Method and device for evaluating value of data of Internet of Things equipment

Abstract

The invention provides a value evaluation method and device for data of Internet of Things equipment. The value evaluation method for the data of the Internet of Things equipment is suitable for the field of the Internet of Things and the related field of the Internet of Things, and the value of the data of the Internet of Things equipment obtained through evaluation based on the value evaluation method for the data of the Internet of Things equipment is objective and accurate; the standard and criterion of the value evaluation industry of the data of the Internet of Things equipment can be standardized, and then the standard and criterion of the value evaluation industry of data assets can be standardized. Secondly, the increase evaluation model combining the equipment increase model and the value evaluation model is adopted to evaluate the value of the data of the Internet of Things equipment, and the particularity of the data of the Internet of Things equipment is fully considered, so that the value of the data of the Internet of Things equipment is evaluated more comprehensively and more accurately, the objectivity of the evaluation result is enhanced, and the accuracy of the evaluation result is improved. And a new thought and a new method are provided for the value evaluation theory of the data of the Internet of Things equipment.

Description

Technical Field

[0001] This application relates to the field of Internet of Things technology, and in particular, to a method and device for evaluating the value of data of Internet of Things devices. Background Art

[0002] With the booming development of the digital economy, data assets have become an important part of the core competitive resources of enterprises.

[0003] In the field of Internet of Things, a large amount of data of Internet of Things devices is continuously generated, forming a considerable scale of data of Internet of Things devices. Gradually, the scale and complexity of the data of Internet of Things devices are also increasing. Thus, the commercial value and potential application prospects of the data of Internet of Things devices cannot be ignored. Therefore, the demand for evaluating the value of the data of Internet of Things devices is increasing day by day, and a unified and accurate evaluation standard is crucial for ensuring the fairness, justice, and transparency of the market.

[0004] However, traditional methods for evaluating the value of data, such as the market approach, cost approach, and income approach, etc., have many limitations when applied to the evaluation of the data of Internet of Things devices. For example, the accuracy rate of evaluating the value of the data of Internet of Things devices is low. Summary of the Invention

[0005] This application discloses a method and device for evaluating the value of data of Internet of Things devices.

[0006] In a first aspect, this application discloses a method for evaluating the value of data of Internet of Things devices, characterized in that the method includes:

[0007] Estimate the estimated number of Internet of Things devices in the Internet of Things device cluster within a future time period;

[0008] Obtain the online rate of the Internet of Things devices in the Internet of Things device cluster within a future time period;

[0009] Obtain the data revenue of a single Internet of Things device in the Internet of Things device cluster within a future time period;

[0010] Obtain the device cost of a single Internet of Things device in the Internet of Things device cluster within a future time period;

[0011] Estimate the value of the data of the Internet of Things devices in the Internet of Things device cluster within a future time period based on at least the estimated number, the online rate, the data revenue, and the device cost.

[0012] In an optional implementation manner, the estimating the value of the data of the Internet of Things devices in the Internet of Things device cluster within a future time period based on at least the estimated number, the online rate, the data revenue, and the device cost includes:

[0013] Calculate the difference between the data revenue and the device cost;

[0014] Calculate the first product between the difference and the estimated quantity;

[0015] Calculate the second product between the product and the online rate;

[0016] Obtain the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period at least according to the second product.

[0017] In an alternative implementation, the obtaining the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period at least according to the second product includes:

[0018] Obtain the number of intervals of the time period between the current time period where the current moment is located and the future time period;

[0019] Calculate the first sum value between the numerical value 1 and the capital interest rate;

[0020] Calculate the second sum value between the numerical value 1 and the number of intervals;

[0021] Calculate the power of the second sum value of the first sum value;

[0022] Calculate the ratio between the second product and the power of the second sum value of the first sum value;

[0023] Obtain the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period according to the ratio.

[0024] In an alternative implementation, the estimating the estimated quantity of the Internet of Things devices in the Internet of Things device cluster in a future time period includes:

[0025] Estimate the estimated quantity of the Internet of Things devices in the Internet of Things device cluster in a future time period based on a device growth model;

[0026] Wherein, the device growth model is obtained by fitting a loss function based on a historical data set, and the historical data set includes each historical time period and the historical quantity of the Internet of Things devices in the Internet of Things device cluster at each historical time period.

[0027] In an alternative implementation, the estimating the estimated quantity of the Internet of Things devices in the Internet of Things device cluster in a future time period based on the device growth model includes:

[0028] Estimate the estimated quantity of the Internet of Things devices in the Internet of Things device cluster in a future time period based on the following formula;

[0029]

[0030] Among them, the above formula is the device growth model, N t is the estimated number of Internet of Things devices in the Internet of Things device cluster within a future time period. m, q, and p are all parameters obtained by fitting in the device growth model, and t is the second sum value between the value 1 and the number of intervals; the number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period.

