on the second sensor information by the above-mentioned method of determining the first quality level.

[0104] In some embodiments, the service cloud platform determines the information reference degree based on the first quality level and the second quality level. For example, the service cloud platform determines the information reference degree based on the gap between the first quality level and the second quality level through a preset corresponding relationship. The preset corresponding relationship may include that the larger the gap between the first quality level and the second quality level, the higher the information reference degree.

[0105] It can be understood that the greater the gap between the first quality level and the second quality level, the better or worse the product quality of the first production line is, and the first sensor information is more useful as a reference for the candidate push object. The candidate push object can refer to the first sensor information to improve product quality or prevent situations similar to the first production line from occurring.

[0106] In some embodiments, the service cloud platform determines a correlation value between a first production line and at least one second production line based on the first sensor information and the second sensor information; and determines an information reference degree based on the correlation value and the effective information density of the first sensor information.

[0107] The correlation value is used to characterize the correlation degree between the first production line and the second production line.

[0108] In some embodiments, the service cloud platform determines the association value between the first production line and at least one second production line based on the first sensor information and the second sensor information. Exemplarily, the process of determining the association value based on the first sensor information and the second sensor information by the service cloud platform includes:

[0109] S1: For each process in the first production line, the service cloud platform calculates the similarity between it and the same type of process in the second production line. The same type of process refers to the process with the same function on the production line.

[0110] In some embodiments, the similarity between processes of the same type is determined based on the similarity between the first production vector and the second production vector corresponding to the production equipment of the process of the same type. For example, the similarity between processes of the same type is equivalent to the similarity between the first production vector and the second production vector corresponding to the production equipment of the process of the same type. For more information about the first production vector, the second production vector, and the similarity of the vectors, see Figure 2 Related description in .

[0111] S2: For processes with similarity greater than the similarity threshold, the process chain that affects the process in the second production line is queried through the preset association table. For an explanation of the similarity threshold, see Figure 2 and its related description.

[0112] In some embodiments, the preset association table is preset based on historical experience, and includes multiple processes and the influencing process chain where each process is located.

[0113] An influence process chain is a sequence of multiple processes that influence each other in a production line. For example, if process B influences process C, process C influences process D, and process D influences process E, then BCDE is an influence process chain.

[0114] S3: Calculate the sub-association value of the process based on similarity and the impact process chain.

[0115] In some embodiments, the sub-association value of a process is positively correlated with the similarity between processes of the same type and the length of the influencing process chain. The higher the similarity, the longer the influencing process chain, and the higher the sub-association value of the process. Exemplarily, the service cloud platform determines the product of the similarity and the length of the influencing process chain as the sub-association value of the process. The length of the influencing process chain can be represented by the number of processes in the influencing process chain.

[0116] S4: Determine the association value of the first production line to the second production line based on the sub-association values ​​of the plurality of processes.

[0117] In some embodiments, the service cloud platform determines the sum of the sub-association values ​​of multiple processes as the association value between the first production line and the second production line.

[0118] The effective information density refers to the proportion of information with reference value in the first sensor information. In some embodiments, the service cloud platform extracts the data volume of the first sensor information corresponding to the process with the sub-association value, and takes the ratio of the aforementioned data volume to the total data volume of the first sensor information as the effective information density. Among them, the service cloud platform extracts the data volume of the sensor information through the storage device of the industrial Internet of Things management platform. The data volume can be represented by the size of the space occupied by the data in the storage device.

[0119] In some embodiments, the service cloud platform determines the information reference degree based on the association value and the effective information density of the first sensor information. The information reference degree determined here is adjusted based on the original information reference degree. Exemplarily, the service cloud platform uses the product of the original information reference degree, the association value and the effective information density as the adjusted information reference degree.

[0120] In some embodiments, the effective information density is positively correlated to the first difference rate between the first sensor information and the second sensor information. The service cloud platform adjusts the effective information density based on the first difference rate. For example, the service cloud platform uses the product of the effective information density and the first difference rate as the adjusted effective information density.

