Method, apparatus, device and computer readable storage medium for determining redundant devices

Abstract

The application discloses a method, device and equipment for determining redundant devices and a computer readable storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring historical temperature data detected by a plurality of temperature detection devices; determining a plurality of temperature vectors based on the historical temperature data, wherein the historical temperature data detected by any temperature detection device corresponds to a temperature vector; clustering the plurality of temperature vectors, and the clustering result comprises at least one clustering cluster; and in response to the number of temperature vectors in any clustering cluster being higher than a number threshold, determining a redundant device from the temperature detection devices corresponding to the temperature vectors in any clustering cluster. The application clusters the temperature vectors corresponding to the plurality of temperature detection devices, so that the plurality of temperature detection devices with similar historical temperature data can be found, the redundant device can be determined from the devices with similar historical temperature data, and the accuracy of determining the redundant device is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computer, and particularly relate to a method, device and computer readable storage medium for determining redundant devices. BACKGROUND

[0002] In the logistics field, temperature monitoring is needed for goods such as cold-chain fresh food. In order to ensure the accuracy and reliability of temperature monitoring, RDC (Regional Distribution Center) needs to densely deploy temperature detection devices. In the deployment process, the problem of redundant temperature detection devices may occur, thereby increasing the cost. Therefore, it is necessary to evaluate whether the number of temperature detection devices and the deployment position are reasonable, determine redundant devices, and perform device deduplication processing.

[0003] In the related art, first temperature data of a target position can be obtained based on a temperature detection device, and second temperature data of the target position can be calculated through a thermal model, and then the first temperature data and the second temperature data are compared. If the difference between the two temperature data is large, it is determined that the temperature detection device is faulty, and the temperature detection device can be determined as a redundant device.

[0004] The method provided by the related art only determines the faulty temperature detection device as a redundant device, and the effect of determining the redundant device is poor and the accuracy is low. SUMMARY

[0005] Embodiments of the present application provide a method, device and computer readable storage medium for determining redundant devices, which can be used to solve the problems in the related art. The technical solutions are as follows:

[0006] In one aspect, the present application provides a method for determining redundant devices, the method comprising:

[0007] obtaining historical temperature data detected by a plurality of temperature detection devices;

[0008] determining a plurality of temperature vectors based on the historical temperature data detected by the plurality of temperature detection devices, wherein the historical temperature data detected by any temperature detection device corresponds to a temperature vector;

[0009] clustering the plurality of temperature vectors to obtain a clustering result, wherein the clustering result includes at least one clustering cluster, and any clustering cluster in the at least one clustering cluster includes at least one temperature vector;

[0010] in response to the number of temperature vectors in the any clustering cluster being higher than a number threshold, determining a redundant device from the temperature detection device corresponding to the temperature vector in the any clustering cluster.

[0011] In a possible implementation, the clustering of the plurality of temperature vectors comprises:

[0012] For any two temperature vectors in the plurality of temperature vectors, calculating an average temperature difference and a maximum temperature difference of historical temperature data corresponding to the any two temperature vectors.

[0013] In response to the average temperature difference satisfying an average temperature difference condition and the maximum temperature difference satisfying a maximum temperature difference condition, the any two temperature vectors belong to a same cluster.

[0014] In a possible implementation, after the determining of the redundant temperature detection device from the temperature detection devices corresponding to the temperature vectors in the any cluster, the method further comprises:

[0015] performing quality detection on a determination result of the redundant temperature detection device by using a quality detection index to obtain a quality detection result, the quality detection index comprising at least one of an adjusted rand index (ARI) and a silhouette coefficient, and the quality detection result being used to indicate accuracy of the determined redundant temperature detection device.

[0016] In a possible implementation, the determining of the plurality of temperature vectors based on the historical temperature data detected by the plurality of temperature detection devices comprises:

[0017] performing grouping processing on the historical temperature data detected by the plurality of temperature detection devices to obtain a processing result, and determining the plurality of temperature vectors based on the processing result.

[0018] In a possible implementation, the grouping processing on the historical temperature data detected by the plurality of temperature detection devices to obtain a processing result comprises:

[0019] grouping the historical temperature data detected by the plurality of temperature detection devices according to temperature detection devices to obtain historical temperature data of each temperature detection device;

[0020] respectively dividing the historical temperature data of each temperature detection device according to a reference granularity to obtain a plurality of groups of historical temperature data;

[0021] respectively taking an average value of each group of the divided historical temperature data to obtain average historical temperature data of each temperature detection device, and the average historical temperature data of any temperature detection device corresponding to a processing result.

[0022] In a possible implementation, after the grouping processing on the historical temperature data detected by the plurality of temperature detection devices to obtain a processing result, the method further comprises:

[0023] in response to the fact that there is missing data in the processing result, complementing the processing result;

[0024] The determining of the plurality of temperature vectors based on the processing result comprises:

[0025] The determining of the plurality of temperature vectors based on the complemented processing result.

[0026] In a possible implementation, after the grouping processing of the historical temperature data detected by the plurality of temperature detection devices to obtain the processing result, the method further comprises:

[0027] in response to the fact that there is repeated data corresponding to the same temperature detection device and the same time in the processing result, deleting the repeated data;

[0028] The determining of the plurality of temperature vectors based on the processing result comprises:

[0029] The determining of the plurality of temperature vectors based on the processing result from which the repeated data is deleted.

[0030] In another aspect, a device for determining redundant devices is provided, and the device comprises:

[0031] an obtaining module configured to obtain historical temperature data detected by a plurality of temperature detection devices;

[0032] a first determining module configured to determine a plurality of temperature vectors based on the historical temperature data detected by the plurality of temperature detection devices, wherein the historical temperature data detected by any temperature detection device corresponds to a temperature vector;

[0033] a clustering module configured to cluster the plurality of temperature vectors to obtain a clustering result, wherein the clustering result comprises at least one clustering cluster, and any clustering cluster in the at least one clustering cluster comprises at least one temperature vector;

[0034] a second determining module configured to, in response to the fact that the number of temperature vectors in any clustering cluster is higher than a number threshold, determine a redundant device from temperature detection devices corresponding to the temperature vectors in the any clustering cluster.