[0031] In an alternative implementation, the method further includes:

[0032] When the current moment reaches the future time period, obtain the actual number of Internet of Things devices in the Internet of Things device cluster within the future time period;

[0033] When the actual number is different from the estimated number, optimize the device growth model according to the future time period, the estimated number, and the actual number through a loss function.

[0034] In a second aspect, the present application discloses a value evaluation device for data of Internet of Things devices, and the device includes:

[0035] A first estimation module, configured to estimate the estimated number of Internet of Things devices in the Internet of Things device cluster within a future time period;

[0036] A first acquisition module, configured to acquire the online rate of Internet of Things devices in the Internet of Things device cluster within a future time period;

[0037] A second acquisition module, configured to acquire the data revenue of a single Internet of Things device in the Internet of Things device cluster within a future time period;

[0038] A third acquisition module, configured to acquire the device cost of a single Internet of Things device in the Internet of Things device cluster within a future time period;

[0039] A second estimation module, configured to estimate the value of data of Internet of Things devices in the Internet of Things device cluster within a future time period at least according to the estimated number, the online rate, the data revenue, and the device cost.

[0040] In an alternative implementation, the second estimation module includes:

[0041] A first calculation unit, configured to calculate the difference between the data revenue and the device cost;

[0042] A second calculation unit, configured to calculate the first product of the difference and the estimated number;

[0043] A third calculation unit for calculating a second product between the product and the online rate;

[0044] A first acquisition unit for acquiring the value of data of Internet of Things devices in an Internet of Things device cluster in a future time period at least according to the second product.

[0045] In an optional implementation manner, the acquisition unit includes:

[0046] A first acquisition subunit for acquiring the number of time period intervals between the current time period where the current moment is located and the future time period;

[0047] A first calculation subunit for calculating a first sum value between the numerical value 1 and the capital interest rate;

[0048] A second calculation subunit for calculating a second sum value between the numerical value 1 and the number of intervals;

[0049] A third calculation subunit for calculating the power of the second sum value of the first sum value;

[0050] A fourth calculation subunit for calculating a ratio between the second product and the power of the second sum value of the first sum value;

[0051] A second acquisition subunit for acquiring the value of data of Internet of Things devices in an Internet of Things device cluster in a future time period according to the ratio.

[0052] In an optional implementation manner, the first estimation module includes:

[0053] An estimation unit for estimating the estimated number of Internet of Things devices in an Internet of Things device cluster in a future time period based on a device growth model;

[0054] Wherein, the device growth model is obtained by fitting through a loss function according to a historical data set, and the historical data set includes each historical time period and the historical number of Internet of Things devices in the Internet of Things device cluster at each historical time period.

[0055] In an optional implementation manner, the estimation unit is specifically configured to:

[0056] Estimate the estimated number of Internet of Things devices in an Internet of Things device cluster in a future time period based on the following formula;

[0057]

[0058] Wherein, the above formula is the device growth model, N tis the estimated number of IoT devices in the IoT device cluster within a future time period, where m, q, and p are all parameters obtained by fitting in the device growth model, and t is the second sum value between the value 1 and the number of intervals; the number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period.

[0059] In an optional implementation, the first estimation module further includes:

[0060] A second acquisition unit, configured to acquire the actual number of IoT devices in the IoT device cluster within the future time period when the current moment reaches the future time period;

[0061] An optimization unit, configured to optimize the device growth model through a loss function according to the future time period, the estimated quantity, and the actual quantity when the actual quantity is different from the estimated quantity.

[0062] In a third aspect, the present application discloses an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to execute the method described in any of the above aspects.

[0063] In a fourth aspect, the present application discloses a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the electronic device, enabling the electronic device to execute the method described in any of the above aspects.

[0064] In a fifth aspect, the present application discloses a computer program product, when the instructions in the computer program product are executed by the processor of the electronic device, enabling the electronic device to execute the method described in any of the above aspects.

[0065] The technical solution provided by the present application may include the following beneficial effects:

[0066] In the present application, the estimated number of IoT devices in the IoT device cluster within a future time period is estimated. The online rate of the IoT devices in the IoT device cluster within the future time period is obtained. The data revenue of a single IoT device in the IoT device cluster within the future time period is obtained. The device cost of a single IoT device in the IoT device cluster within the future time period is obtained. The value of the data of the IoT devices in the IoT device cluster within the future time period is estimated based on at least the estimated quantity, the online rate, the data revenue, and the device cost.