[0121] The first difference rate is used to characterize the degree of difference between the first sensor information and the second sensor information. For example, the service cloud platform uses the difference between 1 and the similarity between the first sensor information and the second sensor information as the first difference rate. The service cloud platform can construct feature vectors based on the first sensor information and the second sensor information respectively, and represent the similarity between the two by the similarity of the feature vectors corresponding to the two. For more information about vector matching, please refer to Figure 2 and its related description.

[0122] In some embodiments of the present specification, the difference between the first sensor information and the second sensor information is taken into account when calculating the effective information density. The higher the difference, the higher the effective information density. An effective information density that is more in line with the actual situation can be determined, thereby making the calculation of the information reference degree more reasonable and comprehensive.

[0123] In some embodiments of the present specification, when calculating the information reference degree, the correlation degree between the processes on different production lines and the effective information density are taken into consideration, so that the information reference degree can be made more accurate.

[0124] In some embodiments, the service cloud platform determines the third sensor information based on historical reference information sent to the candidate push object within a preset time period; determines the reference information increase of the first sensor information based on the second difference rate between the first sensor information and the third sensor information; and determines the information reference degree based on the reference information increase.

[0125] The preset period refers to the time in the past when the second production line produced the product of the current model.

[0126] The third sensory information refers to the sensory information in the historical reference information. In some embodiments, the third sensory information includes multiple groups of data.

[0127] In some embodiments, the third sensor information is determined by the service cloud platform from historical reference information. The historical reference information refers to reference information that the service cloud platform has sent to the candidate push object within a preset period of time.

[0128] The reference information increase refers to the increase in the data volume of the first sensor information compared to the data volume of the third sensor information.

[0129] In some embodiments, the reference information increase is determined based on the data volume of the first sensor information, the second difference rate, and the data volume of the third sensor information. Exemplarily, the service cloud platform can calculate the product of the data volume of the first sensor information and the second difference rate, and use the ratio of the obtained product to the data volume of the third sensor information as the reference information increase. If the reference information increase is less than the preset increase threshold, the service cloud platform continues to use the original information reference. The preset increase threshold can be pre-set based on historical experience. For example, the preset increase threshold is 1.

[0130] For an explanation of the data volume, please refer to step 320 and its related description.

[0131] The second difference rate is used to characterize the difference between the first sensor information and the third sensor information. The second difference rate is determined in the same manner as the first difference rate, and the second difference rate can be determined by referring to the first difference rate.

[0132] In some embodiments, the service cloud platform determines the information reference degree based on the reference information increment. For example, the service cloud platform calculates the difference between the reference information increment and 1, and uses the product of the original information reference degree and the obtained difference as the new information reference degree.

[0133] In some embodiments of the present specification, when determining the information reference degree, the difference between the sensor information in the historical reference information and the first sensor information is taken into consideration, thereby reducing the duplication of the sensor information and making the information reference degree more accurate.

[0134] Step 330: Determine the push requirement value based on the information reference level.

[0135] In some embodiments, the service cloud platform determines the push demand value based on the information reference degree or the adjusted information reference degree. Exemplarily, the service cloud platform determines the push demand value by multiplying the similarity of the production parameters, the information reference degree and the preset coefficient, wherein the preset coefficient can be preset based on historical experience. The similarity of the production parameters can be represented by the similarity of the first production vector and the second production vector. For the similarity of the first production vector and the second production vector, see Figure 2 and its related description.

[0136] In some embodiments of the present specification, by determining the information reference degree, the degree to which the first sensor information can be referenced can be clarified, which is conducive to quickly determining a more reasonable push demand value.

[0137] In some embodiments, the service cloud platform sends preview reference information to the candidate push object based on the push demand value, and obtains the candidate push object's feedback operation on the preview reference information; and corrects the information reference degree based on the feedback operation.

[0138] The preview reference information refers to the generalized reference information used for preview. The service cloud platform can send the preview reference information to the candidate push object. The candidate push object can browse the preview reference information and then determine whether more detailed reference information is needed.

[0139] In some embodiments, the preview reference information includes a context preview and a production instruction preview. The context preview includes a process with sub-context values ​​and the sub-context values ​​of the process.

