A control method and system for multiple industrial sensors

Abstract

An embodiment of the present specification provides a control method and system applied to multiple industrial sensors, the method comprising: obtaining a sensor spectrum corresponding to a sensor system at a first time; determining at least one preferred time point set of at least one sensor based on the sensor spectrum; determining at least one feasible sequence based on at least one preferred time point set of at least one sensor; determining system temperature information corresponding to the sensor system at a judgment time point through a temperature determination model based on at least one feasible sequence and the sensor spectrum; determining a feasible score of at least one feasible sequence based on the similarity between the system temperature information and the ideal temperature information; and determining an execution command based on the feasible score.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a control method and system applied to multiple industrial sensors. Background Art

[0002] In industrial production, multiple sensors are often used to monitor and control different parameters. However, the performance of sensors is affected by factors such as ambient temperature, and traditional control methods often fail to fully consider these factors, resulting in low control precision and data accuracy.

[0003] Therefore, it is hoped to provide a control method for improving sensors applied to multiple industries, which can more accurately control the operation of sensors and improve the accuracy of data collection. Summary of the invention

[0004] One or more embodiments of the present specification provide a control method applied to multiple industrial sensors, including: acquiring a sensor spectrum corresponding to a sensor system at a first time; determining at least one preferred time point set of at least one sensor based on the sensor spectrum; determining at least one feasible sequence based on the at least one preferred time point set of the at least one sensor; determining system temperature information corresponding to the sensor system at a judgment time point through a temperature determination model based on the at least one feasible sequence and the sensor spectrum; determining a feasible score of the at least one feasible sequence based on the similarity between the system temperature information and the ideal temperature information; and determining an execution command based on the feasible score.

[0005] One or more embodiments of the present specification provide a control system applied to multiple industrial sensors, including: a spectrum acquisition module, used to acquire a sensor spectrum corresponding to a sensor system at a first time; a preferred module, used to determine at least one preferred time point set of at least one sensor based on the sensor spectrum; a sequence determination module, used to determine at least one feasible sequence based on the at least one preferred time point set of the at least one sensor; a temperature determination module, used to determine system temperature information corresponding to the sensor system at a judgment time point through a temperature determination model based on the at least one feasible sequence and the sensor spectrum; a score determination module, used to determine a feasible score of the at least one feasible sequence based on a similarity between the system temperature information and the ideal temperature information; and a command determination module, used to determine an execution command based on the feasible score.

[0006] One or more embodiments of the present specification provide a control device applied to multiple industrial sensors, the device comprising 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 a control method applied to multiple industrial sensors as described in any one of the above.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are executed by a processor, a control method applied to multiple industrial sensors as described in any one of the above is implemented. 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 is a schematic diagram of an application scenario of a control method applied to multiple industrial sensors according to some embodiments of this specification;

[0010] Figure 2 is a module schematic diagram of a control system applied to multiple industrial sensors according to some embodiments of this specification;

[0011] Figure 3 is an exemplary flow chart of a control method and system applied to multiple industrial sensors according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart for determining a collection of preferred time points according to some embodiments of this specification. DETAILED DESCRIPTION

[0013] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[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. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0015] As shown in this specification and claims, 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 "comprise" and "include" 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] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 It is a schematic diagram of an application scenario of a control method applied to multiple industrial sensors according to some embodiments of this specification.

[0018] In some embodiments, the application scenario of the control method applied to multiple industrial sensors may include a network 110, a sensor system 120, a terminal 130, a processor 140, and a storage device 150. In some embodiments, the components in the application scenario 100 of the control method applied to multiple industrial sensors may be connected and / or communicated with each other via the network 110 (e.g., wireless connection, wired connection, or a combination thereof).

[0019] The network 110 can connect the components of the system and / or connect the system with external resources. The network 110 allows the components to communicate with each other and with other parts outside the system, and promotes the exchange of data and / or information. In some embodiments, the network 110 may include a local area network and other networks within the venue. For example, the network 110 may be connected to the sensor system 120 and send a command to upload data to the sensor system 120. In some embodiments, the network 110 may also include a cable network, an optical fiber network, a telecommunications network, the Internet, etc. or any combination thereof. The network connection between the various parts may be in one of the above-mentioned ways or in multiple ways. In some embodiments, the network may be a point-to-point, shared, centralized, or other topological structure or a combination of multiple topological structures. In some embodiments, the network 110 may include one or more network access points. For example, the network 110 may include a wired or wireless network access point.

