Sensor data acquisition method, device, equipment, storage medium and product

Abstract

The present application discloses a sensor data acquisition method, device, equipment, storage medium and product, which relates to the field of sensor technology, and discloses a sensor data acquisition method, including: in response to an acquisition instruction, sequentially performing fluctuation analysis on the sensor data in each sensor partition, determining the data fluctuation of each sensor partition, wherein the sensor partition is obtained after the array sensor is partitioned; based on the data fluctuation, updating the frequency of collecting sensor data in each sensor partition, determining the update acquisition frequency of each sensor partition; and collecting sensor data in the corresponding sensor partition according to each update acquisition frequency. That is, according to the data fluctuation, the frequency required for collecting sensor data in each sensor partition is updated in a targeted manner, and the update acquisition frequency of each sensor partition is determined, so as to improve the collection efficiency of sensor data in certain sensor partitions in a targeted manner, so as to improve the real-time performance of collecting sensor data.

Description

Technical Field

[0001] The present application relates to the field of sensor technology, and in particular to sensor data acquisition methods, devices, equipment, storage media and products. Background Art

[0002] An array sensor refers to a whole with certain functions and effects composed of one or more sensors arranged in a certain order. It can realize data collection and extraction in the area, providing convenience for subsequent big data analysis.

[0003] Collecting data through sensors requires converting the physical signal of the target object into a photoelectric signal. However, the conversion of physical signals into photoelectric signals takes a certain amount of time, and multiple sensor elements working simultaneously will generate a large amount of data, which requires powerful data processing capabilities and efficient algorithms to process and store this data. Data bandwidth limitations may affect the real-time performance of data processing, thus reducing the real-time performance of collected data.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a sensor data collection method, aiming to solve the technical problem of reduced real-time performance of collected data.

[0006] To achieve the above objectives, the present application proposes a sensor data collection method, the method comprising:

[0007] In response to the acquisition instruction, sequentially performing fluctuation analysis on the sensor data in each sensor partition to determine the data fluctuation of each sensor partition, wherein the sensor partition is obtained after partitioning the array sensor;

[0008] Based on the data fluctuation, updating the frequency of collecting sensor data of each sensor partition, and determining the update collection frequency of each sensor partition;

[0009] According to each of the update collection frequencies, the sensor data in the corresponding sensor partition is collected.

[0010] In one embodiment, the step of updating the frequency of collecting sensor data of each sensor partition based on the data fluctuation and determining the update collection frequency of each sensor partition comprises:

[0011] Selecting the partitions to be adjusted whose data fluctuations satisfy the preset adjustment fluctuations from the sensor partitions;

[0012] Adjusting in real time the frequency of collecting sensor data from each of the partitions to be adjusted, and determining the latest collection frequency of each of the partitions to be adjusted;

[0013] Based on the latest acquisition frequency, the recorded acquisition frequency of each sensor partition is updated to determine the updated acquisition frequency of each sensor partition.

[0014] In one embodiment, if there are more than two partitions to be adjusted, the step of adjusting the frequency of collecting sensor data from each partition to be adjusted in real time and determining the latest collection frequency of each partition to be adjusted includes:

[0015] Comparing the fluctuations of the partitions to be adjusted;

[0016] If there is no partition with the same fluctuation among the partitions to be adjusted, determining whether there is a priority adjustment partition whose fluctuation satisfies the priority adjustment fluctuation among the partitions to be adjusted;

[0017] If so, the acquisition frequency of the priority-adjusted partition is adjusted first, and the importance of the remaining partitions in the partition to be adjusted is determined;

[0018] Based on the importance, the acquisition frequencies of the remaining partitions are adjusted in turn to determine the latest acquisition frequency of each partition to be adjusted.

[0019] In one embodiment, after comparing the fluctuations of the partitions to be adjusted, the step further includes:

[0020] If there are partitions with the same fluctuation among the partitions to be adjusted, marking the same-frequency partitions with the same fluctuation from the partitions to be adjusted;

[0021] The acquisition frequencies of the same-frequency partitions are adjusted simultaneously to determine the latest acquisition frequencies of the partitions to be adjusted.