[0140] The production quality preview refers to the quality data of the process with the associated value. For example, the quality data of the process with the associated value includes the qualified rate, the excellent rate, the defective rate, etc. The service platform can extract the quality data of the process with the associated value from the first sensor information.

[0141] In some embodiments, the service cloud platform determines the preview reference information in a variety of ways. For example, the service cloud platform determines the amount of content in the preview reference information based on the push demand value. The greater the push demand value, the greater the amount of content in the preview reference information.

[0142] In some embodiments, the preview reference information may be determined based on the reference requirements of the candidate push object.

[0143] In some embodiments, the service cloud platform can filter and identify the preview reference information determined above based on the reference requirements to obtain the preview reference information. For example, if the reference requirement is "preventing defective products", the service cloud platform uses the preview reference information corresponding to the process with lower quality data in the preview reference information determined above as the new preview reference information, and uses the reference requirement as the identifier of the preview reference information. For an explanation of the reference requirements, see Figure 2 and its related description.

[0144] In some embodiments of the present specification, the preview reference information determined according to the reference requirements better meets the requirements of the candidate push objects. At the same time, the data volume of the preview reference information after being screened by the reference requirements is smaller, which can reduce the data processing volume of the service cloud platform.

[0145] Feedback operations refer to operations performed by the management user of the candidate push object after receiving the preview reference information. For example, feedback operations include requesting more content, temporarily ignoring, adjusting the push time, deleting, and other operations.

[0146] In some embodiments, the candidate push object performs a feedback operation by interacting with the industrial Internet of Things user platform.

[0147] In some embodiments, the service cloud platform corrects the information reference degree based on the feedback operation. For example, based on the feedback operation, the service cloud platform queries the correction coefficient corresponding to the feedback operation in the correction table, and multiplies the correction coefficient by the information reference degree to obtain the corrected information reference degree. The correction table can be pre-set based on historical experience, including multiple feedback operations and the correction coefficient corresponding to each feedback operation.

[0148] In some embodiments, the service cloud platform determines a first matching degree between the first sensing information and the candidate push object based on the feedback operation; and corrects the information reference degree based on the first matching degree.

[0149] The first matching degree may reflect the matching degree between the reference demand preference of the candidate push object and the first sensor information. In some embodiments, the first matching degree is represented by a numerical value, for example, the first matching degree may be set to a numerical value in the range of 0-1, and the larger the numerical value, the higher the first matching degree.

[0150] In some embodiments, the service cloud platform determines the first matching degree based on the feedback operation through a first matching relationship between the feedback operation and the first matching degree, wherein the first matching relationship may be preset based on historical experience.

[0151] Exemplarily, the first matching relationship includes: if the feedback operation is to request more content, the first matching degree is 1; if the feedback operation is to adjust the push time, the first matching degree is between 0.9 and 0.6; if the management user has no feedback operation, the first matching degree is 0.5; if the feedback operation is to delete the preview reference information, the first matching degree is 0. The above first matching degree values ​​are only examples and can be set as needed in actual applications.

[0152] In some embodiments, the service cloud platform may multiply the first matching degree by the original information reference degree to obtain a corrected information reference degree.

[0153] In some embodiments of the present specification, preview reference information is sent to candidate push objects before reference information is sent, which can save the management user's confirmation time, and based on the feedback operation, can obtain information reference that is closer to the actual needs of the management user.

[0154] It should be noted that the above description of process 300 is only for example and illustration, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 300 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0155] Figure 4 is an exemplary schematic diagram of a demand model according to some embodiments of the present specification.

[0156] In some embodiments, the service cloud platform determines the reference demand 440 through the demand model 430 based on the target sensor information 410 and the first sensor information 420 corresponding to the target push object within the preset time period. For a description of the preset time period, the target push object, the first sensor information and the reference demand, please refer to Figure 2 and Figure 3 and its related description.

[0157] The target sensor information refers to the second sensor information corresponding to the target production line. In some embodiments, the target sensor information is determined by the service cloud platform from historical reference information. For an explanation of historical reference information, see Figure 3 and its related description.

[0158] The demand model refers to a model that can be used to determine the reference demand. In some embodiments, the demand model is a machine learning model. For example, the demand model is any one or combination of a deep neural network (DNN) model or other custom model structures.