[0035] In a possible implementation, the clustering module is configured to, for any two temperature vectors in the plurality of temperature vectors, calculate an average temperature difference and a maximum temperature difference of historical temperature data corresponding to the any two temperature vectors; and in response to the fact that the average temperature difference satisfies an average temperature difference condition and the maximum temperature difference satisfies a maximum temperature difference condition, the any two temperature vectors belong to a same clustering cluster.

[0036] In a possible implementation, the device further comprises:

[0037] a quality detection module configured to perform quality detection on the determination result of the redundant device by a quality detection index to obtain a quality detection result, the quality detection index comprising at least one of ARI and a profile coefficient, and the quality detection result being used to indicate accuracy of the determined redundant device.

[0038] In a possible implementation, the first determination module is configured to perform grouping processing on the historical temperature data detected by the plurality of temperature detection devices to obtain a processing result, and determine the plurality of temperature vectors based on the processing result.

[0039] In a possible implementation, the first determination module is configured to group the historical temperature data detected by the plurality of temperature detection devices according to temperature detection devices to obtain historical temperature data of each temperature detection device, divide the historical temperature data of each temperature detection device according to a reference granularity to obtain a plurality of groups of historical temperature data, and take an average value of each group of historical temperature data to obtain average historical temperature data of each temperature detection device, wherein the average historical temperature data of any temperature detection device corresponds to a processing result.

[0040] In a possible implementation, the first determination module is further configured to, in response to missing data in the processing result, complete the processing result.

[0041] The first determination module is configured to determine the plurality of temperature vectors based on the completed processing result.

[0042] In a possible implementation, the first determination module is further configured to, in response to repeated data corresponding to the same temperature detection device and the same time in the processing result, delete the repeated data.

[0043] The first determination module is configured to determine the plurality of temperature vectors based on the processing result from which the repeated data is deleted.

[0044] On the other hand, a computer device is provided, which includes a processor and a memory, and the memory stores at least one computer program, the at least one computer program is loaded and executed by the processor, so that the computer device implements any of the above methods for determining a redundant device.

[0045] On the other hand, a computer readable storage medium is also provided, which stores at least one computer program, the at least one computer program is loaded and executed by the processor, so that the computer implements any of the above methods for determining a redundant device.

[0046] In another aspect, a computer program product or computer program is also provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method for determining a redundant device according to any of the above.

[0047] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects:

[0048] The technical solutions provided by the present application cluster the temperature vectors corresponding to the historical temperature data of the plurality of temperature detection devices to obtain at least one cluster. Therefore, the embodiments of the present application can find a plurality of temperature detection devices with similar measured historical temperature data, so that the redundant device can be determined from the devices with similar measured historical temperature data, and the accuracy of determining the redundant device is improved. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0050] Figure 1 is a schematic diagram of an implementation environment provided by the embodiments of the present application;

[0051] Figure 2 is a flowchart of a method for determining a redundant device provided by the embodiments of the present application;

[0052] Figure 3 is a schematic diagram of a clustering effect provided by the embodiments of the present application;

[0053] Figure 4 is a schematic diagram of a clustering result provided by the embodiments of the present application;

[0054] Figure 5 is a flowchart of another method for determining a redundant device provided by the embodiments of the present application;

[0055] Figure 6 is a schematic diagram of a device for determining a redundant device provided by the embodiments of the present application;

[0056] Figure 7 is a schematic diagram of a structure of a computer device provided by the embodiments of the present application;

[0057] Figure 8 is a schematic diagram of another structure of a computer device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0058] In order to make the objects, technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application with reference to the drawings.

[0059] It should be noted that the terms "first", "second" and the like in the description and claims of the present application are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0060] The embodiments of the present application provide a method for determining redundant devices, please refer to Figure 1 which shows a schematic diagram of the implementation environment of the method provided by the embodiments of the present application. The implementation environment can include: a terminal 11 and a server 12, a plurality of temperature detection devices 13.

[0061] Among them, the plurality of temperature detection devices 13 can detect temperature to obtain historical temperature data. The terminal 11 can obtain the historical temperature data detected by the plurality of temperature detection devices 13, determine the redundant device in the plurality of temperature detection devices 13 by applying the method provided by the embodiments of the present application, and obtain the determination result of the redundant device. The terminal 11 can display the determination result of the redundant device so that the interactive object removes the redundant device. The terminal 11 can send the determination result of the redundant device to the server 12. The server 12 can store the determination result of the redundant device. Alternatively, the terminal 11 can obtain the historical temperature data detected by the plurality of temperature detection devices 13, determine the redundant device by applying the method provided by the embodiments of the present application. The terminal 11 can display the determination result of the redundant device so that the interactive object removes the redundant device. And the terminal 11 can store the determination result of the redundant device.

[0062] Alternatively, the server 12 can obtain historical temperature data detected by the plurality of temperature detection devices 13, determine the redundant device by applying the method provided in the embodiments of the present application, and obtain a determination result of the redundant device. The server 12 can send the determination result of the redundant device to the terminal 11. The terminal 11 can display the determination result of the redundant device, so that the interactive object removes the redundant device. The terminal 11 can store the determination result of the redundant device. Alternatively, the server 12 can obtain historical temperature data detected by the plurality of temperature detection devices 13, determine the redundant device by applying the method provided in the embodiments of the present application, and store the determination result of the redundant device. The server 12 can display the determination result of the redundant device through a display screen or the like, so that the interactive object removes the redundant device.

[0063] Optionally, the terminal 11 can be any electronic product that can interact with a user through one or more of a keyboard, a touchpad, a touch screen, a remote controller, voice interaction, a handwriting device, and the like, such as a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a wearable device, a PPC (Pocket PC), a tablet computer, a smart car, a smart television, a smart speaker, and the like. The server 12 can be a server or a server cluster composed of a plurality of servers, or a cloud computing service center. The terminal 11 and the server 12 establish a communication connection through a wired or wireless network.