[0020] The sensor system 120 may be a system composed of sensors used in industry to implement monitoring. The sensor system 120 includes various monitoring objects (e.g., production machines) and various sensors that can monitor various data. The sensor system 120 may be divided into multiple sub-areas according to the production process. For example, the plastic film production plan includes production processes such as granulation, extrusion, and calendering. Each production process is located at a different position and corresponds to a different sub-area. In each sub-area, at least one sensor and at least one sub-processor are included.

[0021] The terminal 130 may refer to one or more terminals or software with a network connection function used by any user. In some embodiments, the user may use the terminal 130 to remotely view the sensor system 120 and the working status of the monitoring object, and control multiple sensors in the sensor system 120. In some embodiments, the terminal 130 may be a mobile device, a tablet computer, a laptop computer, or any combination thereof, and other devices with input and / or output functions.

[0022] The processor 140 can process data and / or information related to the control method applied to multiple industrial sensors. In some embodiments, the processor 140 may include multiple sub-processors. In some embodiments, the processor 140 accesses information and / or data from the storage device 150 and / or the terminal 130. In some embodiments, the processor 140 can be a single server or a server group. The server group can be centralized or distributed (for example, the processor 140 can be a distributed system), can be dedicated, or can be provided by other devices or systems at the same time. In some embodiments, the processor 140 can be regional or remote. In some embodiments, the processor 140 can process data and / or information obtained from other devices or system components. In some embodiments, the processor 140 can execute program instructions based on these data, information, and / or processing results to perform one or more functions described in this application.

[0023] The storage device 150 may be used to store data and / or instructions. In some embodiments, the storage device 150 may include a mass storage device, a removable storage device, a read-write memory, a read-only memory, or the like, or any combination thereof. In some embodiments, the storage device 150 may be implemented on a cloud platform. The storage device 150 may be one or more devices, one or more application programming interfaces, one or more protocol-based information acquisition interfaces, or other means of acquiring information. The information provided by the storage device 150 may already exist when the information is extracted, may be temporarily generated when the information is extracted, or may be a combination of the above.

[0024] It should be noted that the application scenario 100 of the control method applied to multiple industrial sensors is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various modifications or variations can be made based on the description of this specification. For example, the application scenario 100 of the control method applied to multiple industrial sensors can be implemented on other devices to achieve similar or different functions. However, the changes and modifications will not deviate from the scope of this specification.

[0025] Figure 2 It is a module diagram of a control system applied to multiple industrial sensors according to some embodiments of this specification, including: a spectrum acquisition module 210, a optimization module 220, a sequence determination module 230, a temperature determination module 240, a score determination module 250, and a command determination module 260.

[0026] The spectrum acquisition module 210 is used to acquire a sensor spectrum corresponding to the sensor system at a first time.

[0027] The optimization module 220 is used to determine at least one set of preferred time points of at least one sensor based on the sensor spectrum.

[0028] The sequence determination module 230 is configured to determine at least one feasible sequence based on at least one preferred time point set of at least one sensor.

[0029] In some embodiments, the sequence determination module 230 includes: a first time acquisition module for acquiring a set of preset time points; a second time acquisition module for determining an updated time point set based on the preset time point set; and a third time acquisition module for determining a preferred time point set based on the updated time point set.

[0030] In some embodiments, the second time acquisition module includes: a restriction module, which is used to restrict the update direction of the preset time point collection based on the restriction condition to obtain the update time point collection.

[0031] In some embodiments, the third time acquisition module includes: a state determination module, which is used to calculate the corresponding state value based on each update vector in the update time point collection; a preferred selection module, which is used to select at least one update vector whose state value is greater than the state value threshold to determine at least one preferred vector, and the collection of at least one preferred vector is used as the preferred time point collection.

[0032] The temperature determination module 240 is used to determine the system temperature information corresponding to the sensor system at the determination time point through the temperature determination model based on at least one feasible sequence and the sensor spectrum.

