Method and device for recommending detection equipment

Abstract

The embodiment of the invention provides a method and device for recommending detection equipment, and the method comprises the steps: obtaining a target condition inputted by a target account, and enabling the target condition to be a condition for selecting the detection equipment for a target detection region; determining an attribute value of each piece of detection equipment based on attribute information of each piece of detection equipment in a detection equipment library; and determining a first group of detection equipment in the detection equipment library according to the attribute value and the target condition, and recommending the first group of detection equipment to the target account. According to the invention, the problem of low efficiency of selecting the detection equipment is solved, and the effect of improving the efficiency of selecting the detection equipment is achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of communications, and more particularly, to a method and apparatus for recommending detection devices. Background Art

[0002] There are various models of detection devices (e.g., cameras) in the field of security. For the product selection of detection devices, currently, professional personnel or trained customers are required to select products that meet their needs from a large number of security products according to their own application scenario requirements. This requires familiarity with the various parameters of the products. Otherwise, it will be difficult to make a choice and the efficiency will be low.

[0003] In view of the above problems, there is currently no effective solution. Summary of the Invention

[0004] Embodiments of the present invention provide a method and apparatus for recommending detection devices to at least solve the problem of low efficiency in selecting detection devices in related technologies.

[0005] According to an embodiment of the present invention, there is provided a method for recommending a detection device, including: obtaining a target condition input by a target account, where the target condition is a condition for selecting a detection device for a target detection area; determining attribute values of each detection device based on attribute information of each detection device in a detection device library; and determining a first group of detection devices in the detection device library according to the attribute values and the target condition, and recommending the first group of detection devices to the target account.

[0006] In an exemplary embodiment, determining the attribute values of each detection device based on the attribute information of each detection device in the detection device library includes: obtaining the attribute information of each detection device, where the attribute information includes at least one of the following: detection area, detection type, storage size, unit price; and determining the attribute value of the detection device according to at least one of the attribute information.

[0007] In an exemplary embodiment, determining the attribute value of the detection device according to at least one of the attribute information includes: determining a first parameter value by multiplying the detection area and the detection type; determining a second parameter value by a weighted sum of the storage size and the unit price; and determining the attribute value of the detection device by a ratio of the first parameter value and the second parameter value.

[0008] In an exemplary embodiment, determining a first set of detection devices in the detection device library according to the attribute value and the target condition includes: traversing each detection device in the detection device library in descending order of the attribute value; when the attribute information of N detection devices meets the target condition during the traversal, determining the N detection devices as the first set of detection devices.

[0009] In an exemplary embodiment, when the attribute information of N detection devices meets the target condition during the traversal, determining the N detection devices as the first set of detection devices includes at least one of the following: the sum of the detection areas of the N detection devices is greater than or equal to the target detection area; the sum of the storage sizes of the N detection devices is less than or equal to the target storage size; the sum of the unit prices of the N detection devices is less than or equal to the target total price; the target condition includes at least one of the target detection area, the target storage size, and the target total price.

[0010] In an exemplary embodiment, before determining the attribute value of each detection device, the method further includes: determining whether there is a second set of detection devices that matches the target condition in the historical recommendation record, and if so, recommending the second set of detection devices to the target account.

[0011] In an exemplary embodiment, determining whether there is a second set of detection devices that matches the target condition in the historical recommendation record includes: obtaining the recommendation conditions of each recommendation record in the historical recommendation record; determining the recommendation conditions whose similarity with the target condition is greater than or equal to a preset threshold as the target recommendation conditions; determining the recommendation records corresponding to the target recommendation conditions as the target recommendation records; and determining the detection devices in the target recommendation records as the second set of detection devices.

[0012] In an exemplary embodiment, after recommending the first set of detection devices to the target account, the method further includes: when the target account selects the first set of detection devices, recording the first set of detection devices in the historical recommendation record.