[0064] Those skilled in the art should understand that the terminal 11 and the server 12 described above are only examples, and other existing or future terminal or server, such as those applicable to the present application, should also be included in the protection scope of the present application and be included herein by reference.

[0065] Based on the above Figure 1 environment, the embodiments of the present application provide a method for determining a redundant device. As shown in Figure 2 the embodiments of the present application provide a method, which can include the following steps 201 to 204.

[0066] In step 201, historical temperature data detected by a plurality of temperature detection devices is obtained.

[0067] The embodiments of the present application do not limit the application scenarios of the method for determining the redundant device, nor the plurality of temperature detection devices. For example, the application scenario of the method for determining the redundant device can be a logistics warehouse, and the plurality of temperature detection devices can be thermistors or resistance temperature detectors, etc. For another example, the application scenario of the method for determining the redundant device can be a greenhouse, and the plurality of temperature detection devices can be thermocouples or analog thermometers, etc.

[0068] Exemplarily, to determine the redundant device in the plurality of temperature detection devices, historical temperature data detected by the plurality of temperature detection devices can be acquired. For example, the plurality of temperature detection devices can be deployed in the application scenario of the method, and the plurality of temperature detection devices can detect the temperature of the corresponding position respectively to obtain the historical temperature data. Taking the case that the method is applied to a terminal as an example, the plurality of temperature detection devices can upload the temperature data to the terminal, and the terminal can acquire the historical temperature data detected by the plurality of temperature detection devices. Alternatively, the historical temperature data of the plurality of temperature detection devices can be input into the terminal by manual input, and the terminal can acquire the historical temperature data detected by the plurality of temperature detection devices.

[0069] The embodiments of the present application do not limit the quantity and format of the historical temperature data. For example, in one application scenario, 200 temperature detection devices have performed temperature detection for 5 days, and the temperature detection is performed once every 1 hour in 5 days, so the quantity of the historical temperature data is 24000, and the quantity of the historical temperature data detected by any temperature detection device is 120. For example, the result of one temperature detection of any temperature detection device is -3 degrees Celsius, and the historical temperature data detected by the temperature detection device can be -3. For another example, the result of one temperature detection of any temperature detection device is 12.5 degrees Celsius, and the historical temperature data detected by the temperature detection device can be 12.5.

[0070] In step 202, a plurality of temperature vectors are determined based on the historical temperature data detected by the plurality of temperature detection devices, and the historical temperature data detected by any temperature detection device corresponds to one temperature vector.

[0071] In an exemplary embodiment, determining the plurality of temperature vectors based on the historical temperature data detected by the plurality of temperature detection devices includes: performing grouping processing on the historical temperature data detected by the plurality of temperature detection devices to obtain a processing result, and determining the plurality of temperature vectors based on the processing result.

[0072] In a possible implementation, the historical temperature data detected by the temperature detection devices can be grouped according to IDs (Identity Documents) of the temperature detection devices, and the historical temperature data detected by the temperature detection devices with the same ID is a group. Alternatively, the historical temperature data detected by the temperature detection devices can be grouped according to locations of the temperature detection devices, and the historical temperature data detected by the temperature detection devices at the same location is a group.

[0073] Optionally, the historical temperature data detected by the plurality of temperature detection devices is grouped to obtain processing results, including: grouping the historical temperature data detected by the plurality of temperature detection devices according to the temperature detection devices to obtain historical temperature data of each temperature detection device; dividing the historical temperature data of each temperature detection device according to a reference granularity to obtain a plurality of groups of historical temperature data; and averaging each group of historical temperature data to obtain average historical temperature data of each temperature detection device, wherein the average historical temperature data of any temperature detection device corresponds to a processing result.

[0074] The reference granularity is not limited in the embodiments of the present application, and can be limited based on experience or application scenarios. For example, if the temperature detection devices detect temperature every 5 minutes, the reference granularity can be half an hour or 1 hour. For example, if the reference granularity is 1 hour, the historical temperature data corresponding to each temperature detection device can be divided into a plurality of groups according to the reference granularity of 1 hour. For example, in an application scenario, six temperature detection devices detect temperature for three days, and the temperature is detected every 5 minutes in three days. The historical temperature data can be grouped according to the temperature detection devices, and the historical temperature data corresponding to the six temperature detection devices can be divided according to the reference granularity of 1 hour. Therefore, the historical temperature data corresponding to each temperature detection device can be divided into 72 groups, and each group includes 12 historical temperature data.

[0075] After obtaining the plurality of groups of historical temperature data, the average of each group of historical temperature data can be obtained to obtain processing results. For example, in the above example, the six temperature detection devices detect temperature for three days, and the temperature is detected every 5 minutes in three days. The average of the 12 historical temperature data in each group can be obtained, and thus the historical temperature data corresponding to each temperature detection device is changed from 72 groups to 72. Therefore, six processing results can be obtained in this embodiment, and each processing result includes 72 historical temperature data, and each processing result corresponds to a temperature detection device.

[0076] After obtaining the processing result, a plurality of temperature vectors can be determined based on the processing result. Illustratively, the data in the processing result can be arranged in a reference order to obtain the plurality of temperature vectors, and the form of the temperature vectors is not limited in the embodiments of the present application. In an exemplary embodiment, the processing result includes historical temperature data of each hour in n days (e.g., n is an integer greater than 0) detected by the temperature detection device. Therefore, the temperature vector can be a 24-row and n-column vector, where each row corresponds to an hour and each column corresponds to a day. Alternatively, the temperature vector can also be an n-row and 24-column vector, where each row corresponds to a day and each column corresponds to an hour.

[0077] Optionally, after grouping the historical temperature data detected by the plurality of temperature detection devices to obtain the processing result, the method further includes: in response to the existence of missing data in the processing result, completing the processing result; and determining the plurality of temperature vectors based on the processing result, including: determining the plurality of temperature vectors based on the completed processing result.