[0067] The method for evaluating the value of data of the Internet of Things devices in this application is applicable to the field of the Internet of Things and related fields. The value of data of the Internet of Things devices evaluated based on the method for evaluating the value of data of the Internet of Things devices in this application is objective and accurate, which helps to standardize the standards and guidelines for the industry of evaluating the value of data of the Internet of Things devices, and further helps to standardize the standards and guidelines for the industry of evaluating the value of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flowchart of the steps of a method for evaluating the value of data of an Internet of Things device in this application.

[0069] Figure 2 is a block diagram of the structure of an apparatus for evaluating the value of data of an Internet of Things device in this application.

[0070] Figure 3 is a block diagram of an electronic device in this application.

[0071] Figure 4 is a block diagram of an electronic device in this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0073] The inventor found that the value of data of the Internet of Things devices is not only reflected in its direct economic benefits, but sometimes also often reflected in its indirect impact on the enterprise's innovation ability, market competitiveness, and the practicality of the Internet of Things devices.

[0074] Therefore, in order to more accurately evaluate the value of data of the Internet of Things devices, the technical solution of this application is proposed.

[0075] Referring to Figure 1 , a flowchart of the steps of a method for evaluating the value of data of an Internet of Things device in this application is shown. This method is applied to an electronic device, and the electronic device may include a terminal or a server, etc. Among them, this method includes:

[0076] In step S101, estimate the estimated number of Internet of Things devices in the Internet of Things device cluster in a future time period.

[0077] In this application, the Internet of Things devices include: smart home devices, wearable devices, industrial Internet of Things devices, intelligent transportation devices, environmental monitoring devices, and medical Internet of Things devices, etc.

[0078] Among them, smart home devices are connected via the Internet to achieve automation and remote control, enhancing the convenience and security of life.

[0079] Smart home devices include:

[0080] Smart thermostats that can remotely control indoor temperature and learn user preferences.

[0081] Smart door locks that support remote unlocking and authorized access.

[0082] Smart lighting systems that can control lights via mobile phones or voice assistants.

[0083] Smart security systems, including cameras, door and window sensors, and alarms, which can monitor home security in real time.

[0084] Wearable devices are used for health monitoring and activity tracking.

[0085] Wearable devices include:

[0086] Smartwatches with health monitoring, sports tracking, and notification reminder functions.

[0087] Fitness trackers that can record steps, heart rate, sleep quality, etc.

[0088] Health monitoring devices, such as blood glucose meters, blood pressure monitors, and electrocardiogram devices, which can monitor health data in real time.

[0089] Industrial Internet of Things devices are used to improve production efficiency and optimize processes.

[0090] Industrial Internet of Things devices include:

[0091] Smart sensors for monitoring device status, environmental parameters, etc.

[0092] Automation control systems, such as smart agricultural irrigation systems, which can automatically water according to soil humidity.

[0093] Intelligent transportation devices are used to improve traffic efficiency and safety.

[0094] Intelligent transportation devices include:

[0095] Autonomous vehicles: Equipped with lidar, cameras, and sensors, they can achieve autonomous driving.

[0096] Intelligent traffic management systems: Used to monitor traffic flow and optimize traffic lights.

[0097] Environmental monitoring devices are used to monitor air quality, water quality, etc. in real time.

[0098] Environmental monitoring devices include:

[0099] Air quality monitor: It can monitor the indoor pollution level in real time.

[0100] Medical Internet of Things devices are used for remote healthcare and health monitoring.

[0101] Medical Internet of Things devices include:

[0102] Smart cardiac pacemaker: It can monitor the heart condition in real time.

[0103] Remote healthcare devices: Such as smart mattresses, which can monitor the vital signs of patients.

[0104] In this application, multiple Internet of Things devices can form an Internet of Things device cluster, and subsequently, Internet of Things devices in the Internet of Things device cluster can be updated according to actual situations. For example, adding Internet of Things devices, replacing Internet of Things devices, or reducing Internet of Things devices, etc.

[0105] The time periods in this application can be divided by natural days. One natural day is one time period, or two natural days are one time period, etc. The time period where the current moment is located is the current time period, and the future time period is after the current time period.

[0106] The future time period can be a manually specified time period, etc.

[0107] In this application, the estimated number of Internet of Things devices in the Internet of Things device cluster in a future time period can be estimated based on a device growth model. Specifically, the future time period is input into a pre-fitted device growth model so that the device growth model obtains the estimated number of Internet of Things devices in the Internet of Things device cluster in the future time period and outputs the estimated number of Internet of Things devices in the Internet of Things device cluster in the future time period; the electronic device can obtain the estimated number of Internet of Things devices in the Internet of Things device cluster in the future time period output by the device growth model.

[0108] Among them, the device growth model is obtained by fitting through a loss function based on a historical data set, and the historical data set includes each historical time period and the historical number of Internet of Things devices in the Internet of Things device cluster at each historical time period.

