Equipment sharing method and system based on Internet of Things

Abstract

The invention relates to the technical field of equipment sharing management, and particularly discloses an equipment sharing method and system based on the Internet of Things, and the method comprises the steps: obtaining operation parameters and a task scheduling table of each piece of equipment in real time, and determining the reference selection probability of the equipment according to the operation parameters and the task scheduling table; receiving a to-be-processed task sent by a user, performing traversal comparison on the to-be-processed task and the task scheduling table of each device, and updating the reference selection probability of each device according to a traversal comparison result; and determining a target device of the to-be-processed task according to the updated reference selection probability, and synchronously updating a task scheduling table of the target device. According to the method, a random task allocation scheme is adopted, the selection probability of each device is determined in real time according to the actual situation during task allocation, and the task processing efficiency is improved on the basis of ensuring the safety through random allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of device sharing management, and in particular to a device sharing method and system based on the Internet of Things. Background Art

[0002] Device sharing refers to the use of the Internet of Things (IoT), cloud computing and intelligent management platforms to allow multiple users or organizations to share certain devices in order to improve resource utilization, reduce usage costs and enhance service efficiency.

[0003] The task allocation architecture in the existing device sharing process is a fixed rule-based allocation architecture. When a pending task sent by a user is received, the pending task is allocated to a certain device according to the preset allocation rule. This allocation rule is fixed, and a third party can easily know which device the pending task will be assigned to. If there is an attack intention, it is easy to locate the relevant device and carry out subsequent attacks. If a purely random task allocation rule is adopted, efficiency problems will easily occur, and task stacking will occasionally occur. The overall task processing efficiency is very low. How to improve the task processing efficiency while ensuring safety is the technical problem that the technical solution of the present invention aims to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a device sharing method and system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A device sharing method based on the Internet of Things, the method comprising:

[0007] The operating parameters and task schedule of each device are obtained in real time, and the benchmark selection probability of the device is determined according to the operating parameters and task schedule; wherein the calculation process of the benchmark selection probability includes the total number of devices, which is a variable and is updated in real time based on the preset device filing port;

[0008] Receive pending tasks sent by users, perform traversal comparison between the pending tasks and the task schedules of each device, and update the benchmark selection probability of each device according to the traversal comparison results;

[0009] Determine the target device for the task to be processed based on the updated benchmark selection probability, and simultaneously update the task schedule of the target device;

[0010] For any device, before executing a task, the device queries the user corresponding to the task, establishes a connection channel with the user, and provides real-time feedback on the task processing progress.

[0011] As a further solution of the present invention: the step of obtaining the operating parameters and task schedule of each device in real time and determining the benchmark selection probability of the device according to the operating parameters and task schedule includes:

[0012] Receive device registration requests in real time based on the preset device registration port, number the registered devices, and generate a unique label for each device as an identity label;

[0013] Acquiring operating parameters of the device based on sensors built into the device; the operating parameters are arrays containing time tags and identity tags, the number of columns in the array corresponds one-to-one to the number of the sensor, and the corresponding relationship is a preset value;

[0014] The probability calculation process is triggered according to the preset time interval. When the probability calculation process is triggered, the task schedule at the current time is read, and the operating parameters before the preset time length are obtained based on the current time;

[0015] The baseline selection probability of the equipment is determined based on the read task schedule and operating parameters.

[0016] As a further solution of the present invention: the step of determining the benchmark selection probability of the equipment according to the read task schedule and operating parameters includes:

[0017] Query the historical average processing time of each task in the task schedule and calculate the task volume; the historical average processing time of each task is updated in real time according to the processing progress of each device;

[0018] Self-comparison is performed on the operating parameters read at different times to determine the stability of the equipment;

[0019] Determine the benchmark selection probability according to the task volume and equipment stability;

[0020] The calculation process of the task volume is:

[0021] ; In the formula, For the task volume, is the proportionality constant, is the historical average processing time;

[0022] The calculation process of equipment stability is:

[0023] ; In the formula, is the device stability at the current moment, is the dimension of the running parameters, is the number of running parameters within the preset self-comparison time. is the preset proportional constant, The first The time point corresponding to the operating parameters is for the current moment; is a preset constant, The first The operating parameters The value of the element, The first one among the operating parameters corresponding to the current moment The value of the element; is a preset constant;

[0024] The process of determining the benchmark selection probability is as follows:

[0025] ; In the formula, is the probability of selecting the benchmark at the current moment, is the preset proportional constant.