[0078] In a possible implementation, one processing result can be historical temperature data of each hour on December 1, which can be a1=10, a2=12, a3=11, a4=10, a5=11, a6=11, a7=11, a8=10, a9=13, a 10 =12, a 11 =10, a 12 =11. Wherein a1 corresponds to historical temperature data from 0:00 to 1:00 on December 1, a2 corresponds to historical temperature data from 1:00 to 2:00 on December 1, a3 to a 12 corresponds to historical temperature data from 23:00 to 24:00 on December 1. The above rules are followed to obtain historical temperature data of each hour. 10 =12, a 11 =10, a 12 =11, the processing result is missing historical temperature data from 1:00 to 2:00 on December 1, i.e., a2 is missing. At this time, the processing result can be completed.

[0079] In a possible implementation, the processing result can be completed by a Forward Fill method. For example, in the above processing result, a1=10, a3=11, a4=10, a5=11, a6=11, a7=11, a8=10, a9=13, a 10 =12, a 11 =10, a 12= 11, the data of a3 = 11 is copied to a2.

[0080] Optionally, after grouping the historical temperature data detected by the plurality of temperature detection devices and obtaining the processing result, the method further comprises: in response to the repeated data corresponding to the same temperature detection device and the same time existing in the data in the processing result, deleting the repeated data; determining the plurality of temperature vectors based on the processing result after deleting the repeated data.

[0081] In a possible implementation, still taking the above processing result as an example, the processing result is a1 = 10, a2 = 12, a3 = 11, a4 = 10, a5 = 11, a6 = 11, a7 = 11, a8 = 10, a9 = 13, a 10 = 12, a 11 = 10, a 12 = 11. When the obtained processing result is a1 = 10, a2 = 12, a2 = 12, a3 = 11, a4 = 10, a5 = 11, a6 = 11, a7 = 11, a8 = 10, a9 = 13, a 10 = 12, a 11 = 10, a 12 = 11, the historical temperature data from 1:00 to 2:00 on December 1 in the processing result has repeated data, that is, a2 is repeated. Therefore, one of the two a2 can be deleted.

[0082] Completing the missing data in the processing result and deleting the repeated data in the processing result can ensure that the historical temperature data in the processing result corresponding to each temperature detection device can be one-to-one corresponding, improve the accuracy of the processing result, and thus improve the accuracy and reliability of the subsequent clustering result.

[0083] In step 203, the plurality of temperature vectors are clustered to obtain a clustering result, and the clustering result includes at least one clustering cluster, and any clustering cluster in the at least one clustering cluster includes at least one temperature vector.

[0084] In an exemplary embodiment, the plurality of temperature vectors are clustered to obtain a clustering result, including: for any two temperature vectors in the plurality of temperature vectors, calculating the average temperature difference and the maximum temperature difference of the historical temperature data corresponding to the any two temperature vectors; in response to the average temperature difference satisfying the average temperature difference condition and the maximum temperature difference satisfying the maximum temperature difference condition, the any two temperature vectors belong to the same clustering cluster.

[0085] In a possible implementation, the plurality of temperature detection devices include A and B two temperature detection devices, and the temperature vector corresponding to the temperature detection device A can be A = (a1, a2, …, an ). Wherein, a1 to a n correspond to the average historical temperature data of the 1st day to the nth day detected by the temperature detection device A respectively. The temperature vector corresponding to the temperature detection device B can be B = (b1, b2, …, b n ). Wherein, b1 to b n correspond to the average historical temperature data of the 1st day to the nth day detected by the temperature detection device B respectively.

[0086] Exemplarily, the clustering algorithm can be set as:

[0087] eps1 = |a1-b1| + |a2-b2| + … + |a n -b n |; and

[0088] eps2 = σ(|a1-b1|-c) + σ(|a2-b2|-c) + … + σ(|a n -b n |-c).

[0089]

[0090] Wherein, eps1 is used to calculate the sum of the temperature difference of the n historical temperature data detected by the temperature detection device A and the n historical temperature data detected by the temperature detection device B. eps2 is used to calculate the number of times that the temperature difference between the temperature detection device A and the temperature detection device B exceeds the maximum temperature difference threshold c within n days. The maximum temperature difference threshold c is not limited by the embodiments of the present application, which can be limited based on experience or application scenarios.

[0091] After the values of eps1 and eps2 are calculated, the values of eps1 and eps2 can be substituted into the following formula

[0092] dis = σ(eps1-p×n) + σ(eps2-d)

[0093] Wherein, p is the average temperature difference threshold, which can be set as a positive number, for example, 0.5 degrees Celsius or 1 degree Celsius. n is the number of historical temperature data detected by the temperature detection device A or the temperature detection device B, which can be a positive integer, for example, 720 or 500. d is the number of times that the temperature difference between the temperature detection device A and the temperature detection device B exceeds the maximum temperature difference threshold c within n days, which can be set as a positive integer, for example, 3 or 5. Therefore, dis can be set as 0, and when the historical temperature data detected by any two temperature detection devices satisfies dis = 0, it can be considered that the temperature vectors corresponding to the above two temperature detection devices belong to the same clustering cluster.

[0094] In addition, minPts can be set to 1, where minPts is used to indicate a minimum number of temperature vectors in any clustering cluster. When minPts is 1, the minimum number of temperature vectors in any clustering cluster can be 1. For example, if there are 10 temperature vectors in total, 9 of the temperature vectors can be grouped into one clustering cluster, and the remaining 1 temperature vector can be grouped into another clustering cluster. In one possible implementation, any clustering cluster obtained by clustering has only 1 temperature vector, and in this case, there is no redundant device.

[0095] For example, a clustering effect diagram based on the clustering algorithm described above can be as shown in FIG. 7, where temperature vectors corresponding to at least 7 temperature detection devices are clustered to obtain 7 clustering clusters respectively labeled as A, B, C, D, E, F, and G in FIG. 7. Figure 3 Figure 3 For example, a clustering effect diagram based on the clustering algorithm described above can be as shown in FIG. 7, where temperature vectors corresponding to at least 7 temperature detection devices are clustered to obtain 7 clustering clusters respectively labeled as A, B, C, D, E, F, and G in FIG. 7.