[0013] According to another embodiment of the present invention, there is provided a device for recommending detection devices, including: an acquisition module, configured to acquire a target condition input by a target account, where the target condition is a condition for selecting detection devices for a target detection area; a determination module, configured to determine the attribute value of each detection device based on the attribute information of each detection device in the detection device library; and a recommendation module, configured to determine a first set of detection devices in the detection device library according to the attribute value and the target condition, and recommend the first set of detection devices to the target account.

[0014] According to another embodiment of the present invention, there is also provided a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0015] According to another embodiment of the present invention, there is also provided an electronic device including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0016] According to another embodiment of the present invention, there is also provided a computer program product including a computer program, wherein when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0017] Through the present invention, by obtaining the target condition input by the target account, where the target condition is the condition for selecting a detection device for the target detection area; based on the attribute information of each detection device in the detection device library, determining the attribute values of each detection device; and determining a first set of detection devices in the detection device library according to the attribute values and the target condition, and recommending the first set of detection devices to the target account. The purpose of automatically recommending a detection device for the target account by a computer is achieved. Therefore, the problem of low efficiency in selecting a detection device can be solved, and the effect of improving the efficiency of selecting a detection device can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a hardware structure block diagram of a mobile terminal of a method for recommending a detection device according to an embodiment of the present invention;

[0019] Figure 2 is a flowchart of a method for recommending a detection device according to an embodiment of the present invention;

[0020] Figure 3 is an overall flowchart according to an embodiment of the present invention;

[0021] Figure 4 is a structure block diagram of a device for recommending a detection device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The embodiments of the present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence.

[0024] The method embodiments provided in the embodiments of the present application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 is a hardware structure block diagram of a mobile terminal for a method of recommending and detecting devices according to an embodiment of the present invention. As Figure 1 shown, the mobile terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data. Among them, the above mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above mobile terminal. For example, the mobile terminal may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0025] The memory 104 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the method of recommending and detecting devices in the embodiments of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the mobile terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (abbreviated as RF) module, which is used to communicate with the Internet wirelessly.

[0027] In this embodiment, a method of recommending and detecting devices running on the above mobile terminal is provided. Figure 2 is a flowchart of a method of recommending and detecting devices according to an embodiment of the present invention. AsFigure 2 As shown in the figure, the process includes the following steps:

[0028] Step S202: Obtain the target conditions input by the target account, where the target conditions are the conditions for selecting detection devices for the target detection area.

[0029] The above target account is the account logged in by the user on the terminal device. The target conditions include at least one of the following: target detection area (the area size S of the target detection area), target storage size (such as the storage size M of the server), target total price (cost budget C), detection ability (the ability that the detection device has when detecting the objects in the target detection area. For example, temperature measurement, face recognition, vehicle recognition, etc.). The above detection devices are devices used to detect the objects in the target detection area, such as image acquisition devices like cameras and video cameras.

[0030] Step S204: Based on the attribute information of each detection device in the detection device library, determine the attribute values of each detection device.

[0031] Specifically, obtain the attribute information of each detection device, where the attribute information includes at least one of the following: detection area, detection type, storage size, unit price; determine the attribute value of the detection device according to at least one of the attribute information.

[0032] Among them, the above detection area refers to the area that the camera can cover and shoot, the detection type is the type of objects that the camera can detect, for example, temperature measurement, face recognition, vehicle recognition, etc. The storage size refers to the storage size required by the camera, and the unit price refers to the price of a single camera.

[0033] For example, determine the product of the detection area and the detection type as the first parameter value; determine the weighted sum of the storage size and the unit price as the second parameter value; determine the ratio of the first parameter value and the second parameter value as the attribute value of the detection device.

[0034] The attribute value of the detection device can be calculated by the following formula:

[0035] Q = (s × p) / (w 1 × m + w 2 × c)

[0036] Among them, s is the detection area, p is the detection type, m is the storage size, c is the unit price, and w 1 、w 2 are preset weight coefficients.

[0037] Step S206: Determine a first set of detection devices in the detection device library according to the attribute value and the target condition, and recommend the first set of detection devices to the target account.