[0026] As a further solution of the present invention: the step of receiving the pending tasks sent by the user, performing ergodic comparison between the pending tasks and the task schedules of each device, and updating the benchmark selection probability of each device according to the ergodic comparison results includes:

[0027] Receive pending tasks sent by users;

[0028] Perform a traversal comparison between the tasks to be processed and the task schedules of each device, and obtain the number of tasks in the task schedules of each device that are the same as the tasks to be processed as the number of identical tasks;

[0029] Modifying the benchmark selection probability according to the number of identical tasks;

[0030] The correction process is:

[0031] ; In the formula, is the revised benchmark selection probability, The probability of selecting the benchmark before the correction; For the same number of tasks, is the preset quantity threshold.

[0032] As a further solution of the present invention: the step of determining the target device of the task to be processed according to the updated benchmark selection probability and synchronously updating the task schedule of the target device includes:

[0033] Read the updated benchmark selection probabilities of all devices, and normalize the updated benchmark selection probabilities of all devices to obtain the normalized probabilities of each device; the sum of the normalized probabilities of all devices is one;

[0034] The range from zero to one is divided and allocated based on the standard probability of each device to obtain the value segment corresponding to each device;

[0035] Generate a random number in the range of 0 to 1, determine the numerical segment where the random number belongs, and query the device corresponding to the numerical segment as the target device of the task to be processed;

[0036] Insert the pending tasks into the task schedule of the target device.

[0037] As a further solution of the present invention: the method further comprises:

[0038] When the target device that completes the task to be processed is determined, the predicted processing time is calculated based on the task schedule of the target device;

[0039] Feedback the predicted processing time to the user;

[0040] When receiving a speed-up request input by a user, the task amount of each device is read, and the selection range of the target device is narrowed based on the task amount.

[0041] The technical solution of the present invention also provides a device sharing system based on the Internet of Things, the system comprising:

[0042] A benchmark probability determination module is used to obtain the operating parameters and task schedules of each device in real time, and determine the benchmark selection probability of the device according to the operating parameters and task schedules; wherein the calculation process of the benchmark selection probability includes the total number of devices, which is a variable and is updated in real time based on a preset device filing port;

[0043] The benchmark probability update module is used to receive pending tasks sent by users, perform traversal comparison between the pending tasks and the task schedules of each device, and update the benchmark selection probability of each device according to the traversal comparison results;

[0044] The device selection module is used to determine the target device of the task to be processed according to the updated benchmark selection probability, and synchronously update the task schedule of the target device;

[0045] The task progress interaction module is used for any device to query the user corresponding to the task before executing a task, establish a connection channel with the user, and provide real-time feedback on the task processing progress.

[0046] As a further solution of the present invention: the reference probability determination module includes:

[0047] An identity tag generating unit, used to receive a device registration request in real time based on a preset device registration port, number the registered devices, and generate a unique tag for each device as an identity tag;

[0048] An operating parameter acquisition unit, used to acquire the operating parameters of the device based on the sensor built into the device; the operating parameters are arrays containing time tags and identity tags, the number of columns in the array corresponds one-to-one to the number of the sensor, and the corresponding relationship is a preset value;

[0049] An intermittent triggering unit is used to trigger the probability calculation process according to a preset time interval. When a probability calculation process is triggered, the task schedule at the current moment is read, and the operating parameters before the preset time length are obtained based on the current moment;

[0050] The calculation execution unit is used to determine the baseline selection probability of the equipment according to the read task schedule and operation parameters.

[0051] As a further solution of the present invention: the operation execution unit includes:

[0052] The task volume calculation subunit is used to query the historical average processing time of each task in the task schedule and calculate the task volume; the historical average processing time of each task is updated in real time according to the processing progress of each device;

[0053] The self-comparison subunit is used to self-comparison the operating parameters read at different times to determine the stability of the equipment;

[0054] A parameter application subunit, for determining a benchmark selection probability according to the task volume and equipment stability;

[0055] The calculation process of the task volume is:

[0056] ; In the formula, For the task volume, is the proportionality constant, is the historical average processing time;

[0057] The calculation process of equipment stability is:

[0058] ; In the formula, is the device stability at the current moment, is the dimension of the running parameters, is the number of running parameters within the preset self-comparison time. is the preset proportional constant, The first The time point corresponding to the operating parameters is for the current moment; is a preset constant, The first The operating parameters The value of the element, The first one among the operating parameters corresponding to the current moment The value of the element; is a preset constant;

[0059] The process of determining the benchmark selection probability is as follows:

[0060] ; In the formula, is the probability of selecting the benchmark at the current moment, is the preset proportional constant.