[0109] The data fitting method can adopt the existing data fitting methods, and this application does not limit this. The loss function can include loss functions such as mean squared error, and this application does not limit this.

[0110] Among them, estimating the estimated number of Internet of Things devices in the Internet of Things device cluster in a future time period based on the device growth model includes:

[0111] The estimated number of Internet of Things devices in the Internet of Things device cluster in a future time period can be estimated based on the following formula.

[0112]

[0113] Among them, the above formula is the device growth model, and N t is the estimated number of Internet of Things devices in the Internet of Things device cluster within a future time period. Both m, q, and p are parameters obtained by fitting in the device growth model, and t is the second sum value between the numerical value 1 and the number of intervals; the number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period.

[0114] Further, when the current moment reaches the future time period, obtain the actual number of Internet of Things devices in the Internet of Things device cluster within the future time period.

[0115] When the actual number is different from the estimated number, optimize the device growth model according to the future time period, the estimated number, and the actual number through a loss function.

[0116] Alternatively, when the actual number is the same as the estimated number, there is no need to optimize the device growth model.

[0117] In step S102, obtain the online rate of the Internet of Things devices in the Internet of Things device cluster within the future time period.

[0118] In this application, the online rate of the Internet of Things devices in the Internet of Things device cluster can be an empirical value, which is related to the expected lifespan of each Internet of Things device in the Internet of Things device cluster. The starting moments when each Internet of Things device starts working in the Internet of Things device cluster are different, and the expected lifespans (the duration that can work continuously) of each Internet of Things device in the Internet of Things device cluster are different. Therefore, the ending moments when each Internet of Things device ends working in the Internet of Things device cluster are different. Thus, in any time period, some Internet of Things devices in the Internet of Things device cluster may have stopped working.

[0119] Among them, the ratio between the number of still working Internet of Things devices in the Internet of Things device cluster within the future time period and the total number of Internet of Things devices in the Internet of Things device cluster within the future time period can be regarded as the online rate of the Internet of Things devices in the Internet of Things device cluster within the future time period.

[0120] In step S103, obtain the data revenue of a single Internet of Things device in the Internet of Things device cluster within the future time period.

[0121] In an embodiment of this application, the data revenue of a single Internet of Things device within a time period can be approximately regarded as a constant (obtained according to historical statistical analysis).

[0122] Alternatively, in another embodiment of the present application, or, in advance, a revenue model can also be fitted according to the historical data revenue of a single Internet of Things device in different historical time periods. The data fitting method can refer to the existing methods, and the present application does not limit this. Moreover, the structure of the revenue model can be an existing model, and the present application does not limit the parameters in the revenue model and the calculation method between the parameters.

[0123] In this way, the data revenue of a single Internet of Things device in the Internet of Things device cluster in a future time period can be obtained based on the revenue model. Specifically, the future time period is input into the pre-fitted revenue model, so that the revenue model obtains the data revenue of a single Internet of Things device in the Internet of Things device cluster in the future time period, and outputs the data revenue of a single Internet of Things device in the Internet of Things device cluster in the future time period; the electronic device can obtain the data revenue of a single Internet of Things device in the Internet of Things device cluster in the future time period output by the revenue model.

[0124] In step S104, obtain the device cost of a single Internet of Things device in the Internet of Things device cluster in a future time period.

[0125] In one embodiment of the present application, the device cost of a single Internet of Things device in a time period can be approximately regarded as a constant (obtained according to historical statistical analysis).

[0126] Alternatively, in another embodiment of the present application, or, in advance, a cost model can also be fitted according to the historical device costs of a single Internet of Things device in different historical time periods. The data fitting method can refer to the existing methods, and the present application does not limit this. Moreover, the structure of the cost model can be an existing model, and the present application does not limit the parameters in the cost model and the calculation method between the parameters.

[0127] In this way, the device cost of a single Internet of Things device in the Internet of Things device cluster in a future time period can be obtained based on the cost model. Specifically, the future time period is input into the pre-fitted cost model, so that the cost model obtains the device cost of a single Internet of Things device in the Internet of Things device cluster in the future time period, and outputs the device cost of a single Internet of Things device in the Internet of Things device cluster in the future time period; the electronic device can obtain the device cost of a single Internet of Things device in the Internet of Things device cluster in the future time period output by the cost model.

[0128] It should be noted that the present application does not limit the execution order among step S101, step S102, step S103, and step S104.

[0129] In step S105, estimate the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period based on at least the estimated quantity, the online rate, the data revenue, and the device cost.

[0130] In an embodiment of the present application, the difference between the data revenue and the device cost may be calculated, the first product between the difference and the estimated quantity may be calculated, the second product between the product and the online rate may be calculated, and then the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period is obtained based on at least the second product.