[0038] Specifically, traverse each detection device in the detection device library in descending order of the attribute value; when the attribute information of N detection devices meets the target condition during the traversal, determine the N detection devices as the first set of detection devices.

[0039] When the attribute information of N detection devices meets the target condition during the traversal, determining the N detection devices as the first set of detection devices includes at least one of the following: the sum of the detection areas of the N detection devices is greater than or equal to the target detection area; the sum of the storage sizes of the N detection devices is less than or equal to the target storage size; the sum of the unit prices of the N detection devices is less than or equal to the target total price.

[0040] Optionally, the execution subject of the above steps may be a background processor, or other devices with similar processing capabilities, or a machine at least integrated with an image acquisition device and a data processing device. Among them, the image acquisition device may include a graphic acquisition module such as a camera, and the data processing device may include a terminal such as a computer or a mobile phone, but is not limited thereto.

[0041] Through the above steps, by obtaining the target condition input by the target account, where the target condition is the condition for selecting a detection device for the target detection area; based on the attribute information of each detection device in the detection device library, determine the attribute value of each detection device; determine a first set of detection devices in the detection device library according to the attribute value and the target condition, and recommend the first set of detection devices to the target account. The purpose of automatically recommending detection devices for the target account by the computer is achieved. Therefore, the problem of low efficiency in selecting detection devices can be solved, and the effect of improving the efficiency of selecting detection devices can be achieved.

[0042] In an exemplary embodiment, after recommending the first set of detection devices to the target account, the method further includes: when the target account selects the first set of detection devices, record the first set of detection devices in the historical recommendation record.

[0043] In an exemplary embodiment, before determining the attribute value of each detection device, the method further includes: determine whether there is a second set of detection devices in the historical recommendation record that matches the target condition. If so, recommend the second set of detection devices to the target account. If not, recommend the first set of detection devices to the target account.

[0044] Among them, the historical recommendation records store multiple groups of detection devices recommended to the target account and selected by the target account within the historical time range. One recommendation record records a group of detection devices and the recommendation conditions for this group of detection devices, including but not limited to: detection area, storage size, total price, detection type, etc.

[0045] In an exemplary embodiment, determining whether there is a second group of detection devices that match the target conditions in the historical recommendation records includes: obtaining the recommendation conditions of each recommendation record in the historical recommendation records; determining the recommendation conditions with a similarity greater than or equal to a preset threshold between the recommendation conditions and the target conditions as the target recommendation conditions; determining the recommendation records corresponding to the target recommendation conditions as the target recommendation records; and determining the detection devices in the target recommendation records as the second group of detection devices.

[0046] For example, take the target conditions input by the target account as a vector V(S,M,C,P), and the recommendation conditions of each recommendation record in the historical recommendation records as a vector V i (S i ,M i ,C i ,P i ).

[0047] Calculate the cosine similarity between the vectors: R = (V * Vi) / (|V| × |Vi|). Set the preset threshold to 80% (for example only, the threshold can be set according to the actual situation). When the similarity exceeds the threshold, this recommendation record is used as the target recommendation record, and the detection devices in this record are used as the detection devices recommended to the target account. The similarities of all recommendation records can be calculated, sorted in descending order of similarity, and provided to the user to choose whether to use the corresponding detection devices. If so, the selection is completed, otherwise the detection device library recommends detection devices to the target account.

[0048] In the above embodiment, the selection conditions are generalized by calculating the similarity between the target conditions input by the target account and the recommendation conditions of each recommendation record in the historical recommendation records, so that the detection devices with a similarity reaching a certain threshold in the historical recommendation record library can be automatically recommended, improving the applicability of the intelligent selection tool.

[0049] Without using existing historical recommendation records, the attribute values of the detection device can be customized according to the target conditions input by the user, and intelligent algorithms such as the knapsack algorithm can be used according to the attribute values to automatically generate recommended detection devices. The recommended detection devices can be expanded to the historical recommendation records. The user only needs to perform the first step and input the target conditions, and the remaining all will automatically complete the product selection and generate a recommended product selection plan. This greatly reduces the usage threshold of the product selection tool. Even if the user is not familiar with the product, they can still use it. It has a high degree of intelligence, a flexible sample library, and is applicable to many scenarios.