[0061] As a further solution of the present invention: the reference probability updating module includes:

[0062] A task receiving unit, used for receiving tasks to be processed sent by users;

[0063] A traversal comparison unit is used to traversally compare the task to be processed with the task schedule of each device, and obtain the number of tasks in the task schedule of each device that are the same as the task to be processed as the number of identical tasks;

[0064] A correction execution unit, used for correcting the benchmark selection probability according to the same number of tasks;

[0065] The correction process is:

[0066] ; In the formula, is the revised benchmark selection probability, The probability of selecting the benchmark before the correction; For the same number of tasks, is the preset quantity threshold.

[0067] Compared with the prior art, the present invention has the following beneficial effects: the present invention adopts a random task allocation scheme, and when allocating tasks, the selection probability of each device is determined in real time according to the actual situation, thereby improving the task processing efficiency on the basis of ensuring safety through random allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0069] Figure 1 The flowchart of the device sharing method based on the Internet of Things is shown in FIG.

[0070] Figure 2 This is a first sub-flow diagram of a device sharing method based on the Internet of Things.

[0071] Figure 3This is a second sub-flow diagram of the device sharing method based on the Internet of Things.

[0072] Figure 4 This is a third sub-process flowchart of the device sharing method based on the Internet of Things.

[0073] Figure 5 This is a structural block diagram of the device sharing system based on the Internet of Things. DETAILED DESCRIPTION

[0074] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] Figure 1 The flowchart of the device sharing method based on the Internet of Things is shown in FIG. 1 . In an embodiment of the present invention, a device sharing method based on the Internet of Things includes:

[0076] Step S100: acquiring the operating parameters and task schedule of each device in real time, and determining the benchmark selection probability of the device according to the operating parameters and task schedule; wherein the calculation process of the benchmark selection probability includes the total number of devices, which is a variable and is updated in real time based on a preset device filing port;

[0077] The devices targeted by this application are of the same type, differing only in frequency of use and task schedules. In layman's terms, they are the same type of devices, which may have differences in components or usage duration. In addition, different devices target different tasks. In actual scenarios, a platform that provides device sharing services generally does not only provide one type of device. In this case, the platform needs to first classify similar devices into one category, perform independent analysis and processing on similar devices, and build a shared architecture. The solution provided by this application is the independent analysis and processing process for similar devices.

[0078] For each device, obtain the device's operating parameters and task schedule. Based on the operating parameters, we can determine whether the device is in a stable state. Based on the task schedule, we can determine the device's workload. By combining these two types of data, we can determine the probability of each device being selected when facing a new task, which is called the benchmark selection probability. New tasks can be assigned to corresponding devices based on the benchmark selection probability.

[0079] It should be noted that the equipment of the platform is updated in real time, because the platform will add or reduce equipment according to actual conditions, and adding equipment generally means renting equipment from other places; under the architecture of the technical solution of the present invention, it is generally a group of users, each providing different equipment to the platform, and the platform will also provide some equipment, and finally use these devices to provide users with different services, thereby building a device sharing architecture.

[0080] Step S200: receiving the pending tasks sent by the user, performing a traversal comparison between the pending tasks and the task schedules of each device, and updating the benchmark selection probability of each device according to the traversal comparison results;

[0081] Each user can upload pending tasks, and compare the pending tasks with the task schedules of each device in an erroneous manner. The similarity between the pending tasks and the task schedules can be determined, and the benchmark selection probability of each device can be updated. The update logic of this application is to place the pending tasks in the task schedules with a higher degree of similarity as much as possible. This is because, for a device, it will definitely perform some preprocessing when processing tasks. If it processes the same task, it can save some preprocessing time and improve task processing efficiency.

[0082] However, the core of this application is that the task allocation process retains randomness and has a high degree of similarity, but the corresponding modified benchmark selection probability is greater and is not necessarily allocated to the corresponding device.

[0083] In addition, regarding the similarity between the pending tasks and each task schedule, a relatively simple method is to obtain the number of tasks in the task schedule that are the same as the pending tasks. The larger the number of tasks, the higher the similarity.

[0084] Step S300: determining the target device of the task to be processed according to the updated benchmark selection probability, and synchronously updating the task schedule of the target device;

[0085] After the benchmark selection probability of each device is updated, a device is randomly selected from multiple devices according to the updated benchmark selection probability as the device to process the pending task, which is called the target device; the pending task is inserted into the task schedule corresponding to the target device, and its task schedule is updated.