[0022] In one embodiment, after the step of collecting sensor data in the corresponding sensor partition according to each of the update collection frequencies, the step further includes:

[0023] identifying key data marked as key partitions from the sensor data;

[0024] Based on the key data, adjusting the computing power arrangement for analyzing the sensor data;

[0025] The sensor data is analyzed based on the computing power arrangement to determine physical information of the target object collected by the sensor in each sensor partition.

[0026] In one embodiment, the step of collecting sensor data in the corresponding sensor partition according to each of the update collection frequencies includes:

[0027] Based on the updated acquisition frequency, adjusting a target bandwidth required for collecting sensor data in a corresponding sensor partition;

[0028] The target bandwidth is used to collect sensor data in the corresponding sensor partition.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a sensor data acquisition device, the sensor data acquisition device comprising:

[0030] An analysis module, for performing fluctuation analysis on sensor data in each sensor partition in turn in response to the acquisition instruction, and determining data fluctuation of each sensor partition, wherein the sensor partition is obtained after partitioning the array sensor;

[0031] An adjustment module, configured to update the frequency of collecting sensor data of each sensor partition based on the data fluctuation, and determine the update collection frequency of each sensor partition;

[0032] The collection module is used to collect sensor data in the corresponding sensor partition according to each of the update collection frequencies.

[0033] In addition, to achieve the above objectives, the present application also proposes a sensor data acquisition device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the sensor data acquisition method described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the sensor data acquisition method described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the sensor data acquisition method described above are implemented.

[0036] One or more technical solutions proposed in this application have at least the following technical effects:

[0037] The array sensors are partitioned, and then the sensor data in each sensor partition is subjected to fluctuation analysis to determine the data fluctuation in each sensor partition, so as to facilitate the targeted update of the frequency required for collecting sensor data in each sensor partition according to the data fluctuation, determine the update collection frequency of each sensor partition, so as to improve the collection efficiency of sensor data in certain sensor partitions in a targeted manner, and then collect the sensor data in the corresponding sensor partition according to the update collection frequency of the sensor partition, so as to adjust the amount of sensor data that needs to be collected in each partition in a targeted manner, avoid the concentration of computing power, reduce the delay in collecting sensor data, and improve the real-time performance of collecting sensor data. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the sensor data collection method of the present application;

[0041] Figure 2 A schematic diagram of an example of partitioning of an array sensor provided for the sensor data acquisition method of the present application;

[0042] Figure 3 A schematic diagram of the data fluctuations provided by the sensor data collection method of the present application;

[0043] Figure 4 A brief flow chart of the sensor data collection method provided for this application;

[0044] Figure 5 A flow chart of the second embodiment of the sensor data acquisition method of the present application;

[0045] Figure 6 A flow chart of the sensor data acquisition method according to the present invention;

[0046] Figure 7 This is a schematic diagram of the module structure of the sensor data acquisition device according to an embodiment of the present application;

[0047] Figure 8 Schematic diagram of the device structure of the hardware operating environment involved in the sensor data acquisition method in the embodiment of the present application.

[0048] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0050] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The main solution of the embodiment of the present application is: the upper computer responds to the acquisition instruction, performs fluctuation analysis on the sensor data in each sensor partition in turn, and determines the data fluctuation of each sensor partition, where the sensor partition is obtained after the array sensor is partitioned; based on the data fluctuation, updates the frequency of collecting sensor data in each sensor partition, and determines the update acquisition frequency of each sensor partition; and according to each update acquisition frequency, collects the sensor data in the corresponding sensor partition.

[0052] In this embodiment, for the convenience of description, the following description is made with the host computer as the execution subject.

[0053] Since collecting data through sensors requires converting the physical signals of the target object into photoelectric signals, however, the conversion of physical signals into photoelectric signals takes a certain amount of time, and multiple sensor elements working simultaneously will generate a large amount of data, which requires powerful data processing capabilities and efficient algorithms to process and store these data. Data bandwidth limitations may affect the real-time performance of data processing, thus reducing the real-time performance of collected data.

[0054] The present application provides a solution to partition the array sensors and then perform fluctuation analysis on the sensor data in each sensor partition to determine the data fluctuation in each sensor partition, so as to facilitate targeted updating of the frequency required for collecting sensor data in each sensor partition according to the data fluctuation, determine the update collection frequency of each sensor partition, so as to improve the collection efficiency of sensor data in certain sensor partitions in a targeted manner, and then collect the sensor data in the corresponding sensor partition according to the update collection frequency of the sensor partition, so as to adjust the amount of sensor data that needs to be collected in each partition in a targeted manner, avoid concentration of computing power, reduce the delay in collecting sensor data, and improve the real-time performance of collecting sensor data.