[0033] The score determination module 250 is used to determine a feasibility score of at least one feasible sequence based on the similarity between the system temperature information and the ideal temperature information.

[0034] The command determination module 260 is used to determine an execution command based on the feasibility score.

[0035] Figure 3 300 is an exemplary flow chart of a control method and system for multiple industrial sensors according to some embodiments of this specification. In some embodiments, process 300 is executed by a processor, and process 300 includes the following steps:

[0036] In some embodiments, the sensor information includes an upload time point and an acquisition frequency.

[0037] Step 310, obtaining a sensor spectrum corresponding to the sensor system at a first time.

[0038] The sensor system corresponds to a sensor spectrum. The sensor spectrum includes node data and edge data. The node data includes nodes and node information of the sensor spectrum. In some embodiments, the sensor spectrum includes multiple sub-areas, and the sub-areas can be divided manually. Each sub-area includes a sub-processor node and a sensor node connected to the sub-processor. The sensor node attributes include the location of the sensor, the sensor type, the physical properties of the sensor, and the temperature information of the sensor. The sub-processor node attributes include the physical performance of the sub-processor, the processing power of the sub-processor, the temperature information of the sub-processor, and the location of the sub-processor. The physical performance of the sub-processor includes its inherent performance, such as architecture, number of cores, service life, hardware support, power consumption, etc. The sensor type can be temperature, humidity, current, voltage, etc.

[0039] Edge data includes the connection between nodes and edge attributes. The connection between nodes includes the connection between the processor and the sensor. Edge attributes include the physical properties of the connection between nodes, line temperature, etc. The physical properties of the connection can include transmission speed, material, etc.

[0040] The first time may refer to the current time or the historical time.

[0041] Step 320, determining at least one preferred time point set for at least one sensor based on the sensor spectrum.

[0042] The preferred time point collection includes at least one preferred vector, each preferred vector corresponds to a preset time point, and each preferred vector can indicate whether each sensor uploads data at the same preset time point.

[0043] For more information about the preferred time point collection, please refer to Figure 4 Related instructions.

[0044] Step 330: Determine at least one feasible sequence based on at least one preferred time point set of at least one sensor.

[0045] A feasible sequence may refer to a sequence of preset time points for uploading data for a sensor. The number of feasible sequences may be at least one. A feasible sequence may include a second time point, a third time point, etc.

[0046] The second time point may be the preferred time point for uploading data that is closest to the current time for the current sensor. For the same sensor, the number of second time points may be at least one. In some embodiments, the preset time point corresponding to the preferred vector in each preferred time point collection is used as the second time point. The preset relationship can be set manually. For example, the current time is 8:00, the preferred vector B is (0, 1, 0, 0), that is, it includes sensors 1, 2, 3, and 4, and the preferred vector B corresponds to 8:01, then it corresponds to sensor 2, and the second time point is 8:01.

[0047] In some embodiments, at least one third time point may be determined based on the preferred time point collection. The determination process of at least one third time point may refer to the determination process of the third time point, for example, step S14. The third time point may refer to the upload time point closest to the second time point of the current sensor. The second time point is later than the first time point.

[0048] In some embodiments, at least one feasible sequence is determined based on at least a set of preferred time points. For example, in the set of preferred time points, the preferred vector A1 corresponding to 8:01 is (0, 1, 1, 0), and the preferred vector A2 is (1, 1, 0, 1), the preferred vector B1 corresponding to 8:02 is (1, 1, 1, 0), and the preferred vector B2 is (0, 1, 0, 0), and the preferred vector C1 corresponding to 8:03 is (0, 1, 1, 0), and the preferred vector C2 is (1, 1, 0, 0), for sensor 1, based on the preferred vectors A1, B1 and C2, the second time can be 8:02, and the third time is 8:03, then a feasible sequence of sensor 1 is (8:02, 8:03), based on the preferred vectors A2, B2 and C2, the second time can be 8:01, and the third time is 8:03, then another feasible sequence of sensor 1 is (8:01, 8:03).