[0086] From the above content, it can be concluded that the process of selecting the target device is random and highly random. This randomness also means unpredictability. It is impossible to know which device will process a certain pending task, which provides higher security.

[0087] Step S400: For any device, before executing a task, the device queries the user corresponding to the task, establishes a connection channel with the user, and provides real-time feedback on the task processing progress;

[0088] For any device, the device processes the tasks in the task schedule in sequence. Whenever the next task needs to be processed, it queries the user corresponding to the task, establishes a connection channel with the user, and provides real-time feedback on the task processing progress to the user. There is no limit on the amount of feedback content, which can be all processing data or only processing progress. The processing progress is generally in the form of a percentage, indicating the proportion of tasks that have been processed.

[0089] Figure 2 The first sub-flow chart of the device sharing method based on the Internet of Things, wherein the step of obtaining the operating parameters and task schedule of each device in real time and determining the benchmark selection probability of the device according to the operating parameters and task schedule comprises:

[0090] Step S101: receiving a device registration request in real time based on a preset device registration port, numbering the registered devices, and generating a unique label for each device as an identity label;

[0091] Step S102: obtaining the operating parameters of the device based on the sensors built into the device; the operating parameters are arrays containing time tags and identity tags, the number of columns in the array corresponds one-to-one to the number of the sensors, and the corresponding relationship is a preset value;

[0092] Step S103: triggering a probability calculation process according to a preset time interval. When a probability calculation process is triggered, reading the task schedule at the current time, and obtaining the operating parameters before the preset time based on the current time;

[0093] Step S104: Determine the baseline selection probability of the equipment according to the read task schedule and operating parameters.

[0094] The device is a shared device, which is mainly provided by users. The platform only assists in providing some devices. It is completely autonomous for users to provide devices. Users can send provision requests or withdrawal requests at any time. The preset device registration port receives device registration requests (including provision requests and withdrawal requests) in real time, numbers the registered devices, and generates a unique label for each device as an identity label. This process is carried out in real time.

[0095] Then, the operating parameters of the device are obtained based on the sensors built into the device. Since all devices are of the same type, the sensors in the devices are the same. The sensors in the devices are numbered in advance and an array is created. Each element in the array corresponds to a sensor. In actual applications, the operating data of the sensor at each moment is obtained and inserted into the corresponding position in the array. The resulting array containing specific data is called operating parameters. The operating parameters contain an identity tag, indicating which device the operating data is from, and the operating parameters contain a time tag, indicating when the operating data was obtained.

[0096] As a preferred embodiment of the technical solution of the present invention, the step of determining the benchmark selection probability of the equipment according to the read task schedule and operating parameters includes:

[0097] Query the historical average processing time of each task in the task schedule and calculate the task volume; the historical average processing time of each task is updated in real time according to the processing progress of each device;

[0098] Self-comparison is performed on the operating parameters read at different times to determine the stability of the equipment;

[0099] The benchmark selection probability is determined based on the task volume and equipment stability.

[0100] The above content provides a specific solution for generating a benchmark selection probability. In the technical solution of the present invention, the types of tasks that users need to process are limited (the types of devices are limited, and the tasks that each device can complete are also limited). For each task, the processing time in each device is recorded in real time, which is called the historical processing time. The mean of the historical processing time is calculated to obtain the historical average processing time. The historical average processing time indicates the number of tasks. The historical average processing time is converted into a numerical value to obtain a parameter used to indicate the number of tasks, which is the task amount in the above content. Since the work of each device is carried out in real time, the historical average processing time of each task is updated in real time according to the processing progress of each device.

[0101] The operating parameters read at different times are self-compared. The operating parameters are the data of the equipment during operation. By analyzing the data, a parameter reflecting the working stability of the equipment can be determined, which is called equipment stability.

[0102] Finally, the two parameters of task volume and equipment stability are combined to comprehensively determine the benchmark selection probability.

[0103] In an example of the technical solution of the present invention, a solution for determining the benchmark selection probability is provided, which is as follows:

[0104] The calculation process of the task volume is:

[0105] ; In the formula, For the task volume, is the proportionality constant, is the historical average processing time;

[0106] The calculation process of equipment stability is:

[0107] ; In the formula, is the device stability at the current moment, is the dimension of the running parameters, is the number of running parameters within the preset self-comparison time. is the preset proportional constant, The first The time point corresponding to the operating parameters is for the current moment; is a preset constant, The first The operating parameters The value of the element, The first one among the operating parameters corresponding to the current moment The value of the element; is a preset constant.