[0055] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a host computer, etc. The following takes the host computer as an example to illustrate this embodiment and the following embodiments.

[0056] Based on this, the present application embodiment provides a sensor data collection method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the sensor data collection method of the present application.

[0057] In this embodiment, the sensor data collection method includes steps S10 to S30:

[0058] Step S10, in response to the acquisition instruction, sequentially performing fluctuation analysis on the sensor data in each sensor partition to determine the data fluctuation of each sensor partition, wherein the sensor partition is obtained by partitioning the array sensor;

[0059] It should be noted that sensor partitioning is the division of array sensors into several independent areas. The sensors in each area are responsible for collecting data within a specific range. When collecting sensor data, the data is collected in units of partitions to avoid concentrated bandwidth requirements. Data fluctuation analysis is the process of evaluating the volatility of data collected by sensors. By analyzing the fluctuation of data, the stability and change trend of sensor data can be understood. Data fluctuation is the fluctuation that characterizes the stability and change trend of sensor data after fluctuation analysis of sensor data.

[0060] It can be understood that by partitioning the array sensors, the management efficiency of the array sensors can be improved to avoid too many sensors working at the same time and exceeding the computing power and bandwidth allocated to the array sensors. The array sensors are partitioned according to the user's concern for each area. Therefore, the array sensors are managed by independent partitions, and resources can be allocated accordingly according to the different levels of concern for each sensor partition, so as to reasonably allocate existing resources.

[0061] It is understandable that independently partitioning the array sensors can also optimize the data acquisition process, especially in large-scale sensor networks, and can improve the efficiency and pertinence of data processing.

[0062] It can be understood that by analyzing the data fluctuations of the sensor data in each sensor partition, it is convenient to adjust the collection resources of the corresponding sensor partition in a targeted manner, thereby avoiding the failure to collect sensor data with large data fluctuations in a timely manner, thereby improving the real-time performance of sensor data collection.

[0063] In the specific implementation, the array sensor is first partitioned to form several independent sensor partitions. The sensor in each partition is responsible for collecting data within a specific range. For example, Figure 2 , the dotted box is a single sensor, 144 array sensors are simulated, and partitioned according to 4x4, and each partition is marked, that is, the sensor partition in the upper left corner can be marked as partition 1, the sensor partition in the middle of the top layer is marked as partition 2, the sensor partition in the upper right corner can be marked as partition 3, the sensor partition on the leftmost side of the middle layer is marked as partition 4, and so on, and each sensor partition is marked. In response to the acquisition instruction, the sensor data in each sensor partition is analyzed by statistical methods or machine learning algorithms in turn to determine the data fluctuation of each sensor partition, so as to facilitate the subsequent targeted adjustment of the acquisition frequency of each sensor partition according to the data fluctuation.

[0064] Step S20, based on the data fluctuation, updating the frequency of collecting sensor data of each sensor partition, and determining the update collection frequency of each sensor partition;

[0065] It should be noted that the frequency of collecting sensor data is the number of times the sensor data is collected per unit time. The updated collection frequency may be the collection frequency adjusted for each sensor partition.

[0066] It is understandable that if the data fluctuations of a certain sensor partition are large, a higher acquisition frequency may be required; conversely, if the data fluctuations are small, a lower acquisition frequency is sufficient. That is, by dynamically adjusting the acquisition frequency, the bandwidth and computing power required for acquisition can be reasonably classified, thereby reducing resource waste.

[0067] It is understandable that by reasonably allocating bandwidth and computing power, it is also possible to avoid delays in collecting sensor data with large data fluctuations due to insufficient bandwidth and computing power. That is, the frequency required for collecting sensor data in each sensor partition can be adjusted in a targeted manner according to data fluctuations, thereby improving the real-time performance of collecting sensor data in each sensor partition.

[0068] In the specific implementation, refer to Figure 3 ,Dynamically analyze the data of sensors in each sensor partition. If the sensor data in the upper left corner partition has a large fluctuation, increase the sampling frequency of the upper left corner partition and focus on analyzing the data of sensors in the upper left corner area.