[0096] For example, a clustering effect diagram based on the clustering algorithm described above can be as shown in FIG. 7, where temperature vectors corresponding to at least 7 temperature detection devices are clustered to obtain 7 clustering clusters respectively labeled as A, B, C, D, E, F, and G in FIG. 7. Figure 4 Figure 4 For example, a clustering effect diagram based on the clustering algorithm described above can be as shown in FIG. 7, where temperature vectors corresponding to at least 7 temperature detection devices are clustered to obtain 7 clustering clusters respectively labeled as A, B, C, D, E, F, and G in FIG. 7. Figure 4 Figure 4 For example, a clustering effect diagram based on the clustering algorithm described above can be as shown in FIG. 7, where temperature vectors corresponding to at least 7 temperature detection devices are clustered to obtain 7 clustering clusters respectively labeled as A, B, C, D, E, F, and G in FIG. 7.

[0097] The clustering algorithm provided in the embodiments of the present application does not need to specify the number of clustering clusters, and the number of clustering clusters can be automatically determined by the algorithm, thereby improving the robustness of clustering and avoiding the inaccuracy of the specified number of clustering clusters to reduce the accuracy of the clustering result. The embodiments of the present application also do not need to specify the initial clustering center, thereby eliminating the process of changing from the initial clustering center to the true clustering center, and improving the efficiency of clustering. In a certain application scenario, the number and position of temperature detection devices do not have a fixed rule, and therefore the shape and size of the clustering clusters obtained by clustering are not the same. The clustering algorithm provided in the embodiments of the present application can be applied to clustering clusters of various shapes and sizes, thereby improving the reliability and accuracy of clustering.

[0098] ​​​And, compared with a general DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, the embodiment of the application proposes a self-defined distance measurement formula dis = σ (eps1-p×n) + σ (eps2-d) for historical temperature data, which is more in line with the clustering requirements of temperature time series data and improves the reliability and accuracy of clustering. In addition, compared with the general DBSCAN clustering algorithm, the embodiment of the application does not need to convert the historical temperature data, thereby improving the efficiency of clustering.

[0099] In step 204, in response to the number of temperature vectors in any cluster being higher than the number threshold, a redundant device is determined from the temperature detection devices corresponding to the temperature vectors in any cluster.

[0100] The embodiment of the application does not limit the number threshold, for example, the number threshold can be 1. Therefore, if the number of temperature vectors in any cluster is higher than 1, it proves that there is a redundant device in the temperature detection devices corresponding to the temperature vectors in the cluster. For example, the cluster can include 3 temperature vectors, so 2 temperature vectors can be randomly selected in the cluster, and the temperature detection devices corresponding to the 2 temperature vectors are determined as redundant devices.

[0101] In an exemplary embodiment, after determining the redundant device from the temperature detection devices corresponding to the temperature vectors in any cluster, the method further comprises: performing quality detection on the determination result of the redundant device by a quality detection index to obtain a quality detection result, the quality detection index comprising at least one of ARI and a silhouette coefficient, and the quality detection result being used to indicate the accuracy of the determined redundant device.

[0102] In a possible implementation, the determination result of the redundant device can be quality detected by ARI. The value range of ARI is -1 to 1, and the larger the value of ARI is, the better the clustering effect is, and thus the higher the accuracy of the determination result of the redundant device is. The embodiment of the application does not limit the calculation steps of ARI, for example:

[0103]

[0104] Wherein, RI (Rand Index) can be calculated based on a contingency table, E[RI] is the expectation of RI, and max(RI) is the maximum value of RI.

[0105] In some embodiments, the determination result of the redundant device can be quality detected based on a silhouette coefficient. The value range of the silhouette coefficient is -1 to 1, and the greater the value of the silhouette coefficient, the better the clustering effect, and thus the higher the accuracy of the determination result of the redundant device. The calculation steps of the silhouette coefficient are not limited in the embodiments of the application, for example:

[0106]

[0107] wherein S(i) is the silhouette coefficient, a(i) is the intra-cluster dissimilarity of the historical temperature data i, that is, the average distance of the historical temperature data i to other historical temperature data in the same clustering cluster, and b(i) is the inter-cluster dissimilarity of the historical temperature data i, that is, the average distance of the historical temperature data i to historical temperature data in other clustering clusters.

[0108] Exemplarily, the application scenario of the method for determining the redundant device provided by the embodiments of the application can be a logistics warehouse. In this application scenario, the determination result of the redundant device is quality detected by taking ARI as the quality detection index, and the obtained quality detection result can be 0.655. 0.655 is closer to 1, and thus the accuracy of the determination result of the redundant device is higher. The determination result of the redundant device is quality detected by taking the silhouette coefficient as the quality detection index, and the obtained quality detection result can be 0.3. 0.3 is closer to 1, and thus the accuracy of the determination result of the redundant device is higher. Therefore, the redundant device is removed based on the obtained determination result of the redundant device, which can avoid the device redundancy caused by the intensive deployment of temperature detection devices in the logistics warehouse.

[0109] Exemplarily, the application scenario of the method for determining the redundant device provided by the embodiments of the application can be a greenhouse. In this application scenario, the determination result of the redundant device is quality detected by taking ARI as the quality detection index, and the obtained quality detection result can be 0.709. 0.709 is closer to 1, and thus the accuracy of the determination result of the redundant device is higher. The determination result of the redundant device is quality detected by taking the silhouette coefficient as the quality detection index, and the obtained quality detection result can be 0.35. 0.35 is closer to 1, and thus the accuracy of the determination result of the redundant device is higher. Therefore, the redundant device is removed based on the obtained determination result of the redundant device, which can avoid the device redundancy caused by the intensive deployment of temperature detection devices in the greenhouse.

[0110] The technical solution provided by the application clusters the temperature vectors corresponding to the historical temperature data of the plurality of temperature detection devices, and at least one clustering cluster is obtained. Therefore, the embodiments of the application can find the plurality of temperature detection devices with more similar historical temperature data, so that the redundant device can be determined from the devices with more similar historical temperature data, and the accuracy of determining the redundant device is improved.

[0111] As Figure 5 shown, the embodiment of the present application provides a method for determining redundant devices, which can include the following steps.

[0112] 501, obtaining historical temperature data. The implementation of this step can refer to step 201 described above, and will not be repeated here.