[0108] The process of determining the benchmark selection probability is as follows:

[0109] ; In the formula, is the probability of selecting the benchmark at the current moment, is the preset proportional constant.

[0110] The above contents provide the determination schemes for the task volume, equipment stability and benchmark selection probability respectively. The principle of the determination process is explained as follows:

[0111] The amount of tasks is proportional to the historical average processing time, which also means is a positive number.

[0112] The device stability is the device stability at each moment. For a device, its operating parameters are an array, and each element corresponds to a sensor. When calculating the device stability at a certain moment, first determine a preset time length, take the current moment as the starting point, reverse the preset time length, and obtain the operating parameters within the reversed preset time length. For each element in the operating parameter, calculate the absolute value of the difference between it and the corresponding element in the operating parameter at the current moment, and then add the absolute values ​​together to represent the fluctuation value of the element (reflecting the degree of fluctuation). Among them, when adding the absolute values ​​together, Gaussian weights are introduced so that the larger the time difference, the smaller the corresponding weight, that is, item.

[0113] After the fluctuation of each element is determined, the fluctuation values ​​of each element are added to obtain the final fluctuation value. A subtraction function is compounded on the final fluctuation value to obtain the device stability that reflects the stability.

[0114] The probability of benchmark selection is directly proportional to the equipment stability and inversely proportional to the task volume.

[0115] Figure 3 The second sub-flow diagram of the device sharing method based on the Internet of Things, wherein the steps of receiving the pending tasks sent by the user, performing traversal comparison between the pending tasks and the task schedules of each device, and updating the benchmark selection probability of each device according to the traversal comparison results include:

[0116] Step S201: receiving a task to be processed sent by a user;

[0117] Step S202: performing a traversal comparison between the task to be processed and the task schedule of each device, and obtaining the number of tasks in the task schedule of each device that are identical to the task to be processed as the number of identical tasks;

[0118] Step S203: modifying the benchmark selection probability according to the number of identical tasks.

[0119] Receive pending tasks sent by users, and perform traversal comparison between the pending tasks and the task schedules of each device. Since the task names are limited and pre-set, the comparison process can use the most basic AND operation. After the traversal comparison is completed, the number of tasks in the task schedules of each device that are the same as the pending tasks can be obtained, which is called the number of identical tasks. The more identical tasks there are, the more similar the pending tasks are to the corresponding task schedules, and the more compatible they are with the corresponding devices. Finally, the benchmark selection probability can be corrected according to the number of identical tasks.

[0120] Regarding the correction process of the benchmark selection probability, one feasible solution is as follows:

[0121] The correction process is:

[0122] ; In the formula, is the revised benchmark selection probability, The probability of selecting the benchmark before the correction; For the same number of tasks, is the preset quantity threshold.

[0123] The correction process is not complicated. The ratio of the number of identical tasks to the quantity threshold is calculated as the correction coefficient and then multiplied by the original benchmark selection probability. The more identical tasks there are, the greater the revised benchmark selection probability.

[0124] Figure 4 The third sub-flow diagram of the device sharing method based on the Internet of Things, wherein the step of determining the target device of the task to be processed according to the updated benchmark selection probability and synchronously updating the task schedule of the target device includes:

[0125] Step S301: reading the updated benchmark selection probabilities of all devices, and normalizing the updated benchmark selection probabilities of all devices to obtain the standard probabilities of each device; the sum of the standard probabilities of all devices is one;

[0126] Step S302: dividing and allocating the range from zero to one based on the standard probability of each device to obtain a numerical segment corresponding to each device;

[0127] Step S303: Generate a random number in the range of 0 to 1, determine the numerical segment where the random number is located, and query the device corresponding to the numerical segment as the target device of the task to be processed;

[0128] Step S304: inserting the task to be processed into the task schedule of the target device.

[0129] The values ​​of the updated benchmark selection probabilities of each device are independent and have no relationship with each other. This makes it inconvenient to operate when randomly selecting devices. To this end, the present application normalizes the updated benchmark selection probabilities of all devices to obtain the standardized probabilities of each device. The normalization process is to first calculate the sum of the benchmark selection probabilities of all devices, and then calculate the ratio of the benchmark selection probability of each device to the sum to obtain the standardized probability of each device. After this calculation, the sum of the standardized probabilities of all devices is one.

[0130] Then, the standard probability of the devices is read in order (device number), and the range from zero to one is divided and allocated. The process of division and allocation is easy to understand. For example, if the standard probability of the first device is 2%, then the range of (0.0.2] corresponds to the first device. After the division and allocation are completed, a random number is generated in the range from zero to one. The device corresponding to the numerical segment of the random number is the selected target device. Finally, the task to be processed can be inserted into the task schedule of the target device.