[0069] Step S30: collecting sensor data in corresponding sensor partitions according to the update collection frequencies.

[0070] It can be understood that according to each of the update sampling frequencies, the sensor data in the corresponding sensor partition is collected, that is, the sensor of each partition will work at its specific sampling frequency, thereby ensuring that data collection is both efficient and accurate.

[0071] It can be understood that through the sensor dynamic collection method, it is possible to effectively collect data at a higher frequency for sensors in key signal partitions. By increasing the sampling frequency of key areas, not only the delay in collecting data is reduced, but also the real-time performance is improved.

[0072] In the specific implementation, refer to Figure 4 After receiving the collection instruction, the sensors in each sensor partition respectively collect the data of the target object (sensor data), and each sensor then transmits the data to the host computer (application device). The host computer analyzes the fluctuation of the sensor data in each sensor partition in turn, determines the data fluctuation of each sensor data, and then updates the collection frequency of the sensor partition that needs to be adjusted according to the data fluctuation to determine the updated collection frequency of each sensor partition after adjustment. Finally, at the set time of collecting sensor data, according to the updated collection frequency, the sensor data in the corresponding sensor partition is collected, and the next cycle of collecting sensor data is entered. This cycle continues until the user shuts down the host computer or device, and then the cycle ends.

[0073] The present embodiment provides a sensor data acquisition method, which partitions the array sensor, and then performs fluctuation analysis on the sensor data in each sensor partition to determine the data fluctuation in each sensor partition, so as to facilitate targeted updating of the frequency required for collecting sensor data in each sensor partition according to the data fluctuation, determine the update collection frequency of each sensor partition, so as to improve the collection efficiency of sensor data in certain sensor partitions in a targeted manner, and then collect sensor data in the corresponding sensor partition according to the update collection frequency of the sensor partition, so as to adjust the amount of sensor data that needs to be collected in each partition in a targeted manner, avoid concentration of computing power, reduce the delay in collecting sensor data, and improve the real-time performance of collecting sensor data.

[0074] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 5 , step S20 also includes steps S01 to S03:

[0075] Step S01, selecting from the sensor partitions the partitions to be adjusted whose data fluctuations satisfy the preset adjustment fluctuations;

[0076] Step S02, adjusting the frequency of collecting sensor data from each of the partitions to be adjusted in real time, and determining the latest collection frequency of each of the partitions to be adjusted;

[0077] Step S03: based on the latest acquisition frequency, the recorded acquisition frequency of each sensor partition is updated to determine the updated acquisition frequency of each sensor partition.

[0078] It should be noted that the preset adjustment fluctuation is a pre-set threshold or standard used to determine whether the data fluctuation reaches the level that requires adjustment of the data collection frequency. If the data fluctuation exceeds this preset value, the system will consider that the current data changes greatly and the collection frequency needs to be increased to capture more detailed information. The partitions to be adjusted are selected from all sensor partitions, and their data fluctuations meet the preset adjustment fluctuation conditions. The data of these partitions changes greatly, so their data collection frequency needs to be adjusted in real time.

[0079] It can be understood that according to the data fluctuations, the partitions to be adjusted that need to adjust the collection frequency are screened out from the sensor partitions to facilitate targeted resource adjustment of the partitions to be adjusted, and then the collection frequency of the partitions to be adjusted is adjusted. By dynamically adjusting the collection frequency, resources can be allocated more reasonably to avoid excessive collection of stable data, and to avoid the resources allocated to sensor partitions with large data fluctuations not being able to meet the corresponding collection requirements, resulting in delays in the collection of sensor data, thereby improving the overall data collection efficiency and improving the real-time nature of the collected sensor data.

[0080] In the specific implementation, by monitoring the data changes of each sensor partition, calculating the data fluctuation value, and comparing the fluctuation value with the preset adjustment fluctuation threshold, when the data fluctuation value exceeds the threshold, the partition is determined as the partition to be adjusted, and then the frequency of data collection is dynamically adjusted according to the data fluctuation characteristics of the partition to be adjusted. For example, for partitions with large data fluctuations, the collection frequency can be increased to more accurately capture data changes; for partitions with small data fluctuations, the collection frequency can be appropriately reduced to reduce resource consumption, and then the adjusted collection frequency is recorded in the corresponding partition information so that subsequent data collection work can be carried out according to the new frequency.