[0113] 502, grouping the historical temperature data to obtain the historical temperature data of each temperature detection device. The implementation of this step can refer to step 202 described above, and will not be repeated here.

[0114] 503, sorting and dividing the historical temperature data of each temperature detection device by time to obtain multiple groups of historical temperature data. The implementation of this step can refer to step 202 described above, and will not be repeated here.

[0115] 504, judging whether there is missing data in the multiple groups of historical temperature data. If yes, complete it. The implementation of this step can refer to step 202 described above, and will not be repeated here.

[0116] 505, judging whether there is repeated data in the multiple groups of historical temperature data. If yes, remove the repeated data. The implementation of this step can refer to step 202 described above, and will not be repeated here.

[0117] 506, determining a temperature vector based on the multiple groups of historical temperature data after zero to two operations of completion and removal of repeated data. The implementation of this step can refer to step 202 described above, and will not be repeated here.

[0118] 507, clustering based on the temperature vector. The implementation of this step can refer to step 203 described above, and will not be repeated here.

[0119] 508, determining redundant devices based on the clustering result. The implementation of this step can refer to step 204 described above, and will not be repeated here.

[0120] 509, quality detection of the determination result of the redundant devices. The implementation of this step can refer to step 204 described above, and will not be repeated here.

[0121] Referring to Figure 6 , the embodiment of the present application provides a device for determining redundant devices, which comprises:

[0122] The acquisition module 601 is configured to acquire historical temperature data detected by multiple temperature detection devices.

[0123] The first determination module 602 is configured to determine a plurality of temperature vectors based on historical temperature data detected by a plurality of temperature detection devices, and the historical temperature data detected by any temperature detection device corresponds to a temperature vector.

[0124] The clustering module 603 is configured to cluster the plurality of temperature vectors to obtain a clustering result, and the clustering result includes at least one cluster, and any cluster in the at least one cluster includes at least one temperature vector.

[0125] The second determination module 604 is configured to determine a redundant device from a temperature detection device corresponding to a temperature vector in any cluster in response to a quantity of the temperature vectors in the any cluster being higher than a quantity threshold.

[0126] In a possible implementation, the clustering module 603 is configured to calculate an average temperature difference and a maximum temperature difference of historical temperature data corresponding to any two temperature vectors in the plurality of temperature vectors, and in response to the average temperature difference satisfying an average temperature difference condition and the maximum temperature difference satisfying a maximum temperature difference condition, the any two temperature vectors belong to a same cluster.

[0127] In a possible implementation, the apparatus further includes:

[0128] The quality detection module is configured to perform quality detection on a determination result of the redundant device by using a quality detection index to obtain a quality detection result, the quality detection index includes at least one of an ARI and a profile coefficient, and the quality detection result is used to indicate accuracy of the determined redundant device.

[0129] In a possible implementation, the first determination module 602 is configured to perform grouping processing on the historical temperature data detected by the plurality of temperature detection devices to obtain a processing result, and determine the plurality of temperature vectors based on the processing result.

[0130] In a possible implementation, the first determination module 602 is configured to group historical temperature data detected by the plurality of temperature detection devices according to temperature detection devices to obtain historical temperature data of each temperature detection device, divide the historical temperature data of each temperature detection device according to a reference granularity respectively to obtain a plurality of groups of historical temperature data, and take an average value of each group of the divided historical temperature data respectively to obtain average historical temperature data of each temperature detection device, and the average historical temperature data of any temperature detection device corresponds to a processing result.

[0131] In a possible implementation, the first determination module 602 is further configured to perform completion on the processing result in response to missing data in the processing result.

[0132] The first determination module 602 is configured to determine the plurality of temperature vectors based on the completed processing result.

[0133] In a possible implementation, the first determining module 602 is further configured to delete repeated data corresponding to the same temperature detection device and the same time in the processing result.

[0134] The first determining module 602 is configured to determine the plurality of temperature vectors based on the processing result from which the repeated data is deleted.

[0135] In the embodiments of the present application, the technical solution provided by the present application clusters the temperature vectors corresponding to the historical temperature data of the plurality of temperature detection devices to obtain at least one clustering cluster. Therefore, the embodiments of the present application can find the plurality of temperature detection devices with relatively similar historical temperature data, so as to determine the redundant devices from the devices with relatively similar historical temperature data, thereby improving the accuracy of determining the redundant devices.

[0136] It should be noted that the apparatus provided by the above embodiments is only used as an example to divide the above functional modules in realizing the functions thereof, and in actual applications, the above functions can be completed by different functional modules according to the needs, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and the specific implementation process is described in detail in the method embodiments, which will not be described here.

[0137] Figure 7 FIG. 1 is a structural schematic diagram of a computer device provided by an embodiment of the present application. The computer device can be a server. The server can have great differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) 701 and one or more memories 702. At least one computer program is stored in the one or more memories 702, and the at least one computer program is loaded and executed by the one or more processors 701, so that the server implements the method for determining redundant devices provided by each method embodiment described above. Of course, the server can also have a wired or wireless network interface, a keyboard, an input and output interface, and other components for realizing the functions of the apparatus, and will not be described here.

[0138] Figure 8is a structural schematic diagram of a computer device provided by an embodiment of the present application. The device can be a terminal, for example, can be: a smart phone, a tablet computer, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a notebook computer or a desktop computer. The terminal can also be referred to as a user equipment, a portable terminal, a laptop terminal, a desktop terminal, and other names.

[0139] Generally, the terminal includes a processor 801 and a memory 802.

[0140] The processor 801 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 801 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor 801 can also include a main processor and a coprocessor, the main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 801 can be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, the processor 801 can also include an AI (Artificial Intelligence) processor for processing machine learning-related computing operations.

[0141] The memory 802 can include one or more computer-readable storage media, which can be non-transitory. The memory 802 can also include a high-speed random access memory, and a non-volatile memory such as one or more disk storage devices, flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one instruction for being executed by the processor 801 to enable the terminal to implement the method for determining a redundant device provided by the method embodiment of the present application.