[0131] It is worth mentioning that there are many ways to insert the pending task into the task schedule of the target device. For example, insert it in chronological order. At this time, the pending task is at the end of the task schedule. If the efficiency of the preprocessing process is taken into consideration, the task that is the same as the pending task can be located in the task schedule and the pending task can be inserted after the same task. At this time, one preprocessing process can be saved. In fact, there are many ways to insert the pending task after the same task, because there may be multiple identical tasks in the task schedule. Generally, it is inserted after the last identical task to try to ensure the sequential task processing architecture.

[0132] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0133] When the target device that completes the task to be processed is determined, the predicted processing time is calculated based on the task schedule of the target device;

[0134] Feedback the predicted processing time to the user;

[0135] When receiving a speed-up request input by a user, the task amount of each device is read, and the selection range of the target device is narrowed based on the task amount.

[0136] In an example of the technical solution of the present invention, an interactive solution is provided. When a user sends a task to be processed and the execution platform of this method determines the target device for completing the task to be processed, the predicted processing time is calculated based on the insertion position of the task to be processed. Since the historical average processing time of each task is known, the task to be processed and all previous tasks are queried, and the sum of the historical average processing times of the queried tasks is calculated as the predicted processing time.

[0137] The predicted processing time is fed back to the user, and the user determines whether to input a speed-up request. When the speed-up request input by the user is received, the task volume of each device is read, and the device with a task volume less than a preset task volume threshold is selected as the selection range of the target device.

[0138] In the above content, devices with a task volume less than a preset task volume threshold are selected as the selection range of the target device. This process actually affects step S301. When the target device selection range is narrowed, the "reading the updated baseline selection probability of all devices" in step S301 becomes "reading the updated baseline selection probability of devices within the selection range". Accordingly, the subsequent target device selection process only occurs within the selection range.

[0139] It is worth mentioning that the above scheme essentially provides a recursive correction architecture. In practical applications, the concept of security can also be introduced. For example, the number of times each device has been attacked is obtained. When allocating tasks to be processed, devices whose number of attacks is less than a preset number threshold are selected as the selection range. The same as the above content, the process of step S301 is recursively adjusted. At this time, the selected target device is a device with a sufficiently high security level (the number of attacks is sufficiently small).

[0140] Figure 5 1 is a structural block diagram of a device sharing system based on the Internet of Things. In an embodiment of the present invention, a device sharing system based on the Internet of Things, the system 10 includes:

[0141] The benchmark probability determination module 11 is used to obtain the operating parameters and task schedule of each device in real time, and determine the benchmark selection probability of the device according to the operating parameters and task schedule; wherein the calculation process of the benchmark selection probability includes the total number of devices, the total number of devices is a variable, and is updated in real time based on a preset device filing port;

[0142] The benchmark probability updating module 12 is used to receive the pending tasks sent by the user, perform ergodic comparison between the pending tasks and the task schedules of each device, and update the benchmark selection probability of each device according to the ergodic comparison results;

[0143] The device selection module 13 is used to determine the target device of the task to be processed according to the updated benchmark selection probability, and synchronously update the task schedule of the target device;

[0144] The task progress interaction module 14 is used for any device to query the user corresponding to the task before executing a task, establish a connection channel with the user, and provide real-time feedback on the task processing progress.

[0145] Furthermore, the reference probability determination module 11 includes:

[0146] An identity tag generating unit, used to receive a device registration request in real time based on a preset device registration port, number the registered devices, and generate a unique tag for each device as an identity tag;

[0147] An operating parameter acquisition unit, used to acquire the operating parameters of the device based on the sensor built into the device; the operating parameters are arrays containing time tags and identity tags, the number of columns in the array corresponds one-to-one to the number of the sensor, and the corresponding relationship is a preset value;

[0148] An intermittent triggering unit is used to trigger the probability calculation process according to a preset time interval. When a probability calculation process is triggered, the task schedule at the current moment is read, and the operating parameters before the preset time length are obtained based on the current moment;

[0149] The calculation execution unit is used to determine the baseline selection probability of the equipment according to the read task schedule and operation parameters.