[0081] Furthermore, if there are more than two partitions to be adjusted, step S02 further includes:

[0082] Comparing the fluctuations of the partitions to be adjusted;

[0083] If there is no partition with the same fluctuation among the partitions to be adjusted, determining whether there is a priority adjustment partition whose fluctuation satisfies the priority adjustment fluctuation among the partitions to be adjusted;

[0084] If so, the acquisition frequency of the priority-adjusted partition is adjusted first, and the importance of the remaining partitions in the partition to be adjusted is determined;

[0085] Based on the importance, the acquisition frequencies of the remaining partitions are adjusted in turn to determine the latest acquisition frequency of each partition to be adjusted.

[0086] It should be noted that the priority adjustment of fluctuations is the fluctuation value of the collection frequency that the user sets based on experience, ignoring the importance. For example, when the range of fluctuations is greater than or equal to 0.4, the collection frequency of the corresponding partition needs to be adjusted first, that is, the priority adjustment fluctuation is 0.4, which is used to identify the partitions that need to adjust the collection frequency first among multiple partitions to be adjusted. When the fluctuation of a partition meets this priority adjustment fluctuation, it means that the data of the partition changes more drastically, and more frequent data collection is required to accurately reflect its changes. The priority adjustment partition is the partition whose fluctuation meets the priority adjustment fluctuation among multiple partitions to be adjusted. This partition can be the partition that the user focuses on. This partition will be given priority to adjust the collection frequency to ensure that its data can be collected in a timely and accurate manner. The latest collection frequency is the new collection frequency adopted by a partition after the collection frequency of the partition is adjusted. This frequency will replace the original collection frequency and be used for subsequent data collection work.

[0087] It should be noted that when adjusting the collection frequency of multiple partitions to be adjusted, in addition to giving priority to adjusting the partitions, it is also necessary to consider the importance of other partitions. The importance can be comprehensively evaluated based on factors such as the actual application requirements of the partition, the importance of the data, and the impact on the overall performance. Partitions with higher importance will be given priority in adjusting the collection frequency.

[0088] It can be understood that first adjusting the partitions whose fluctuations are greater than or equal to the priority adjustment fluctuations can ensure that the data of these partitions are collected more timely, thereby more accurately reflecting the actual situation. Then, according to the importance of each sensor partition, the partitions to be adjusted are adjusted in turn to ensure that sensor data can be collected from the key partitions in a timely manner, avoiding the delay in collecting sensor data from the key partitions, and then improving the real-time performance of collecting sensor data in a targeted manner according to user needs.

[0089] It is understandable that, since the importance of each sensor partition is set by the user, the acquisition frequency can be adaptively adjusted according to data fluctuations, making the system more flexible and reliable, and helping to cope with various complex and changing application scenarios.

[0090] Furthermore, after the step of comparing the fluctuations of the partitions to be adjusted, the step further includes:

[0091] If there are partitions with the same fluctuation among the partitions to be adjusted, marking the same-frequency partitions with the same fluctuation from the partitions to be adjusted;

[0092] The acquisition frequencies of the same-frequency partitions are adjusted simultaneously to determine the latest acquisition frequencies of the partitions to be adjusted.

[0093] It should be noted that the same-frequency partitions are the partitions to be adjusted in which the fluctuation values ​​are the same or similar. The same-frequency partitions mean that their data change characteristics are similar, so the same collection frequency adjustment strategy can be applied to them.

[0094] It can be understood that for multiple partitions marked as same-frequency partitions, adopting the same acquisition frequency adjustment strategy and performing frequency adjustment at the same time or a similar time can ensure that these partitions maintain consistency in data acquisition, thereby improving the efficiency and accuracy of data acquisition.

[0095] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 6 After step S30, the sensor data collection method further includes steps S1 to S3:

[0096] Step S1, identifying key data marked as key partitions from the sensor data;

[0097] Step S2, adjusting the computing power arrangement for analyzing the sensor data based on the key data;

[0098] Step S3: Analyze the sensor data based on the computing power arrangement to determine the physical information of the target object collected by the sensor in each sensor partition.