[0049] Step 340: Based on at least one feasible sequence and the sensor spectrum, determine the system temperature information corresponding to the sensor system at the determination time point through the temperature determination model.

[0050] The system temperature information may include a feasible sub-processor temperature of at least one sub-processor and a feasible line temperature of at least one upload line. The temperature determination model may include a processor temperature model and a line temperature model.

[0051] The judgment time point can be preset by the system. The judgment time point can be later than the current time point. The judgment time point can be earlier than the time corresponding to the last element of at least one feasible sequence. For example, if feasible sequence 1 is (8:02, 8:03) and feasible sequence 2 is (8:01, 8:02), the judgment time point can be located at 8:02.

[0052] The temperature determination model may be a machine learning model and may be determined through training. The temperature sequence corresponds to the time point of the feasible sequence. The temperature sequence of the subprocessor may be predicted by the processor temperature model based on the feasible sequence, the corresponding physical performance of the subprocessor, and the temperature at the first time. The temperature sequence of the subprocessor may be predicted by the circuit temperature model based on the physical performance of the feasible subprocessor, the feasible sequence, and the temperature at the first time. The predicted temperatures may be sorted according to the time corresponding to the elements in the feasible sequence to obtain the temperature sequence.

[0053] Step 350: Determine a feasibility score of at least one feasible sequence based on the similarity between the system temperature information and the ideal temperature information.

[0054] The similarity between the system temperature information and the ideal temperature information can be determined by a difference or ratio between the two. The similarity between the system temperature information and the ideal temperature information can include a sub-processor similarity between a feasible sub-processor temperature of at least one sub-processor and a corresponding ideal processor temperature, and a line similarity between a feasible line temperature of at least one upload line and a corresponding ideal line temperature.

[0055] The feasible score is the system status score of the sensor system based on the feasible sequence. The higher the feasible score, the better the system status. The feasible score can be represented by a number. Since the feasible sequence is a preset time sequence of uploaded data, when the data volume of the upload line exceeds or falls below the ideal data range of the upload line, the utilization rate of the upload line will be too high or insufficient, and the feasible score will be low, indicating that the system status is degraded.

[0056] For example, for sensor 1, feasible sequence 1 is (8:00, 8:01, 8:05), and feasible sequence 2 is (8:00, 8:02, 8:03). For the judgment time point 8:03, based on feasible sequence 1, the feasible route temperature is 25°C, and the ideal route temperature is 25°C. Based on feasible sequence 2, the feasible route temperature is 26°C, and the ideal route temperature is 25°C. Then, the route similarity corresponding to feasible sequence 1 is higher than the route similarity corresponding to feasible sequence 2.

[0057] In some embodiments, the feasible sequence includes a preset time point later than the judgment time point, because considering that the continued heating of the sub-processor / circuit has an impact on the feasible score, a more accurate feasible score of the judgment time point can be obtained through the feasible sequence. For example, based on the feasible sequence 3 being (8:00, 8:05) and the feasible sequence 4 being (8:00, 8:03), the judgment time point is set to 8:02. Since the preset upload time point later than 8:02 in the feasible sequence 3 also includes 8:05, and the preset upload time point later than 8:02 in the feasible sequence 4 also includes 8:03, since 8:03 is closer to the judgment time point than 8:05, the temperature predicted by the feasible sequence 4 at the judgment time point is higher than the temperature of the feasible sequence 3 at the judgment time point.

[0058] In some embodiments, a sub-feasible score of at least one sub-region may be determined based on the sub-processor similarity of at least one sub-processor and the at least one upload line similarity. The number of feasible scores may be multiple. The higher the similarity, the higher the feasible score. Exemplarily, the feasible score may be determined by formula (1).

[0059]

[0060] Among them, α1 represents the sub-feasible fraction of at least one sub-region, γ i represents the sub-processor similarity between the feasible sub-processor temperature and the corresponding ideal processor temperature of at least one sub-processor in the sub-region, or the line similarity between the feasible line temperature and the corresponding ideal line temperature of at least one upload line, β i It represents the weight of the similarity of a sub-processor in the sub-region or the similarity of the upload line in the sub-region, and n represents the total number of sub-processors and upload lines in the corresponding sub-region.