[0131] Wherein, when obtaining the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period based on at least the second product, the number of intervals of the time period between the current time period where the current moment is located and the future time period may be obtained. For example, assuming that the time period in the present application is distinguished by natural days, that is, a time period is a natural day, assuming that the current time period where the current location is located is January 1st and the future time period is January 5th, then there are 4 natural days between January 1st and January 5th. Calculate the first sum value between the numerical value 1 and the capital interest rate (such as the bank deposit interest rate, etc., which may be the current interest rate or the fixed interest rate, etc.). Calculate the second sum value between the numerical value 1 and the number of intervals. Calculate the power of the second sum value of the first sum value. Calculate the ratio between the second product and the power of the second sum value of the first sum value, and obtain the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period according to the ratio. For example, determine the ratio as the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period.

[0132] In an example, the value evaluation model may be reflected by the following formula:

[0133]

[0134] Wherein, V is the value of the data of the Internet of Things devices in the Internet of Things device cluster in a future time period, R t is the data revenue of a single Internet of Things device in the Internet of Things device cluster in a future time period, C t is the device cost of a single Internet of Things device in the Internet of Things device cluster in a future time period, N t is the estimated quantity of the Internet of Things devices in the Internet of Things device cluster in a future time period, d is the capital interest rate, t is the second sum value between the numerical value 1 and the number of intervals; the number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period, r t is the online rate of the Internet of Things devices in the Internet of Things device cluster in a future time period.

[0135] In this application, estimate the estimated number of Internet of Things (IoT) devices in the IoT device cluster within a future time period. Obtain the online rate of the IoT devices in the IoT device cluster within the future time period. Obtain the data revenue of a single IoT device in the IoT device cluster within the future time period. Obtain the device cost of a single IoT device in the IoT device cluster within the future time period. Estimate the value of the data of the IoT devices in the IoT device cluster within the future time period based at least on the estimated number, the online rate, the data revenue, and the device cost.

[0136] The method for evaluating the value of the data of the IoT devices in this application is applicable to the field of the Internet of Things and related fields. The value of the data of the IoT devices evaluated based on the method for evaluating the value of the data of the IoT devices in this application is objective and accurate, which helps to standardize the standards and guidelines for the industry of evaluating the value of the data of the IoT devices, and further helps to standardize the standards and guidelines for the industry of evaluating the value of data assets.

[0137] Secondly, this application uses a growth evaluation model that combines a device growth model and a value evaluation model to evaluate the value of the data of the IoT devices, fully considering the particularity of the data of the IoT devices, so as to evaluate the value of the data of the IoT devices more comprehensively and accurately, enhance the objectivity of the evaluation results, and provide new ideas and methods for the theory of evaluating the value of the data of the IoT devices.

[0138] In another embodiment of this application, there are multiple future time periods, and the multiple future time periods can be consecutive time periods in sequence. Then, the value of the data of the IoT devices in the IoT device cluster within each future time period can be estimated respectively in the above manner, and then the values of the data of the IoT devices in the IoT device cluster within each future time period are summed to obtain the total value of the data of the IoT devices in the IoT device cluster within the multiple future time periods.

[0139] Furthermore, in another embodiment of this application, after obtaining the value of the data of the IoT devices in the IoT device cluster within the future time period, it is next necessary to strip out the value of the combined intangible assets therefrom.

[0140] For example, the value of the data of the IoT devices in the IoT device cluster within the future time period is: the sum of the tangible assets of the IoT devices in the IoT device cluster and the value of the combined intangible assets of the IoT devices in the IoT device cluster. The tangible assets account for a certain proportion in the overall value of the enterprise, and this proportion can be determined by financial experts according to the actual situation of the enterprise, and is often relatively accurate. The product of this proportion and the value of the data of the IoT devices in the IoT device cluster within the future time period can be calculated to obtain the tangible assets of the IoT devices in the IoT device cluster.

[0141] After that, further, the difference between the value of the data of the IoT devices in the IoT device cluster and the tangible assets of the IoT devices in the IoT device cluster within a future time period can be calculated, and the combined intangible asset value of the IoT devices in the IoT device cluster can be obtained.

[0142] Further, in another embodiment of the present application, the asset discount rate of the value of the data of the IoT devices in the IoT device cluster within a future time period can be measured by using the analytic hierarchy process.

[0143] For example, first, a structural model is constructed according to the combined intangible asset value of the IoT devices in the IoT device cluster calculated as described above. The structural model includes: decision-making purpose - goal layer, judgment criteria - criterion layer, and solution - solution layer.