[0159] In some embodiments, the input of the demand model includes target sensor information and first sensor information, and the output of the demand model includes reference demand.

[0160] In some embodiments, the service cloud platform trains the demand model based on a large number of labeled training samples by gradient descent method, etc. Each training sample in the plurality of training samples may include sample target sensing information and sample first sensing information, and the label of each training sample may be an actual reference demand.

[0161] In some embodiments, training samples can be obtained based on historical data, and labels can be obtained based on manual annotation. For example, the service cloud platform can use the sensor information on any two production lines in the historical data as a training sample. The labels are obtained by multiple technicians who analyze and experiment on the training samples and then manually annotate them. Exemplarily, technicians analyze the sensor information of one production line in the training sample to provide a reference for another production line to improve product quality, and conduct actual experiments based on the training samples. If the experiment verifies that the above analysis is correct, the label corresponding to the training sample is "improve quality" in the reference requirement.

[0162] In some embodiments, the demand model can be trained by: inputting a plurality of training samples with training labels into the initial demand model, constructing a loss function through the training labels and the prediction results of the initial demand model, updating the initial demand model based on the iteration of the loss function, and completing the demand model training when the loss function of the initial demand model meets the preset conditions. The preset conditions may be that the loss function converges, the number of iterations reaches a set value, etc.

[0163] In some embodiments, the service cloud platform obtains a target feedback operation of the target push object on the preview reference information; based on the target feedback operation, determines the degree of match between the reference demand output by the demand model and the preference demand of the target push object; in response to the degree of match not satisfying the matching condition, based on the target feedback operation, determines the service preference characteristics of the target push object; based on the service preference characteristics, determines a preference training set; and trains the demand model based on the preference training set to obtain a target demand model of the target push object, where the target demand model is a machine learning model.

[0164] The target feedback operation refers to the feedback operation of the target push object when it receives the preview reference information. For more information about the feedback operation, see Figure 3The corresponding description.

[0165] The matching degree refers to the matching degree between the reference requirement and the preference requirement of the target push object. In some embodiments, the matching degree is represented by a numerical value or the like. For example, the matching degree can be set to a numerical value in the range of 0-1. The larger the numerical value, the higher the matching degree.

[0166] The preference requirement refers to a reference requirement that the target push object prefers. In some embodiments, the preference requirement can be input into the service cloud platform by the target push object.

[0167] In some embodiments, the service cloud platform determines the second matching degree based on the target feedback operation through the second matching relationship between the target feedback operation and the second matching degree. The second matching relationship can be preset based on historical experience. The second matching relationship is similar to the first matching relationship, and its implementation method can refer to the implementation method of the first matching relationship.

[0168] In some embodiments, the matching condition includes that the second matching degree is greater than a matching degree threshold. The matching degree threshold may include a minimum value of the second matching degree corresponding to the target feedback operation being "delayed push time". It is understandable that when the target feedback operation is "delayed push time", it means that the target push object believes that the preview reference information needs to be viewed, and the prediction of the demand model is more accurate.

[0169] The service preference feature may reflect the preference of the target push object for the reference information. In some embodiments, the service preference feature includes the proportion of different identifiers of the reference information.

[0170] In some embodiments, the service cloud platform determines the proportion of different identifiers of the reference information in the reference information with the target feedback operation of "click to get more content" and "delay push time" as the service preference feature. For the identifier of the reference information, see step 250 and its related description.

[0171] The preference training set refers to a collection of training samples for personalized training of the demand model. In some embodiments, the preference training set may include a first part and a second part. The service cloud platform may combine the historical first sensor information and the historical second sensor information in the target push record into the training samples of the first part. The target push record is a historical push record in which the target feedback operation is "click to get more content" and "delay push time". The historical push record includes multiple groups, each group of historical push records corresponds to a process of pushing historical reference information at a historical time, including data related to the process of pushing historical reference information.

[0172] The service cloud platform can determine the historical reference information with the same identifier as the highest proportion in the service preference feature based on the identifier of the historical reference information in the historical push record, and form the historical first sensor information and the historical second sensor information in the historical push record corresponding to this type of historical reference information into the training samples of the second part.