[0099] It should be noted that in the sensor network, sensors are divided into different partitions according to certain standards or requirements. Among them, the partitions that are of special concern or considered important are called key partitions. Key partitions may be marked as key due to factors such as the importance of their data, frequency of change, and degree of impact on the system. Data identified in sensor data that is considered to be of special importance or criticality. These data may be marked as key data due to the size of their values, the severity of their changes, and their relevance to specific events. Computing power arrangement is the allocation and scheduling of computing resources required for data analysis and processing, including determining which computing devices to use, how much computing resources to allocate, and how to arrange the priority of computing tasks. Physical information is obtained by analyzing sensor data, which is information about the actual physical state or properties of the target object collected by the sensor. For example, the data collected by the temperature sensor can analyze the temperature information of the target object, and the data collected by the acceleration sensor can analyze the motion state of the target object.

[0100] It is understandable that by identifying key data and dynamically adjusting computing power arrangements, it is possible to ensure that computing power resources are allocated to the most important data first, thereby improving the overall efficiency of data processing and helping to obtain key information more quickly and make timely responses under limited computing power resources.

[0101] It is understandable that giving priority to the processing and analysis of key data can ensure a higher degree of accuracy of these data, and by adopting more appropriate algorithms and models, as well as allocating more computing resources, it is possible to more accurately extract and analyze useful information in the data, thereby obtaining more reliable physical information.

[0102] It is understandable that by dynamically adjusting the computing power arrangement, it is possible to avoid waste or shortage of computing power resources. When the amount of data is large or the data changes rapidly, the allocation of computing power resources can be automatically increased to meet the needs of data processing; when the amount of data is small or the data changes slowly, the allocation of computing power resources can be appropriately reduced to avoid delays in collecting sensor data, thereby improving the real-time nature of collecting sensor data.

[0103] In the specific implementation, the raw data collected by the sensor network is screened and identified, and the data from the key partitions that are considered to be particularly important or critical are found, and these data are marked as key data. After the key data are identified, the computing power arrangement for analyzing the sensor data is dynamically adjusted according to the importance and urgency of the data, including increasing the computing resource allocation for the key data, increasing the processing priority, or using more efficient algorithms to process the data, so as to ensure that the key data can be analyzed in a timely and accurate manner. After the computing power arrangement is completed, the sensor data is deeply analyzed and processed according to the adjusted computing power resources, for example, various algorithms and models are used to extract useful information from the data, and the physical information of the target object collected by the sensor in each sensor partition is determined based on this information, so as to better understand and comprehend the actual state of the environment or system monitored by the sensor network through this information. Among them, the physical information may include the specific values ​​of physical quantities such as temperature, humidity, pressure, and the motion state and position information of the target object.

[0104] Optionally, step S30 further includes:

[0105] Based on the updated acquisition frequency, adjusting a target bandwidth required for collecting sensor data in a corresponding sensor partition;

[0106] The target bandwidth is used to collect sensor data in the corresponding sensor partition.

[0107] It should be noted that the target bandwidth is the data transmission bandwidth determined when collecting sensor data based on the updated collection frequency and the actual needs of sensor partitioning. The target bandwidth should be sufficient to support the collection of sensor data at the updated collection frequency and ensure stable and efficient data transmission. The allocation and adjustment of bandwidth is crucial to ensure the real-time and accuracy of data collection.

[0108] It is understandable that by dynamically updating the acquisition frequency and adjusting the bandwidth allocation, data acquisition can be ensured to be more in line with actual needs. When data fluctuations are large or system requirements are high, the acquisition frequency and bandwidth allocation are increased to improve the real-time and accuracy of data acquisition; when data fluctuations are small or system requirements are low, the acquisition frequency and bandwidth allocation are reduced to save resources and reduce energy consumption. This dynamic adjustment mechanism can significantly improve the overall efficiency of data acquisition.

[0109] In the specific implementation, according to a certain strategy or condition (such as data fluctuation, system requirements, etc.), the acquisition frequency in the sensor partition is updated to obtain a new acquisition frequency, namely, the "updated acquisition frequency". The new frequency is used to guide the subsequent data collection work. Because different acquisition frequencies have different requirements for data transmission bandwidth, in order to ensure the real-time and accuracy of data collection, the data transmission bandwidth required for collecting sensor data in the corresponding sensor partition is adjusted based on this updated acquisition frequency, namely, the "target bandwidth". Finally, the sensor data in the corresponding sensor partition can be collected using this determined target bandwidth. That is, through reasonable bandwidth allocation and efficient acquisition strategies, the stable and efficient transmission of sensor data can be ensured, providing strong support for subsequent data processing and analysis.