[0144] Secondly, a questionnaire is designed according to the constructed structural model, and experts in the field of asset value assessment are invited to assign relative weights to the factors. The introduction of expert scoring and sensitivity analysis reduces subjectivity in the process and makes the results more objective.

[0145] In addition, the questionnaire results are integrated, a matrix is constructed, and a consistency test is carried out. After the test passes, the weights of the factor of the value of the data of the IoT devices in the IoT device cluster within a future time period in each criterion layer are calculated respectively, and each is multiplied by the proportion of each factor in the criterion layer in the combined intangible assets and then added together, and the asset discount rate of the value of the data of the IoT devices in the IoT device cluster within a future time period can be obtained.

[0146] In the present application, by combining the device growth model and the value assessment model, a growth assessment model is obtained, and the construction of the growth assessment model and the determination of the parameters in the growth assessment model are clarified, and the calculation method in the growth assessment model is optimized. For example, the exponential smoothing method is used to predict the data contribution of future active IoT devices, and the average total asset growth rate is used as a reference for the perpetual growth rate, etc., which improves the accuracy and scientific nature of the growth assessment model, has a wide range of application scenarios and practical value, further improves the evaluation method of the value of the data of IoT devices, provides a reference example for evaluation institutions and enterprises in the industry, improves the comparability and credibility of evaluation results, and promotes the healthy development of the data asset value assessment industry.

[0147] By predicting the number of future active IoT devices and the value contribution of each active IoT device, valuable information for future development is provided for asset assessment, and the value of data assets is more comprehensively reflected.

[0148] It should be noted that for method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily essential for this application.

[0149] Referring to Figure 2 , a value evaluation device for data of an Internet of Things device according to this application is shown. The device includes:

[0150] A first estimation module 11, configured to estimate the estimated number of Internet of Things devices in an Internet of Things device cluster within a future time period;

[0151] A first acquisition module 12, configured to acquire the online rate of Internet of Things devices in an Internet of Things device cluster within a future time period;

[0152] A second acquisition module 13, configured to acquire the data revenue of a single Internet of Things device in an Internet of Things device cluster within a future time period;

[0153] A third acquisition module 14, configured to acquire the device cost of a single Internet of Things device in an Internet of Things device cluster within a future time period;

[0154] A second estimation module 15, configured to estimate the value of data of Internet of Things devices in an Internet of Things device cluster within a future time period at least based on the estimated number, the online rate, the data revenue, and the device cost.

[0155] In an optional implementation manner, the second estimation module includes:

[0156] A first calculation unit, configured to calculate the difference between the data revenue and the device cost;

[0157] A second calculation unit, configured to calculate the first product of the difference and the estimated number;

[0158] A third calculation unit, configured to calculate the second product of the product and the online rate;

[0159] A first acquisition unit, configured to acquire the value of data of Internet of Things devices in an Internet of Things device cluster within a future time period at least based on the second product.

[0160] In an optional implementation manner, the acquisition unit includes:

[0161] A first acquisition subunit, configured to acquire the number of intervals of the interval period between the current time period where the current moment is located and the future time period;

[0162] A first calculation subunit, configured to calculate a first sum value between numerical value 1 and a capital interest rate;

[0163] A second calculation subunit, configured to calculate a second sum value between numerical value 1 and the number of intervals;

[0164] A third calculation subunit, configured to calculate the power of the second sum value of the first sum value;

[0165] A fourth calculation subunit, configured to calculate a ratio between the second product and the power of the second sum value of the first sum value;

[0166] A second acquisition subunit, configured to obtain the value of data of Internet of Things devices in an Internet of Things device cluster within a future time period according to the ratio.

[0167] In an optional implementation manner, the first estimation module includes:

[0168] An estimation unit, configured to estimate the estimated number of Internet of Things devices in an Internet of Things device cluster within a future time period based on a device growth model;

[0169] Wherein, the device growth model is obtained by fitting through a loss function according to a historical data set, and the historical data set includes each historical time period and the historical number of Internet of Things devices in the Internet of Things device cluster at each historical time period.

[0170] In an optional implementation manner, the estimation unit is specifically configured to:

[0171] Estimate the estimated number of Internet of Things devices in an Internet of Things device cluster within a future time period based on the following formula;

[0172]

[0173] Wherein, the above formula is the device growth model, N t Is the estimated number of Internet of Things devices in the Internet of Things device cluster within a future time period, m, q, and p are all parameters obtained by fitting in the device growth model, and t is the second sum value between numerical value 1 and the number of intervals; the number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period.

[0174] In an optional implementation manner, the first estimation module further includes:

[0175] A second acquisition unit, configured to obtain the actual number of Internet of Things devices in the Internet of Things device cluster within a future time period when the current moment reaches the future time period;

[0176] An optimization unit is used to optimize the device growth model according to the future time period, the estimated quantity, and the actual quantity by using a loss function when the actual quantity is different from the estimated quantity.