[0061] The number of upload lines can be the number of sensors.

[0062] The weight of a certain sub-processor similarity / upload line similarity in a sub-region can be determined based on its importance. The importance of the sub-processor similarity / upload line similarity can be determined based on the location of the upload line of the sub-processor / connected sensor, and the importance of the sub-processor / connected sensor upload line to the production program corresponding to the sub-region can be obtained through the location of the upload line of the sub-processor / connected sensor. The relevant content of the importance of the sub-processor / upload line similarity can refer to the importance of the sensor type. In some embodiments, the feasibility score can be determined based on formula (2).

[0063]

[0064] Among them, τ represents the feasibility score, α1 represents the sub-feasible fraction of at least one sub-region, m represents the number of sub-regions, δ i The weight of the sub-region can determine the importance of the sub-region based on its location.

[0065] Step 360, determining an execution command based on the feasibility score.

[0066] For each sensor, the industrial sensor is controlled to upload data based on the feasible sequence with the highest feasible score.

[0067] Figure 4 This is an exemplary flow chart of determining a preferred time point set according to some embodiments of this specification. In some embodiments, process 400 is executed by a processor, and process 400 includes the following steps:

[0068] Step S11, obtaining a collection of preset time points.

[0069] In some embodiments, a collection of preset time points is obtained. The collection of preset time points may be a preset collection of the time points at which the sensor will upload data next time closest to the current time point. The collection of preset time points may be set by the system itself or manually. The preset time points of each sensor in the collection of preset time points may be the same. For example, it may be uploaded every 1 minute. If the current time is 8:00, then the time when the sensor in the sensor spectrum uploads data next time is 8:01. The collection of time points may include at least two upload vectors, each of which corresponds to an upload preset time point, and each upload vector indicates whether all sensors upload data at the same time point. For example, upload vector A corresponds to 8:00 in the morning, and upload vector A is (0, 1, 1, 1), which respectively indicates that the four sensors do not upload data, upload data, upload data, and upload data at 8:00.

[0070] Step S12, determining an update time point collection based on the preset time point collection.

[0071] The updated time point collection may refer to a set of time points after the preset time point collection is updated. In some embodiments, the preset time point collection may be expanded and updated by crossing and iterating the uploaded vectors to obtain multiple update vectors, and the updated time point collection is obtained based on the update vectors. The updated time point collection includes the preset time point collection and the updated time point collection. The preset time point collection includes at least two update vectors. In the updated time point collection, the same uploaded preset time point corresponds to at least one update vector.

[0072] By crossing two or more upload vectors, an updated time point collection can be generated. Specifically, two upload vectors can be crossed according to a certain rule (such as randomly selecting intersection points, crossing in chronological order, etc.). After crossing, the elements in each upload vector may change. For example, the same upload preset time point may change from uploading data to not uploading data.

[0073] In some embodiments, it is necessary to add restrictions to restrict the update direction of the preset time point collection. In some embodiments, the restrictions may include that the importance exceeds the importance threshold. In some embodiments, the importance of the sensor type is determined based on the production plan corresponding to each preset time point uploaded. The importance may refer to the importance of the sensor type to the production plan. The importance may be represented by a number. The production plan may include multiple production programs. In some embodiments, based on the sensor location, type and production plan, the importance of the sensor type is determined by the importance model, and the satisfaction of the restriction is used as the update direction of the upload vector. The location of the sensor can determine the production program corresponding to the sensor. Different production programs have different importance of sensor types to the production program. For example, when the production volume increases, sensors of different locations and types may have different importance, and the importance of the sensor type may also change with the increase in production volume. The temperature sensor can help optimize the heating time and temperature control in the production process, detect temperature anomalies in time, and thus improve production efficiency. At this time, the temperature sensor of the key production program can be set to the highest importance. If the update direction is to change the temperature at a certain time point from upload to non-upload, it will not be updated based on the update direction. The importance model can be a machine learning model, such as a deep neural network. The importance model can be obtained by training with a first training sample and a first label. The first training sample includes the historical position, historical type and historical production plan of the sensor, and the first label is the importance of the historical sensor.

[0074] Step S13, determining a collection of preferred time points based on the collection of updated time points.