[0050] As Figure 3 shown is the overall flowchart, including the following steps:

[0051] Step S301, the user inputs specific target conditions, such as the size S of the monitoring map area, the storage size M of the server, the cost budget C, the detection ability P requirements, etc.;

[0052] Step S302, regard the target conditions input by the user as a vector V(S, M, C, P), retrieve the recommended conditions Vi of each recommended record in the historical recommendation records, and calculate the cosine correlation between the vectors: R = (V * Vi) / (|V| × |Vi|), and set the threshold to 80%;

[0053] When the correlation exceeds the threshold, this recommended record in the historical recommendation records can be selected as an alternative, and the detection device in this recommended record is used as an alternative detection device. After calculating all the results, sort the alternative recommended records in descending order of similarity and provide them to the user to choose whether to use. If so, the selection ends, otherwise enter the sub-process of the system automatically generating a plan;

[0054] Step S303, the system automatically identifies the camera corresponding to the detection type according to the detection type in the target conditions. For example, if the detection ability requirement in the target conditions is temperature measurement, then cameras with the detection type of being able to measure temperature need to be screened, such as thermal imaging cameras. This step first conducts a rough screening, and the screened detection devices are used as the detection device library;

[0055] Step S304, select the optimal detection device for recommendation according to the intelligent algorithm, which can be implemented by dynamic recursion or greedy algorithm. For the convenience of quantification, the greedy algorithm is selected here to implement. First, an attribute value Q of the detection device needs to be defined. Each time, the detection device with the largest attribute value is selected from the detection device library until the system ability that meets the target conditions is satisfied, and at the same time, the limit conditions of the target conditions cannot be exceeded. The attribute value of the detection device can be defined as: the ratio of the total revenue attribute of the detection device to the total cost, where s, p, m, and c respectively represent the detection area, detection type, occupied storage size, and unit price of a single detection device.

[0056] Q = (s × p) / (w1 ×m + w 2 ×c)

[0057] Where s is the detection area, p is the detection type, m is the storage size, c is the unit price, and w 1 and w 2 are preset weight coefficients.

[0058] The end condition in this model can be described as follows: the sum of the detection areas of all recommended detection devices is greater than or equal to the input target condition S, the sum of the occupied storage is less than or equal to the input target condition M, and the sum of the prices of all detection devices is less than or equal to the input target condition C.

[0059] Step S304: Recommend the calculated detection devices to the target account. The target account confirms whether to use them. If confirmed correctly, proceed to the next step; otherwise, the product selection can be adjusted manually.

[0060] Step S305: Automatically save the detection devices used by the target account and archive them to the historical recommendation record. The historical recommendation record includes the target conditions input by the user and the recommended product list (recommended detection devices), which is used for the next recommendation of detection devices to the target account.

[0061] In the product selection tool application in the security field of this application, an intelligent algorithm is proposed to enable the system to automatically recommend products, replacing the product selection method of manual selection by customers, greatly improving the product selection efficiency. At the same time, the usability of the tool is no longer restricted by the customer's familiarity with the product. Even if the customer is not familiar with the product, the tool can be used for product selection. At the same time, the concept of the attribute value of the detection device is innovatively proposed, and an efficient knapsack algorithm can be used to quickly generate a recommendation scheme. The intelligent recommendation algorithm here is not limited to the knapsack algorithm described in the proposal, and other recommendation algorithms such as dynamic recursion, and even more complex deep learning and reinforcement learning can also be adopted. The core is to use the algorithm to generate a recommended product scheme;

[0062] This application also introduces conditional similarity and expands the applicable scenarios of the historical recommendation record. The historical recommendation record is saved in the database for subsequent recommendations. In the security field, for the product selection scheme of the same scenario, the similarity of the scenario is often more concerned, that is, the concept of conditional similarity in this application, and it is not required that the conditions be exactly the same. Therefore, calculating the similarity and then recommending the scheme based on the similarity degree can greatly improve the utilization rate of the existing schemes in the scheme library and improve the product selection efficiency. For example, setting the similarity threshold to 80% correspondingly increases the utilization rate of the schemes in the scheme library by 20%.