[0177] In this application, estimate the estimated quantity of Internet of Things devices in the Internet of Things device cluster within a future time period. Obtain the online rate of the Internet of Things devices in the Internet of Things device cluster within the future time period. Obtain the data revenue of a single Internet of Things device in the Internet of Things device cluster within the future time period. Obtain the device cost of a single Internet of Things device in the Internet of Things device cluster within the future time period. Estimate the value of the data of the Internet of Things devices in the Internet of Things device cluster within the future time period based on at least the estimated quantity, the online rate, the data revenue, and the device cost.

[0178] The method for evaluating the value of the data of the Internet of Things devices in this application is applicable to the Internet of Things field and related fields of the Internet of Things. The value of the data of the Internet of Things devices evaluated based on the method for evaluating the value of the data of the Internet of Things devices in this application is objective and accurate, which helps to standardize the standards and guidelines of the industry for evaluating the value of the data of the Internet of Things devices, and further helps to standardize the standards and guidelines of the industry for evaluating the value of data assets.

[0179] Secondly, this application uses a growth evaluation model that combines a device growth model and a value evaluation model to evaluate the value of the data of the Internet of Things devices, fully considering the particularity of the data of the Internet of Things devices, so as to more comprehensively and accurately evaluate the value of the data of the Internet of Things devices, enhance the objectivity of the evaluation results, and provide new ideas and methods for the theory of evaluating the value of the data of the Internet of Things devices.

[0180] Optionally, an embodiment of this application also provides an electronic device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements each process of the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0181] An embodiment of this application also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements each process of the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium is, for example, a read-only memory (Read-Only Memory, abbreviated as ROM), a random access memory (Random Access Memory, abbreviated as RAM), a magnetic disk, or an optical disc, etc.

[0182] Figure 3FIG. 0 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0183] Referring Figure 3 , the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0184] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above methods. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0185] The memory 804 is configured to store various types of data to support the operation of the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0186] The power component 806 provides power to various components of the electronic device 800. The power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0187] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of a touch or swipe action but also monitor the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0188] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0189] The I / O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power button, and a lock button.

[0190] The sensor component 814 includes one or more sensors for providing an assessment of the status of various aspects of the electronic device 800. For example, the sensor component 814 can monitor the on / off state of the device 800, the relative positioning of components, such as the display and the keypad of the electronic device 800. The sensor component 814 can also monitor a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor component 814 can include a proximity sensor configured to monitor the presence of nearby objects without any physical contact. The sensor component 814 can also include a light sensor, such as a CMOS or a CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0191] The communication component 816 is configured to facilitate communication, either wired or wirelessly, between the electronic device 800 and other devices. The electronic device 800 may access a communication standard-based wireless network, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0192] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described method.

[0193] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 804 including instructions, is also provided. The above instructions may be executed by the processor 820 of the electronic device 800 to complete the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, magnetic tape, floppy disk, and optical data storage device, among others.

[0194] Figure 4 FIG. 10 is a block diagram of an electronic device 1900 shown in the present application. For example, the electronic device 1900 may be provided as a server.

[0195] Referring to Figure 4 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by the memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above-described method.

[0196] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0197] It should be noted that, in this document, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising that element.

[0198] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0199] The embodiments of the present application have been described above with reference to the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

[0200] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in connection with the embodiments disclosed in the embodiments of the present application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0201] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0202] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0203] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0204] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0205] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0206] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the value of data of an Internet of Things device, characterized in that, The method includes: estimating the estimated number of Internet of Things (IoT) devices in an IoT device cluster within a future time period; obtaining the online rate of the IoT devices in the IoT device cluster within the future time period; obtaining the data revenue of a single IoT device in the IoT device cluster within the future time period; obtaining the device cost of a single IoT device in the IoT device cluster within the future time period; estimating the value of the data of the IoT devices in the IoT device cluster within the future time period based on at least the estimated number, the online rate, the data revenue, and the device cost.

2. The method according to claim 1, wherein The estimating the value of the data of the IoT devices in the IoT device cluster within the future time period based on at least the estimated number, the online rate, the data revenue, and the device cost includes: calculating the difference between the data revenue and the device cost; calculating a first product of the difference and the estimated number; calculating a second product of the product and the online rate; estimating the value of the data of the IoT devices in the IoT device cluster within the future time period based on at least the second product.