[0142] In some embodiments, the terminal can further optionally include a peripheral device interface 803 and at least one peripheral device. The processor 801, the memory 802 and the peripheral device interface 803 can be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 803 through a bus, a signal line or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 804, a display screen 805, a camera component 806, an audio circuit 807, a positioning component 808 and a power supply 809.

[0143] The peripheral device interface 803 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 801 and the memory 802. In some embodiments, the processor 801, the memory 802 and the peripheral device interface 803 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 801, the memory 802 and the peripheral device interface 803 can be implemented on a separate chip or circuit board, and the present embodiments are not limited in this regard.

[0144] The radio frequency circuit 804 is used to receive and send RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 804 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 804 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 804 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and the like. The radio frequency circuit 804 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to metropolitan area networks, various generations of mobile communication networks (2G, 3G, 4G and 5G), wireless local area networks and / or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 804 can also include NFC (Near Field Communication) related circuitry, and the present application is not limited in this regard.

[0145] The display screen 805 is configured to display a UI (User Interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 805 is a touch display screen, the display screen 805 is further configured to capture touch signals on or above the surface of the display screen 805. The touch signals can be input to the processor 801 as control signals for processing. In this case, the display screen 805 can also be configured to provide virtual buttons and / or virtual keyboard, also known as soft buttons and / or soft keyboard. In some embodiments, the display screen 805 can be one, disposed on the front panel of the terminal; in other embodiments, the display screen 805 can be at least two, respectively disposed on different surfaces of the terminal or in a folding design; in other embodiments, the display screen 805 can be a flexible display screen, disposed on a curved surface or a folding surface of the terminal. Even, the display screen 805 can also be disposed in an irregular shape, i.e., a special-shaped screen. The display screen 805 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc.

[0146] The camera assembly 806 is configured to capture images or videos. Optionally, the camera assembly 806 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal, and the rear camera is disposed on the back of the terminal. In some embodiments, the rear camera is at least two, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, to realize the background blur function by fusing the main camera and the depth-of-field camera, the panoramic shooting and VR (Virtual Reality) shooting function by fusing the main camera and the wide-angle camera, or other fusion shooting functions. In some embodiments, the camera assembly 806 can further include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. The dual-color temperature flash refers to the combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0147] The audio circuit 807 can include a microphone and a speaker. The microphone is used to collect sound waves of a user and an environment, and convert the sound waves into an electrical signal input to the processor 801 for processing, or input to the radio frequency circuit 804 to realize voice communication. For the purpose of stereo sound collection or noise reduction, the microphone can be multiple, and arranged at different parts of the terminal. The microphone can also be an array microphone or an omnidirectional collection type microphone. The speaker is used to convert an electrical signal from the processor 801 or the radio frequency circuit 804 into sound waves. The speaker can be a conventional diaphragm speaker, or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only can it convert an electrical signal into a sound wave audible to humans, but it can also convert an electrical signal into an inaudible sound wave to humans for ranging purposes, etc. In some embodiments, the audio circuit 807 can also include a headphone jack.

[0148] The positioning component 808 is used to position the current geographic location of the terminal to realize navigation or LBS (Location Based Service, Location Based Service). The positioning component 808 can be a positioning component based on the GPS (Global Positioning System, Global Positioning System) of the United States, the Beidou system of China, the Glonass system of Russia or the Galileo system of the European Union.

[0149] The power supply 809 is used to supply power to each component in the terminal. The power supply 809 can be alternating current, direct current, disposable battery or rechargeable battery. When the power supply 809 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0150] In some embodiments, the terminal also includes one or more sensors 810. The one or more sensors 810 include but are not limited to: an acceleration sensor 811, a gyroscope sensor 812, a pressure sensor 813, a fingerprint sensor 814, an optical sensor 815 and a proximity sensor 816.

[0151] The acceleration sensor 811 can detect the acceleration in three coordinate axes of the coordinate system established by the terminal. For example, the acceleration sensor 811 can be used to detect the components of gravitational acceleration in three coordinate axes. The processor 801 can control the display screen 805 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 811. The acceleration sensor 811 can also be used for game or user motion data collection.

[0152] The gyro sensor 812 can detect the body direction and rotation angle of the terminal, and the gyro sensor 812 can collect 3D motions of the user on the terminal in cooperation with the acceleration sensor 811. The processor 801 can implement the following functions according to the data collected by the gyro sensor 812: motion sensing (e.g., changing a UI according to a tilt operation of the user), image stabilization when shooting, game control, and inertial navigation.

[0153] The pressure sensor 813 can be disposed at the side frame of the terminal and / or the lower layer of the display screen 805. When the pressure sensor 813 is disposed at the side frame of the terminal, the grip signal of the user on the terminal can be detected, and the left / right hand recognition or shortcut operation can be performed by the processor 801 according to the grip signal collected by the pressure sensor 813. When the pressure sensor 813 is disposed at the lower layer of the display screen 805, the operable control on the UI interface can be controlled by the processor 801 according to the pressure operation of the user on the display screen 805. The operable control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0154] The fingerprint sensor 814 is used to collect the fingerprint of the user, and the identity of the user can be recognized by the processor 801 according to the fingerprint collected by the fingerprint sensor 814, or by the fingerprint sensor 814 according to the collected fingerprint. When the identity of the user is recognized as a trusted identity, the processor 801 authorizes the user to perform a related sensitive operation, which includes unlocking the screen, viewing encrypted information, downloading software, payment, and changing settings, etc. The fingerprint sensor 814 can be disposed at the front, back, or side of the terminal. When the terminal is provided with a physical button or a manufacturer logo (trademark), the fingerprint sensor 814 can be integrated with the physical button or the manufacturer logo.

[0155] The optical sensor 815 is used to collect the ambient light intensity. In one embodiment, the processor 801 can control the display brightness of the display screen 805 according to the ambient light intensity collected by the optical sensor 815. Specifically, when the ambient light intensity is high, the display brightness of the display screen 805 is increased, and when the ambient light intensity is low, the display brightness of the display screen 805 is decreased. In another embodiment, the processor 801 can also dynamically adjust the shooting parameters of the camera assembly 806 according to the ambient light intensity collected by the optical sensor 815.