[0075] In some embodiments, a preferred time point collection is determined based on the state value and the state value threshold of each update vector. In some embodiments, based on each update vector in the update time point collection, the corresponding state value is calculated, and the update vector with a state value greater than the state value threshold is selected as the preferred vector, and the collection of preferred vectors is used as the preferred time point collection. The state value of the update vector may refer to the state value of the sensor system corresponding to each update vector.

[0076] In some embodiments, the status value of each update vector may be determined based on the satisfaction degree of the production plan corresponding to each update vector, the estimated line utilization rate, and the estimated conformity.

[0077] The degree of satisfaction of the production plan may refer to the degree to which the update vector satisfies the production plan at the preset time point of uploading. The degree of satisfaction of the production plan may be determined by the sum of the product of the importance of the sensor type and the corresponding importance coefficient. Exemplarily, the degree of satisfaction of the production plan may be determined by formula (3).

[0078]

[0079] Among them, ε is the satisfaction of the production plan, is the importance of sensor type, ω i is the importance coefficient of the sensor type, and t is the number of sensors.

[0080] The importance coefficient of the sensor type can be determined by the satisfaction model based on the update vector, sensor position, sensor type and importance, and production plan. The satisfaction of the production plan can be represented by a number. Since the sensor position can determine the corresponding process, the impact on the satisfaction of the production plan is determined based on the importance of the process to production.

[0081] The satisfaction model may be a machine learning model, for example, any two or a combination of NN, GNN, etc. The satisfaction model may be obtained by training with the second training sample and the second label.

[0082] The second training sample includes the historical update vector, the historical sensor position, the historical sensor type and importance, and the historical production plan, and the second label is the satisfaction of the historical production plan.

[0083] The estimated line usage rate may refer to the estimated usage rate of the sensor uploading data line at any preset upload time point. The usage rate may be determined by the estimated required bandwidth / line bandwidth. The estimated required bandwidth may refer to the bandwidth required for uploading the amount of data determined based on the update vector. In some embodiments, the estimated required bandwidth may be determined by the usage rate model. The estimated required bandwidth may refer to the bandwidth required for uploading the data line after the sensor uploads the value at a certain moment based on each upload vector in the time point collection. The usage rate model may be a machine learning model, for example, any two or a combination of NN, GNN, etc. The satisfaction model may be obtained by training. The input of the usage rate model may include the update time point collection, and the output may include the estimated required bandwidth, and then the estimated line usage rate is determined by the estimated required bandwidth / line bandwidth. For example, the line bandwidth of the transmission line is 1GB / s, and the estimated line usage rate is determined based on the usage rate model = (required bandwidth) / (line bandwidth) = 100MB / 1GB = 0.1, and 0.1 indicates that the utilization rate of the transmission line at this moment is 10%. When the estimated line usage rate is within the preset range, the higher the state value. For example, if the sensor estimates that the bandwidth required at a certain moment is 800MB / s, and the bandwidth of the transmission line is 1GB / s, then the estimated line usage is 0.8 (i.e., 80%). If we set the estimated line usage to be between 0.7 and 0.9 as a reasonable range, then when the estimated line usage is within this range, the state value will be at a higher level. Unless otherwise specified in this article, a line can refer to the line connecting the current sensor to the corresponding sub-processor.

[0084] The estimated degree of conformity may refer to the degree of conformity between the amount of data uploaded on the line at any preset upload time point and the processing capacity of the sub-processor in an ideal state. The estimated degree of conformity may refer to the difference between the amount of data uploaded on the line at any preset upload time point and the amount of data that the sub-processor can process in an ideal state. The smaller the absolute value of the estimated degree of conformity, the higher the state value. The amount of data that the processor can process in an ideal state can be determined by the physical properties of the processor.

[0085] In some embodiments, the amount of data uploaded on the line at any preset upload time point can be determined through a satisfaction model, and then the difference between the amount of data uploaded on the line and the amount of data that the processor can process under ideal conditions can be calculated, and the estimated degree of conformity can be determined based on the preset relationship between the above difference and satisfaction.