3. The method according to claim 2, wherein The estimating the value of the data of the IoT devices in the IoT device cluster within the future time period based on at least the second product includes: obtaining the number of intervals of the time interval between the current time period in which the current moment is located and the future time period; calculating a first sum value between the value 1 and the capital interest rate; calculating a second sum value between the value 1 and the number of intervals; calculating the power of the second sum value of the first sum value; calculating the ratio between the second product and the power of the second sum value of the first sum value; obtaining the value of the data of the IoT devices in the IoT device cluster within the future time period according to the ratio.

4. The method according to claim 1, wherein The estimating the estimated number of IoT devices in the IoT device cluster within the future time period includes: estimating the estimated number of IoT devices in the IoT device cluster within the future time period based on a device growth model; wherein the device growth model is obtained by fitting a loss function based on a historical data set, and the historical data set includes each historical time period and the historical number of IoT devices in the IoT device cluster at each historical time period.

5. The method according to claim 4, wherein The estimating the estimated number of IoT devices in the IoT device cluster within the future time period based on the device growth model includes: estimating the estimated number of IoT devices in the IoT device cluster within the future time period based on the following formula; Among them, the above formula is the device growth model, N t is the estimated number of IoT devices in the IoT device cluster within the future time period. Both m, q, and p are parameters obtained by fitting in the device growth model. t is the second sum value between the numerical value 1 and the number of intervals. The number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period.

6. The method according to claim 4 or 5, characterized in that, The method further includes: when the current moment reaches the future time period, obtaining the actual number of IoT devices in the IoT device cluster within the future time period; when the actual number is different from the estimated number, optimizing the device growth model according to the future time period, the estimated number, and the actual number through a loss function.

7. An apparatus for evaluating the value of data of an Internet of Things device, characterized in that The apparatus includes: a first estimation module, configured to estimate the estimated number of IoT devices in the IoT device cluster within the future time period; a first acquisition module, configured to obtain the online rate of the IoT devices in the IoT device cluster within the future time period; A second acquisition module, configured to acquire the data revenue of a single Internet of Things device in the Internet of Things device cluster within a future time period; A third acquisition module, configured to acquire the device cost of a single Internet of Things device in the Internet of Things device cluster within a future time period; A second estimation module, configured to estimate the value of the data of the Internet of Things devices in the Internet of Things device cluster within a future time period at least based on the estimated quantity, the online rate, the data revenue, and the device cost.

8. The device according to claim 7, characterized in that, The second estimation module includes: A first calculation unit, configured to calculate the difference between the data revenue and the device cost; A second calculation unit, configured to calculate a first product of the difference and the estimated quantity; A third calculation unit, configured to calculate a second product of the product and the online rate; A first acquisition unit, configured to acquire the value of the data of the Internet of Things devices in the Internet of Things device cluster within a future time period at least based on the second product.

9. The device according to claim 8, characterized in that, The acquisition unit includes: A first acquisition subunit, configured to acquire the number of intervals of the time period between the current time period where the current moment is located and the future time period; A first calculation subunit, configured to calculate a first sum value between the numerical value 1 and the capital interest rate; A second calculation subunit, configured to calculate a second sum value between the numerical value 1 and the number of intervals; A third calculation subunit, configured to calculate the power of the second sum value of the first sum value; A fourth calculation subunit, configured to calculate a ratio between the second product and the power of the second sum value of the first sum value; A second acquisition subunit, configured to acquire the value of the data of the Internet of Things devices in the Internet of Things device cluster within a future time period according to the ratio.

10. The device according to claim 7, wherein, The first estimation module includes: An estimation unit, configured to estimate the estimated quantity of the Internet of Things devices in the Internet of Things device cluster within a future time period based on a device growth model; Wherein, the device growth model is obtained by fitting through a loss function according to a historical data set, and the historical data set includes each historical time period and the historical quantity of the Internet of Things devices in the Internet of Things device cluster at each historical time period.

11. The device according to claim 10, characterized in that, The estimation unit is specifically configured to: Estimate the estimated quantity of the Internet of Things devices in the Internet of Things device cluster within a future time period based on the following formula; Among them, the above formula is the device growth model, and N t is the estimated number of Internet of Things devices in the Internet of Things device cluster in the future time period. m, q, and p are all parameters obtained by fitting in the device growth model. t is the second sum value between the value 1 and the number of intervals. The number of intervals is the number of intervals of the time period between the current time period where the current moment is located and the future time period.

12. The device according to claim 10 or 11, characterized in that, The first estimation module further includes: A second acquisition unit, configured to acquire the actual quantity of the Internet of Things devices in the Internet of Things device cluster within a future time period when the current moment reaches the future time period; An optimization unit, configured to optimize the device growth model through a loss function according to the future time period, the estimated quantity, and the actual quantity when the actual quantity is different from the estimated quantity.

13. An electronic device, characterized in that, Includes: A processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program, when executed by the processor, implements the method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the method according to any one of claims 1 to 6.

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