[0150] Specifically, the operation execution unit includes:

[0151] The task volume calculation subunit is used to query the historical average processing time of each task in the task schedule and calculate the task volume; the historical average processing time of each task is updated in real time according to the processing progress of each device;

[0152] The self-comparison subunit is used to self-comparison the operating parameters read at different times to determine the stability of the equipment;

[0153] A parameter application subunit, for determining a benchmark selection probability according to the task volume and equipment stability;

[0154] The calculation process of the task volume is:

[0155] ; In the formula, For the task volume, is the proportionality constant, is the historical average processing time;

[0156] The calculation process of equipment stability is:

[0157] ; In the formula, is the device stability at the current moment, is the dimension of the running parameters, is the number of running parameters within the preset self-comparison time. is the preset proportional constant, The first The time point corresponding to the operating parameters is for the current moment; is a preset constant, The first The operating parameters The value of the element, The first one among the operating parameters corresponding to the current moment The value of the element; is a preset constant;

[0158] The process of determining the benchmark selection probability is as follows:

[0159] ; In the formula, is the probability of selecting the benchmark at the current moment, is the preset proportional constant.

[0160] Furthermore, the reference probability updating module 12 includes:

[0161] A task receiving unit, used for receiving tasks to be processed sent by users;

[0162] A traversal comparison unit is used to traversally compare the task to be processed with the task schedule of each device, and obtain the number of tasks in the task schedule of each device that are the same as the task to be processed as the number of identical tasks;

[0163] A correction execution unit, used for correcting the benchmark selection probability according to the same number of tasks;

[0164] The correction process is:

[0165] ; In the formula, is the revised benchmark selection probability, The probability of selecting the benchmark before the correction; For the same number of tasks, is the preset quantity threshold.

[0166] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A device sharing method based on the Internet of Things, characterized in that: The method comprises: The operating parameters and task schedule of each device are obtained in real time, and the benchmark selection probability of the device is determined according to the operating parameters and task schedule; wherein the calculation process of the benchmark selection probability includes the total number of devices, which is a variable and is updated in real time based on the preset device filing port; Receive pending tasks sent by users, perform traversal comparison between the pending tasks and the task schedules of each device, and update the benchmark selection probability of each device according to the traversal comparison results; Determine the target device for the task to be processed based on the updated benchmark selection probability, and simultaneously update the task schedule of the target device; For any device, before executing a task, the device queries the user corresponding to the task, establishes a connection channel with the user, and provides real-time feedback on the task processing progress.

2. The device sharing method based on the Internet of Things according to claim 1 is characterized in that: The step of obtaining the operating parameters and task schedule of each device in real time and determining the benchmark selection probability of the device according to the operating parameters and task schedule comprises: Receive device registration requests in real time based on the preset device registration port, number the registered devices, and generate a unique label for each device as an identity label; Acquiring operating parameters of the device based on sensors built into the device; the operating parameters are arrays containing time tags and identity tags, the number of columns in the array corresponds one-to-one to the number of the sensor, and the corresponding relationship is a preset value; The probability calculation process is triggered according to the preset time interval. When the probability calculation process is triggered, the task schedule at the current time is read, and the operating parameters before the preset time length are obtained based on the current time; The baseline selection probability of the equipment is determined based on the read task schedule and operating parameters.

3. The device sharing method based on the Internet of Things according to claim 2 is characterized in that: The step of determining the benchmark selection probability of the equipment according to the read task schedule and operating parameters comprises: Query the historical average processing time of each task in the task schedule and calculate the task volume; the historical average processing time of each task is updated in real time according to the processing progress of each device; Self-comparison is performed on the operating parameters read at different times to determine the stability of the equipment; Determine the benchmark selection probability according to the task volume and equipment stability; The calculation process of the task volume is: ; In the formula, For the task volume, is the proportionality constant, is the historical average processing time; The calculation process of equipment stability is: ; In the formula, is the device stability at the current moment, is the dimension of the running parameters, is the number of running parameters within the preset self-comparison time. is the preset proportional constant, The first The time point corresponding to the operating parameters is for the current moment; is a preset constant, The first The operating parameters The value of the element, The first one among the operating parameters corresponding to the current moment The value of the element; is a preset constant; The process of determining the benchmark selection probability is as follows: ; In the formula, is the probability of selecting the benchmark at the current moment, is the preset proportional constant.

4. The device sharing method based on the Internet of Things according to claim 3 is characterized in that: The step of receiving the pending tasks sent by the user, performing ergodic comparison between the pending tasks and the task schedules of each device, and updating the benchmark selection probability of each device according to the ergodic comparison results includes: Receive pending tasks sent by users; Perform a traversal comparison between the tasks to be processed and the task schedules of each device, and obtain the number of tasks in the task schedules of each device that are the same as the tasks to be processed as the number of identical tasks; Modifying the benchmark selection probability according to the number of identical tasks; The correction process is: ; In the formula, is the revised benchmark selection probability, The probability of selecting the benchmark before the correction; For the same number of tasks, is the preset quantity threshold.