[0086] In some embodiments, based on each update vector in the update time point collection, the corresponding state value is calculated; at least one update vector whose state value is greater than a state value threshold is selected to determine at least one preferred vector, and the collection of at least one preferred vector is used as the preferred time point collection.

[0087] In some embodiments, the status value thresholds corresponding to different preset upload time points may be different. The status value thresholds may be manually set.

[0088] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0089] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0090] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0091] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0092] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0093] 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 digits. Although the numerical domains and parameters used to confirm the breadth of their 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.

[0094] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0095] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A control method applied to multiple industrial sensors, characterized in that: include: Obtaining a sensor spectrum corresponding to the sensor system at the first time; Determining at least one preferred set of time points for at least one sensor based on the sensor spectra; Determining at least one feasible sequence based on the at least one preferred set of time points of the at least one sensor; Based on the at least one feasible sequence and the sensor spectrum, determining the system temperature information of the sensor system corresponding to the judgment time point through a temperature determination model, wherein the feasible sequence refers to a sequence of preset time points for uploading data for a sensor; Determining a feasibility score of the at least one feasible sequence based on a similarity between the system temperature information and the ideal temperature information; Determining an execution command based on the feasible score includes: for each sensor, controlling the industrial sensor to upload data based on a feasible sequence with the highest feasible score.

2. The control method applied to multiple industrial sensors according to claim 1, characterized in that: The determining of at least one preferred time point set of at least one sensor based on the sensor spectrum comprises: Get a collection of preset time points; Determine an update time point collection based on the preset time point collection; A preferred time point collection is determined based on the update time point collection.

3. The control method applied to multiple industrial sensors according to claim 2, characterized in that: Determine the update time point collection, including: Based on the restriction condition, the update direction of the preset time point collection is restricted to obtain the update time point collection.

4. The control method applied to multiple industrial sensors according to claim 2, characterized in that: The determining of the preferred time point collection based on the update time point collection includes: Based on each update vector in the update time point collection, calculate a corresponding state value; At least one update vector whose state value is greater than a state value threshold is selected to determine at least one preferred vector, and a collection of the at least one preferred vector is used as a collection of preferred time points.

5. A control system applied to multiple industrial sensors, characterized in that: include: A spectrum acquisition module, used to acquire a sensor spectrum corresponding to the sensor system at a first time; A optimization module, configured to determine at least one set of preferred time points of at least one sensor based on the sensor spectrum; A sequence determination module, configured to determine at least one feasible sequence based on the at least one preferred time point set of the at least one sensor, wherein the feasible sequence refers to a sequence of preset time points for uploading data for a sensor; A temperature determination module, configured to determine system temperature information corresponding to the sensor system at a determination time point through a temperature determination model based on the at least one feasible sequence and the sensor spectrum; a score determination module, configured to determine a feasibility score of the at least one feasible sequence based on a similarity between the system temperature information and the ideal temperature information; The command determination module is used to determine the execution command based on the feasible score, including: for each sensor, controlling the industrial sensor to upload data based on the feasible sequence with the highest feasible score.

6. The control system applied to multiple industrial sensors according to claim 5, characterized in that: The sequence determination module comprises: A first time acquisition module, used to acquire a collection of preset time points; A second time acquisition module, used to determine an update time point set based on the preset time point set; The third time acquisition module is used to determine a set of preferred time points based on the set of updated time points.

7. The control system applied to multiple industrial sensors according to claim 6, characterized in that: The second time acquisition module includes: The restriction module is used to restrict the update direction of the preset time point collection based on the restriction condition to obtain the update time point collection.

8. The control system applied to multiple industrial sensors according to claim 6, characterized in that: The third time acquisition module includes: A state determination module, configured to calculate a corresponding state value based on each update vector in the update time point collection; The preferred selection module is used to select at least one update vector whose state value is greater than a state value threshold to determine at least one preferred vector, and the collection of the at least one preferred vector is used as a collection of preferred time points.

9. A control device applied to multiple industrial sensors, characterized in that: The apparatus comprises 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 the control method applied to multiple industrial sensors as claimed in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, and when the computer instructions are executed by a processor, the control method applied to multiple industrial sensors according to any one of claims 1 to 4 is implemented.

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