[0063] Compared with traditional product selection tools in the security field, intelligent algorithms are used to achieve automatic product recommendation by the system, replacing the way of relying on customers to manually select products. This makes the use of the product selection tool independent of customers' familiarity with products, reduces the usage difficulty, and improves the selection efficiency. Using the idea of the knapsack algorithm, the attribute values of cameras are defined, and then the camera product with the highest attribute value is selected each time to meet the customer's conditions, enabling the rapid generation of recommended product solutions. A new usage method is proposed, that is, by calculating the correlation of condition vectors to define the similarity between conditions, and then setting a similarity threshold. The solutions exceeding the threshold can be used for recommendation. This can significantly improve the utilization rate of the solution library and enhance the recommendation ability of the system. In the application of product selection tools in the security field, using intelligent algorithms to recommend products instead of manual selection by humans, the implementation forms of intelligent algorithms include but are not limited to the knapsack algorithm, dynamic recursion, neural network, deep learning, etc.; when the product solution library makes recommendations, it uses the condition similarity threshold for recommendation instead of the same conditions as the standard; by calculating the similarity of conditions, the applicable scenarios of the recommendation system are generalized, no longer limited to a specific condition, improving the usability of the system.

[0064] Through the description of the above implementation manners, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0065] In this embodiment, a device for a recommendation detection device is further provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0066] Figure 4 is a structural block diagram of the device for a recommendation detection device according to an embodiment of the present invention, as Figure 4As shown in the figure, the device includes: an acquisition module 42, configured to acquire a target condition input by a target account, where the target condition is a condition for selecting a detection device for a target detection area; a determination module 44, configured to determine an attribute value of each of the detection devices based on the attribute information of each detection device in a detection device library; and a recommendation module 46, configured to determine a first set of detection devices in the detection device library according to the attribute value and the target condition, and recommend the first set of detection devices to the target account.

[0067] In an exemplary embodiment, the above device is further configured to acquire the attribute information of each of the detection devices, where the attribute information includes at least one of the following: detection area, detection type, storage size, unit price; and determine the attribute value of the detection device according to at least one of the attribute information.

[0068] In an exemplary embodiment, the above device is further configured to determine the product of the detection area and the detection type as a first parameter value; determine the weighted sum of the storage size and the unit price as the second parameter value; and determine the ratio of the first parameter value to the second parameter value as the attribute value of the detection device.

[0069] In an exemplary embodiment, the above device is further configured to traverse each of the detection devices in the detection device library in descending order of the attribute value; and when the attribute information of N detection devices satisfies the target condition during the traversal, determine the N detection devices as the first set of detection devices.

[0070] In an exemplary embodiment, the above device is further configured to, when the attribute information of N detection devices satisfies the target condition during the traversal, determine the N detection devices as the first set of detection devices, including at least one of the following: the sum of the detection areas of the N detection devices is greater than or equal to a target detection area; the sum of the storage sizes of the N detection devices is less than or equal to a target storage size; the sum of the unit prices of the N detection devices is less than or equal to a target total price; and the target condition includes at least one of the target detection area, the target storage size, and the target total price.

[0071] In an exemplary embodiment, the above device is further configured to, before determining the attribute value of each of the detection devices, determine whether there is a second set of detection devices that matches the target condition in a historical recommendation record, and if so, recommend the second set of detection devices to the target account.

[0072] In an exemplary embodiment, the above device is further configured to obtain the recommendation conditions of each recommendation record in the historical recommendation record; determine the recommendation conditions whose similarity with the target condition is greater than or equal to a preset threshold as the target recommendation conditions; determine the recommendation records corresponding to the target recommendation conditions as the target recommendation records; and determine the detection devices in the target recommendation records as the second group of detection devices.