[0110] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the sensor data acquisition method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0111] This application also provides a sensor data acquisition device, please refer to Figure 7 , the sensor data acquisition device comprises:

[0112] An analysis module 10, for performing fluctuation analysis on sensor data in each sensor partition in turn in response to the acquisition instruction, and determining data fluctuation of each sensor partition, wherein the sensor partition is obtained after partitioning the array sensor;

[0113] An adjustment module 20, configured to update the frequency of collecting sensor data of each sensor partition based on the data fluctuation, and determine the update collection frequency of each sensor partition;

[0114] The collection module 30 is used to collect sensor data in the corresponding sensor partition according to each of the update collection frequencies.

[0115] Optionally, the adjustment module 20 is also used to screen out the partitions to be adjusted from the sensor partitions whose data fluctuations meet the preset adjustment fluctuations; adjust the frequency of collecting sensor data from each partition to be adjusted in real time, and determine the latest collection frequency of each partition to be adjusted; based on the latest collection frequency, update the recorded collection frequency of each sensor partition, and determine the updated collection frequency of each sensor partition.

[0116] Optionally, if there are more than two partitions to be adjusted, the adjustment module 20 is also used to compare the fluctuations of each partition to be adjusted; if there is no partition with the same fluctuation among the partitions to be adjusted, it is determined whether there is a priority adjustment partition whose fluctuation meets the priority adjustment fluctuation among the partitions to be adjusted; if so, the frequency of acquisition of the priority adjustment partition is adjusted first, and the importance of the remaining partitions in the partition to be adjusted is determined; based on the importance, the acquisition frequency of the remaining partitions is adjusted in turn, and the latest acquisition frequency of each partition to be adjusted is determined.

[0117] Optionally, the adjustment module 20 is also used to mark the same-frequency partitions with the same fluctuation from the partitions to be adjusted if there are partitions with the same fluctuation among the partitions to be adjusted; adjust the acquisition frequency of the same-frequency partitions at the same time to determine the latest acquisition frequency of each partition to be adjusted.

[0118] Optionally, the acquisition module 30 is also used to identify key data marked as key partitions from the sensor data; based on the key data, adjust the computing power arrangement for analyzing the sensor data; analyze the sensor data based on the computing power arrangement, and determine the physical information of the target object collected by the sensor in each of the sensor partitions.

[0119] Optionally, the acquisition module 30 is used to adjust a target bandwidth required for acquiring sensor data in a corresponding sensor partition based on the updated acquisition frequency; and to acquire sensor data in the corresponding sensor partition using the target bandwidth.

[0120] The sensor data acquisition device provided by the present application adopts the sensor data acquisition method in the above embodiment, which can solve the technical problem of reduced real-time performance of collected data. Compared with the prior art, the beneficial effects of the sensor data acquisition device provided by the present application are the same as the beneficial effects of the sensor data acquisition method provided by the above embodiment, and other technical features in the sensor data acquisition device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0121] The present application provides a sensor data acquisition device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the sensor data acquisition method in the above-mentioned embodiment 1.

[0122] Reference below Figure 8 , which shows a schematic diagram of the structure of a sensor data acquisition device suitable for implementing the embodiment of the present application. The sensor data acquisition device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The sensor data acquisition device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0123] like Figure 8As shown, the sensor data acquisition device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the sensor data acquisition device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the sensor data acquisition device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a sensor data acquisition device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0124] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0125] The sensor data acquisition device provided by the present application adopts the sensor data acquisition method in the above embodiment, which can solve the technical problem of reduced real-time performance of collected data. Compared with the prior art, the beneficial effects of the sensor data acquisition device provided by the present application are the same as the beneficial effects of the sensor data acquisition method provided by the above embodiment, and other technical features in the sensor data acquisition device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0126] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0127] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0128] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the sensor data acquisition method in the above-mentioned embodiment.

[0129] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0130] The computer-readable storage medium may be included in the sensor data acquisition device; or may exist independently without being assembled into the sensor data acquisition device.

[0131] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the sensor data acquisition device, the sensor data acquisition device: responds to the acquisition instruction, performs fluctuation analysis on the sensor data in each sensor partition in turn, and determines the data fluctuation of each sensor partition, where the sensor partition is obtained after the array sensor is partitioned; based on the data fluctuation, updates the frequency of collecting sensor data for each sensor partition, and determines the update collection frequency of each sensor partition; and collects the sensor data in the corresponding sensor partition according to each update collection frequency.