[0156] The proximity sensor 816, also referred to as a distance sensor, is usually arranged on the front panel of the terminal. The proximity sensor 816 is used to collect the distance between the user and the front of the terminal. In one embodiment, when the proximity sensor 816 detects that the distance between the user and the front of the terminal gradually decreases, the display screen 805 is switched from the bright screen state to the screen-off state under the control of the processor 801; when the proximity sensor 816 detects that the distance between the user and the front of the terminal gradually increases, the display screen 805 is switched from the screen-off state to the bright screen state under the control of the processor 801.

[0157] Those skilled in the art can understand that the structure shown in the foregoing embodiments does not constitute a limitation on the computer device, and the computer device can include more or fewer components than those shown in the drawings, or combine certain components, or adopt a different arrangement of components. Figure 8

[0158] In an example embodiment, a computer device is also provided, which includes a processor and a memory having at least one computer program stored therein. The at least one computer program is loaded and executed by one or more processors to enable the computer device to implement any of the above-described methods for determining a redundant device.

[0159] In an example embodiment, a computer readable storage medium is also provided, which stores at least one computer program. The at least one computer program is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-described methods for determining a redundant device.

[0160] In a possible implementation manner, the above computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0161] In an example embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the computer device to implement any of the above-described methods for determining a redundant device.

[0162] ​It should be understood that the "multiple" mentioned herein refers to two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents that the associated objects before and after it are in an "or" relationship.

[0163] It should be noted that the information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the present application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions. For example, the historical temperature data detected by the multiple temperature detection devices involved in the present application is obtained under sufficient authorization.

[0164] The above only describes exemplary embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining redundant devices, characterized in that, The method includes: Acquire historical temperature data detected by multiple temperature detection devices; Multiple temperature vectors are determined based on the historical temperature data detected by the multiple temperature detection devices, with each temperature detection device corresponding to a temperature vector based on the historical temperature data detected by any one of the temperature detection devices. Clustering is performed on the plurality of temperature vectors to obtain clustering results, wherein the clustering results include at least one cluster, and any cluster in the at least one cluster includes at least one temperature vector; In response to the number of temperature vectors in any cluster being higher than a number threshold, redundant devices are determined from the temperature detection devices corresponding to the temperature vectors in any cluster; The clustering of the multiple temperature vectors to obtain the clustering results includes: For any two of the plurality of temperature vectors, calculate the average temperature difference and the maximum temperature difference of the historical temperature data corresponding to the two temperature vectors; If the average temperature difference satisfies the average temperature difference condition and the maximum temperature difference satisfies the maximum temperature difference condition, then any two temperature vectors belong to the same cluster.

2. The method according to claim 1, characterized in that, After determining redundant devices from the temperature detection devices corresponding to the temperature vectors in any of the clusters, the method further includes: The determination of redundant equipment is evaluated using quality inspection indicators to obtain quality inspection results. These indicators include at least one of the Adjusted Rand Index (ARI) and the Profile Coefficient. The quality inspection results are used to indicate the accuracy of the determined redundant equipment.

3. The method according to claim 1, characterized in that, The determination of multiple temperature vectors based on historical temperature data detected by the multiple temperature detection devices includes: The historical temperature data detected by the multiple temperature detection devices are grouped and processed to obtain the processing results, and the multiple temperature vectors are determined based on the processing results.

4. The method according to claim 3, characterized in that, The process of grouping and processing historical temperature data detected by the multiple temperature detection devices to obtain processing results includes: The historical temperature data detected by the multiple temperature detection devices are grouped according to the temperature detection device to obtain the historical temperature data of each temperature detection device. The historical temperature data of each temperature detection device is divided according to the reference granularity to obtain multiple sets of historical temperature data; The average value of each group of historical temperature data obtained from the division is taken to obtain the average historical temperature data of each temperature detection device. The average historical temperature data of any temperature detection device corresponds to a processing result.

5. The method according to claim 3, characterized in that, After grouping and processing the historical temperature data detected by the multiple temperature detection devices to obtain the processing results, the method further includes: In response to missing data in the processing result, the processing result is completed; Determining the plurality of temperature vectors based on the processing result includes: The multiple temperature vectors are determined based on the completed processing results.

6. The method according to claim 3, characterized in that, After grouping and processing the historical temperature data detected by the multiple temperature detection devices to obtain the processing results, the method further includes: If duplicate data corresponding to the same temperature detection device and the same time exists in the processing result, the duplicate data is deleted. Determining the plurality of temperature vectors based on the processing result includes: The multiple temperature vectors are determined based on the processing results after removing duplicate data.

7. An apparatus for determining redundant equipment, characterized in that, The device includes: The acquisition module is used to acquire historical temperature data detected by multiple temperature detection devices; The first determining module is used to determine multiple temperature vectors based on historical temperature data detected by the multiple temperature detection devices, wherein any historical temperature data detected by any temperature detection device corresponds to a temperature vector. A clustering module is used to cluster the plurality of temperature vectors to obtain clustering results, wherein the clustering results include at least one cluster, and any cluster in the at least one cluster includes at least one temperature vector; the step of clustering the plurality of temperature vectors to obtain clustering results includes: for any two temperature vectors among the plurality of temperature vectors, calculating the average temperature difference and the maximum temperature difference of the historical temperature data corresponding to the two temperature vectors; in response to the average temperature difference satisfying the average temperature difference condition and the maximum temperature difference satisfying the maximum temperature difference condition, then the two temperature vectors belong to the same cluster. The second determining module is used to determine redundant devices from the temperature detection devices corresponding to the temperature vectors in any cluster in response to the number of temperature vectors in any cluster being higher than a quantity threshold.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one computer program, which is loaded and executed by the processor to enable the computer device to implement the method for determining redundant devices as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to implement the method for determining redundant devices as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium, a processor of a computer device reading the computer instructions from the computer-readable storage medium, and the processor executing the computer instructions to cause the computer device to perform the method for determining redundant devices as described in any one of claims 1 to 6.

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