[0073] In an exemplary embodiment, after the above device recommends the first group of detection devices to the target account, in the case where the target account selects the first group of detection devices, the above device is further configured to record the first group of detection devices in the historical recommendation record.

[0074] It should be noted that the above-mentioned respective modules can be implemented by software or hardware. For the latter, it can be achieved in the following ways, but not limited thereto: the above-mentioned modules are all located in the same processor; or, the above-mentioned respective modules are separately located in different processors in any combination form.

[0075] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. Wherein, when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0076] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media such as a USB flash drive, a read-only memory (ROM for short), a random access memory (RAM for short), a mobile hard disk, a magnetic disk, or an optical disc that can store a computer program.

[0077] An embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0078] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. Wherein, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0079] Specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated herein.

[0080] An embodiment of the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0081] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0082] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for recommending a testing device, characterized in that: include: Obtaining a target condition input by a target account, wherein the target condition is a condition for selecting a detection device for a target detection area; Determine the attribute value of each detection device based on the attribute information of each detection device in the detection device library; A first group of detection devices is determined in the detection device library according to the attribute value and the target condition, and the first group of detection devices is recommended to the target account.

2. The method according to claim 1, characterized in that Based on the attribute information of each detection device in the detection device library, determining the attribute value of each detection device includes: Acquire attribute information of each of the detection devices, wherein the attribute information includes at least one of the following: detection area, detection type, storage size, and unit price; Determine a property value of the detection device according to at least one of the property information.

3. The method according to claim 2, characterized in that Determining the attribute value of the detection device according to at least one of the attribute information includes: determining a product of the detection area and the detection type as a first parameter value; determining a weighted sum of the storage size and the unit price as a second parameter value; A ratio of the first parameter value to the second parameter value is determined as a property value of the detection device.

4. The method according to claim 1, characterized in that: Determining a first group of detection devices in the detection device library according to the attribute value and the target condition includes: Traversing each of the detection devices in the detection device library in descending order of the attribute values; When the attribute information of the traversed N detection devices meets the target condition, the N detection devices are determined as the first group of detection devices.

5. The method according to claim 4, characterized in that In the case where the attribute information of the traversed N detection devices meets the target condition, determining the N detection devices as the first group of detection devices includes at least one of the following: The sum of the detection areas of the N detection devices is greater than or equal to the target detection area; The sum of the storage sizes of the N detection devices is less than or equal to the target storage size; The sum of the unit prices of the N detection devices is less than or equal to the target total price; The target condition includes at least one of the target detection area, the target storage size, and the target total price.

6. The method according to claim 1, characterized in that Before determining the attribute value of each of the detection devices, the method further includes: Determine whether there is a second group of detection devices matching the target condition in the historical recommendation records, and if so, recommend the second group of detection devices to the target account.

7. The method according to claim 6, characterized in that Determining whether there is a second group of detection devices matching the target condition in the historical recommendation record includes: Obtaining the recommendation condition of each recommendation record in the historical recommendation record; Determining a recommendation condition whose similarity between the recommendation condition and the target condition is greater than or equal to a preset threshold as a target recommendation condition; Determining a recommendation record corresponding to the target recommendation condition as a target recommendation record; The detection devices in the target recommendation record are determined as the second group of detection devices.

8. The method according to claim 1, characterized in that After recommending the first group of detection devices to the target account, the method further includes: In a case where the target account selects the first group of detection devices, the first group of detection devices is recorded in a historical recommendation record.

9. A device for recommending a detection device, characterized in that: include: An acquisition module, used to acquire a target condition input by a target account, wherein the target condition is a condition for selecting a detection device for a target detection area; A determination module, used to determine the attribute value of each detection device based on the attribute information of each detection device in the detection device library; A recommendation module is used to determine a first group of detection devices in the detection device library according to the attribute value and the target condition, and recommend the first group of detection devices to the target account.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 8 when executed by a processor.

11. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

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