[0173] The target demand model refers to a model specifically used to determine the reference demand of the target push object. The target demand model includes the demand model after retraining.

[0174] In some embodiments, the service cloud platform retrains the demand model based on the preference training set to obtain a target demand model, wherein the learning rate of the first part of the retraining process is higher than the learning rate of the second part.

[0175] In some embodiments, the service cloud platform inputs the first part and the second part with the training label into the demand model to retrain the demand model. The training process of retraining the demand model is similar to the training process of the demand model above, and the training process of the demand model above can be referred to.

[0176] In some embodiments of the present specification, since the preferences of the target push object will change with the production situation, if the browsing rate and adoption rate of the reference information by the target push object is low, it means that the current demand model does not meet the preferences of the target push object. By performing preference training on the demand model, a demand model that better meets the actual needs of the target push object can be obtained, which is beneficial for making the reference information more in line with the needs of the target push object.

[0177] In some embodiments, the input of the demand model also includes the sub-association value size and the influencing process chain of the process with sub-association value in the target production line. The training sample also includes the sample sub-association value size and the sample influencing process chain of the sample process. For the relevant description of the influencing process chain and sub-association value, please refer to Figure 3 The corresponding description.

[0178] In some embodiments of this specification, the longer the influencing process chain and the higher the sub-correlation value, the more it indicates that similar situations exist between the target production line and the first production line. Inputting the influencing process chain and its sub-correlation value into the demand model can obtain more accurate reference demand.

[0179] In some embodiments of the present specification, the sensor information on different production lines is huge and complex. Using a demand model to determine the reference demand of a target push object can avoid inaccurate reference demand due to a lack of accuracy in the subjective judgment of the target push object, and thereby determine a more accurate and sufficient reference demand.

[0180] Some embodiments of the present specification also provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any of the methods in the above embodiments.

[0181] In some embodiments of this specification, an industrial Internet of Things information push device is also provided, which includes at least one processor and at least one memory. The at least one memory is used to store computer instructions. The at least one processor is used to execute at least part of the computer instructions to implement any of the methods in the above embodiments.

[0182] Furthermore, certain features, structures or characteristics in one or more embodiments of this specification may be appropriately combined.

[0183] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0184] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the materials cited in this specification and the contents described in this specification, the descriptions, definitions, and / or usage of terms in this specification shall prevail.

Claims

1. A method for pushing information of industrial Internet of Things based on a service cloud platform, characterized in that: The method is executed by a service cloud platform of an industrial Internet of Things information push system, and the method includes: Acquire first sensor information of the first production line through the industrial Internet of Things service platform corresponding to the first production line; Determine candidate push objects based on the first sensing information, wherein the candidate push objects include a management user of at least one second production line; constructing a plurality of first production vectors based on the production parameters of each production device in the first sensing information, and constructing a plurality of second production vectors based on the production parameters of each production device in the second sensing information of the at least one second production line; determining a reference attribute of the first sensor information based on monitoring data in the first sensor information; Determine a push requirement value of the candidate push object based on the reference attribute, the similarity between the plurality of second production vectors and the plurality of first production vectors, and the candidate push object, wherein the push requirement value is used to represent the degree of requirement of the candidate push object for the first sensor information; Based on the push demand value, determining a target push object, wherein the target push object includes a management user of a target production line in the at least one second production line; Based on the first sensing information, determining reference information of the target push object, and sending the reference information to the industrial Internet of Things user platform corresponding to the target push object; In response to receiving a recommendation requirement, generating a recommended production parameter and / or a recommended monitoring parameter based on the reference information, wherein the recommendation requirement is determined by the target push object based on the reference information, and the target push object issues the recommendation requirement through the industrial Internet of Things user platform corresponding to the target push object; Sending the recommended production parameters and / or the recommended monitoring parameters to the industrial Internet of Things user platform corresponding to the target push object, and obtaining confirmation information; and Based on the confirmation information, an adjustment instruction is generated, and the adjustment instruction is sent to the industrial Internet of Things service platform corresponding to the target production line to adjust the production parameters of the production equipment and the monitoring parameters of the monitoring equipment on the target production line.