[0132] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0134] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0135] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned sensor data acquisition method, and can solve the technical problem of reduced real-time performance of collected data. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the sensor data acquisition method provided in the above-mentioned embodiment, and will not be repeated here.

[0136] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned sensor data acquisition method when executed by a processor.

[0137] The computer program product provided by this application can solve the technical problem of reduced real-time performance of collected data. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the sensor data collection method provided by the above embodiment, which will not be repeated here.

[0138] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A sensor data acquisition method, characterized in that: The method includes: In response to the acquisition instruction, sequentially performing fluctuation analysis on the sensor data in each sensor partition to determine the data fluctuation of each sensor partition, wherein the sensor partition is obtained after partitioning the array sensor; Based on the data fluctuation, updating the frequency of collecting sensor data of each sensor partition, and determining the update collection frequency of each sensor partition; According to each of the update collection frequencies, the sensor data in the corresponding sensor partition is collected.

2. The method according to claim 1, characterized in that The step of updating the frequency of collecting sensor data of each sensor partition based on the data fluctuation and determining the update collection frequency of each sensor partition comprises: Selecting the partitions to be adjusted whose data fluctuations satisfy the preset adjustment fluctuations from the sensor partitions; Adjusting in real time the frequency of collecting sensor data from each of the partitions to be adjusted, and determining the latest collection frequency of each of the partitions to be adjusted; Based on the latest acquisition frequency, the recorded acquisition frequency of each sensor partition is updated to determine the updated acquisition frequency of each sensor partition.

3. The method according to claim 2, characterized in that If there are more than two partitions to be adjusted, the step of adjusting the frequency of collecting sensor data from each partition to be adjusted in real time and determining the latest collection frequency of each partition to be adjusted includes: Comparing the fluctuations of the partitions to be adjusted; If there is no partition with the same fluctuation among the partitions to be adjusted, determining whether there is a priority adjustment partition whose fluctuation satisfies the priority adjustment fluctuation among the partitions to be adjusted; If so, the acquisition frequency of the priority-adjusted partition is adjusted first, and the importance of the remaining partitions in the partition to be adjusted is determined; Based on the importance, the acquisition frequencies of the remaining partitions are adjusted in turn to determine the latest acquisition frequency of each partition to be adjusted.

4. The method according to claim 3, characterized in that After the step of comparing the fluctuations of the partitions to be adjusted, the following step further includes: If there are partitions with the same fluctuation among the partitions to be adjusted, marking the same-frequency partitions with the same fluctuation from the partitions to be adjusted; The acquisition frequencies of the same-frequency partitions are adjusted simultaneously to determine the latest acquisition frequencies of the partitions to be adjusted.

5. The method according to any one of claims 1 to 4, characterized in that After the step of collecting sensor data in the corresponding sensor partition according to each of the update collection frequencies, the following step further comprises: identifying key data marked as key partitions from the sensor data; Based on the key data, adjusting the computing power arrangement for analyzing the sensor data; The sensor data is analyzed based on the computing power arrangement to determine physical information of the target object collected by the sensor in each sensor partition.

6. The method according to any one of claims 1 to 4, characterized in that The step of collecting sensor data in the corresponding sensor partition according to each of the update collection frequencies comprises: Based on the updated acquisition frequency, adjusting a target bandwidth required for collecting sensor data in a corresponding sensor partition; The target bandwidth is used to collect sensor data in the corresponding sensor partition.

7. A sensor data acquisition device, characterized in that: The device comprises: An analysis module, for performing fluctuation analysis on sensor data in each sensor partition in turn in response to the acquisition instruction, and determining data fluctuation of each sensor partition, wherein the sensor partition is obtained after partitioning the array sensor; An adjustment module, configured to update the frequency of collecting sensor data of each sensor partition based on the data fluctuation, and determine the update collection frequency of each sensor partition; The collection module is used to collect sensor data in the corresponding sensor partition according to each of the update collection frequencies.

8. A sensor data acquisition device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the sensor data collection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the sensor data acquisition method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the sensor data collection method according to any one of claims 1 to 6 are implemented.

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