5. The device sharing method based on the Internet of Things according to claim 1 is characterized in that: The step of determining the target device of the task to be processed according to the updated benchmark selection probability and synchronously updating the task schedule of the target device comprises: Read the updated benchmark selection probabilities of all devices, and normalize the updated benchmark selection probabilities of all devices to obtain the normalized probabilities of each device; the sum of the normalized probabilities of all devices is one; The range from zero to one is divided and allocated based on the standard probability of each device to obtain the value segment corresponding to each device; Generate a random number in the range of 0 to 1, determine the numerical segment where the random number belongs, and query the device corresponding to the numerical segment as the target device of the task to be processed; Insert the pending tasks into the task schedule of the target device.

6. The device sharing method based on the Internet of Things according to claim 1 is characterized in that: The method further comprises: When the target device that completes the task to be processed is determined, the predicted processing time is calculated based on the task schedule of the target device; Feedback the predicted processing time to the user; When receiving a speed-up request input by a user, the task amount of each device is read, and the selection range of the target device is narrowed based on the task amount.

7. A device sharing system based on the Internet of Things, characterized in that: The system comprises: A benchmark probability determination module is used to obtain the operating parameters and task schedules of each device in real time, and determine the benchmark selection probability of the device according to the operating parameters and task schedules; wherein the calculation process of the benchmark selection probability includes the total number of devices, which is a variable and is updated in real time based on a preset device filing port; The benchmark probability update module is used to receive pending tasks sent by users, perform traversal comparison between the pending tasks and the task schedules of each device, and update the benchmark selection probability of each device according to the traversal comparison results; The device selection module is used to determine the target device of the task to be processed according to the updated benchmark selection probability, and synchronously update the task schedule of the target device; The task progress interaction module is used for any device to query the user corresponding to the task before executing a task, establish a connection channel with the user, and provide real-time feedback on the task processing progress.

8. The device sharing system based on the Internet of Things according to claim 7 is characterized in that: The reference probability determination module comprises: An identity tag generating unit, used to receive a device registration request in real time based on a preset device registration port, number the registered devices, and generate a unique tag for each device as an identity tag; An operating parameter acquisition unit, used to acquire the operating parameters of the device based on the sensor built into the device; the operating parameters are arrays containing time tags and identity tags, the number of columns in the array corresponds one-to-one to the number of the sensor, and the corresponding relationship is a preset value; An intermittent triggering unit is used to trigger the probability calculation process according to a preset time interval. When a probability calculation process is triggered, the task schedule at the current moment is read, and the operating parameters before the preset time length are obtained based on the current moment; The calculation execution unit is used to determine the baseline selection probability of the equipment according to the read task schedule and operation parameters.

9. The device sharing system based on the Internet of Things according to claim 8, characterized in that: The operation execution unit comprises: The task volume calculation subunit is used to query the historical average processing time of each task in the task schedule and calculate the task volume; the historical average processing time of each task is updated in real time according to the processing progress of each device; The self-comparison subunit is used to self-comparison the operating parameters read at different times to determine the stability of the equipment; A parameter application subunit, for determining a benchmark selection probability according to the task volume and equipment stability; The calculation process of the task volume is: ; In the formula, For the task volume, is the proportionality constant, is the historical average processing time; The calculation process of equipment stability is: ; In the formula, is the device stability at the current moment, is the dimension of the running parameters, is the number of running parameters within the preset self-comparison time. is the preset proportional constant, The first The time point corresponding to the operating parameters is for the current moment; is a preset constant, The first The operating parameters The value of the element, The first one among the operating parameters corresponding to the current moment The value of the element; is a preset constant; The process of determining the benchmark selection probability is as follows: ; In the formula, is the probability of selecting the benchmark at the current moment, is the preset proportional constant.

10. The device sharing system based on the Internet of Things according to claim 9, characterized in that: The benchmark probability updating module comprises: A task receiving unit, used for receiving tasks to be processed sent by users; A traversal comparison unit is used to traversally compare the task to be processed with the task schedule of each device, and obtain the number of tasks in the task schedule of each device that are the same as the task to be processed as the number of identical tasks; A correction execution unit, used for correcting the benchmark selection probability according to the same number of tasks; The correction process is: ; In the formula, is the revised benchmark selection probability, The probability of selecting the benchmark before the correction; For the same number of tasks, is the preset quantity threshold.

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