2. The method according to claim 1, characterized in that The determining, based on the first sensor information and the candidate push object, a push requirement value of the candidate push object comprises: Acquire second sensor information of the at least one second production line through the industrial Internet of Things service platform corresponding to the at least one second production line; Determining, based on the first sensor information and the second sensor information, an information reference degree of the first sensor information to the candidate push object; and Based on the information reference degree, the push requirement value is determined.

3. The method according to claim 2, characterized in that The method further comprises: Based on the push requirement value, sending preview reference information to the candidate push object, and obtaining a feedback operation of the candidate push object on the preview reference information; and Based on the feedback operation, the information reference degree is corrected.

4. The method according to claim 1, characterized in that The determining, based on the first sensing information, reference information of the target push object, and sending the reference information to the industrial Internet of Things user platform corresponding to the target push object includes: Obtaining the reference requirements of the target push object; and The reference information is determined based on the reference requirement and the first sensing information.

5. The method according to claim 4, characterized in that The determining the reference information based on the reference requirement and the first sensing information includes: Based on the reference requirement, determining associated sensor information in the first sensor information; Based on the reference requirement, setting a reference identifier for the associated sensor information; and The associated sensing information with the reference identifier is used as the reference information.

6. An industrial Internet of Things information push system based on a service cloud platform, characterized in that: The system includes a service cloud platform and multiple subsystems, wherein the multiple subsystems include subsystems corresponding to multiple production lines, each of the multiple subsystems includes an industrial Internet of Things user platform, an industrial Internet of Things service platform, an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform, and an industrial Internet of Things perception control platform, and the multiple production lines include a first production line and at least one second production line, and the service cloud platform is configured as follows: Acquire first sensor information of the first production line through the industrial Internet of Things service platform corresponding to the first production line; Determine candidate push objects based on the first sensing information, wherein the candidate push objects include a management user of at least one second production line; constructing a plurality of first production vectors based on the production parameters of each production device in the first sensing information, and constructing a plurality of second production vectors based on the production parameters of each production device in the second sensing information of the at least one second production line; determining a reference attribute of the first sensor information based on monitoring data in the first sensor information; Determine a push requirement value of the candidate push object based on the reference attribute, the similarity between the plurality of second production vectors and the plurality of first production vectors, and the candidate push object, wherein the push requirement value is used to represent the degree of requirement of the candidate push object for the first sensor information; Based on the push demand value, determining a target push object, wherein the target push object includes a management user of a target production line in the at least one second production line; Based on the first sensing information, determining reference information of the target push object, and sending the reference information to the industrial Internet of Things user platform corresponding to the target push object; In response to the target push object issuing a recommendation requirement through the industrial Internet of Things user platform corresponding to the target push object, generating recommended production parameters and / or recommended monitoring parameters based on the reference information, wherein the recommendation requirement is determined by the target push object based on the reference information; Send the recommended production parameters and / or the recommended monitoring parameters to the industrial Internet of Things user platform corresponding to the target push object, and obtain confirmation information; as well as Based on the confirmation information, an adjustment instruction is generated, and the adjustment instruction is sent to the industrial Internet of Things service platform corresponding to the target production line to adjust the production parameters of the production equipment and the monitoring parameters of the monitoring equipment on the target production line.

7. The system according to claim 6, characterized in that The service cloud platform is further configured as follows: Acquire second sensor information of the at least one second production line through the industrial Internet of Things service platform corresponding to the at least one second production line; Determining, based on the first sensor information and the second sensor information, an information reference degree of the first sensor information for the candidate push object; as well as Based on the information reference degree, the push requirement value is determined.

8. The system according to claim 7, characterized in that The service cloud platform is further configured as follows: Based on the push requirement value, sending preview reference information to the candidate push object, and obtaining a feedback operation of the candidate push object on the preview reference information; and Based on the feedback operation, the information reference degree is corrected.

9. The system according to claim 6, characterized in that The service cloud platform is further configured as follows: Obtaining the reference requirements of the target push object; and The reference information is determined based on the reference requirement and the first sensing information.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method as claimed in claim 1.

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