Building value evaluation method and system

Abstract

The invention discloses a building value evaluation method and system, and the method comprises the steps: training an Internet private line service type prediction model based on obtained network format data before opening, network protocol data before opening, network performance parameter data before opening and an Internet private line service type; based on the acquired wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the wireless network user number, the wireless network flow data and the fusion service coverage rate, training a fusion service coverage rate prediction model; and predicting a target building by adopting the trained Internet special line business type prediction model and the fusion business coverage rate prediction model to obtain an Internet special line business type prediction result and a fusion business coverage rate prediction result of each enterprise not opening the Internet special line business, and carrying out value evaluation on the target building. According to the method provided by the invention, the building value evaluation efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet business management, and in particular to a building value assessment method and system. Background Art

[0002] Internet operators need to regularly conduct value assessments on target buildings, so that business personnel can judge the service provisioning potential of target buildings when expanding Internet dedicated line services and converged services, so as to reduce the reliance of Internet dedicated line services and converged services on channel and front-line personnel to visit buildings. However, the existing building value assessment generally relies on the subjective experience of front-line business personnel, and there are problems such as inaccurate assessment results and low assessment efficiency, which cannot adapt to the rapid development of the Internet industry.

[0003] It can be seen that how to improve the efficiency and accuracy of building value assessment has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] The present invention provides a building value assessment method and system to solve the technical problem of how to improve the efficiency and accuracy of building value assessment, thereby achieving the effect of improving the efficiency and accuracy of Internet business development.

[0005] In a first aspect, the present invention provides a building value assessment method, the method comprising: obtaining pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type, as well as wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate of enterprises in sample buildings;

[0006] Based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type, training an Internet dedicated line service type prediction model constructed using a back propagation neural network;

[0007] Based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage rate, training a converged service coverage rate prediction model constructed using a deep neural network;

[0008] In the real-time evaluation process, the current network format data, current network protocol data and current network performance parameter data of each enterprise in the target building that has not yet opened an Internet dedicated line service are input into the trained Internet dedicated line service type prediction model to obtain the Internet dedicated line service type prediction result of each of the enterprises that have not yet opened an Internet dedicated line service;

[0009] Based on the current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, the trained converged service coverage prediction model is used to predict the converged service coverage of each target building to obtain a converged service coverage prediction result;

[0010] The Internet dedicated line service type prediction result and the converged service coverage prediction result are analyzed, and the value of the target building is evaluated according to the analysis results to obtain the value evaluation result of the target building.

[0011] Preferably, the training of an Internet dedicated line service type prediction model constructed using a back propagation neural network based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type comprises:

[0012] Constructing a dataset of Internet dedicated line services that have been opened according to the mapping relationship between the pre-opening network format data, the pre-opening network protocol data, and the pre-opening network performance parameter data of the enterprise that has opened the Internet dedicated line service and the Internet dedicated line service type;

[0013] According to the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprise that has not opened the Internet dedicated line service and the demand for no Internet dedicated line service, a data set for the non-opening Internet dedicated line service is constructed;

[0014] The Internet dedicated line service type prediction model is trained using the activated Internet dedicated line service data set and the unactivated Internet dedicated line service data set.

[0015] Preferably, the training of the converged service demand prediction model constructed by using a deep neural network based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage rate includes:

[0016] Using a first ratio of the wireless network signal coverage area data to the high-frequency usage area of ​​the building as the indoor signal coverage ratio;

[0017] Taking the average value of the wireless network uplink signal strength data in each area of ​​each sample building as the first room-based signal strength;

[0018] Taking the average value of the wireless network downlink signal strength data in each area of ​​each sample building as the second room signal strength;

[0019] Calculating a second ratio of the number of wireless network users of each of the sample buildings to the total number of wireless network users of all the sample buildings, and a third ratio of the wireless network user traffic data of each of the sample buildings to the total wireless network traffic data of all the sample buildings, and determining a wireless network demand level according to the sum of the second ratio and the third ratio;

[0020] The indoor signal coverage ratio, the first indoor signal strength, the second indoor signal strength and the wireless network demand level are taken as inputs, and the integrated service coverage rate is taken as output to train the integrated service demand prediction model.

[0021] Preferably, the analyzing the Internet dedicated line service type prediction result and the converged service coverage prediction result, and performing a value assessment on the target building according to the analysis result to obtain the value assessment result of the target building includes:

[0022] According to the Internet dedicated line service type prediction result, the number of enterprises with Internet dedicated line service demand and the number of Internet dedicated line service types in the target building are obtained;

[0023] Calculating a fourth ratio of the number of enterprises to a preset threshold value of the number of enterprises;

[0024] Calculating a fifth ratio of the number of Internet dedicated line service types to the total number of Internet dedicated line service types;

[0025] The fourth ratio, the fifth ratio and the prediction result of the integrated service coverage are weightedly fused, and the target building is classified into value grades according to the weighted fusion result to obtain a value assessment result of the target building.

[0026] Preferably, the method further comprises:

[0027] Sending the value assessment results to Internet business development personnel and the cloud;

[0028] Obtaining the accuracy evaluation of the value assessment result by the Internet business development personnel, and sending the accuracy evaluation result to the cloud;

[0029] Based on the value assessment result and the accuracy evaluation result, the Internet dedicated line service type prediction model and the converged service coverage prediction model are regularly optimized.

[0030] In a second aspect, the present invention further provides a building value assessment system to implement the above-mentioned building value assessment method, the system comprising: a data acquisition unit, a first model training unit, a second model training unit, an Internet dedicated line service type prediction unit, a converged service coverage prediction unit and a building value assessment unit;

[0031] The data collection unit is used to obtain the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprises in the sample buildings, as well as the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate;

[0032] The first model training unit is used to train the Internet dedicated line service type prediction model constructed by using a back propagation neural network based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type;

[0033] The second model training unit is used to train the converged service coverage prediction model constructed by using a deep neural network based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage;

[0034] The Internet dedicated line service type prediction unit is used to input the current network format data, current network protocol data and current network performance parameter data of each enterprise that has not opened the Internet dedicated line service in the target building into the trained Internet dedicated line service type prediction model during the real-time evaluation process, and obtain the Internet dedicated line service type prediction result of each of the enterprises that have not opened the Internet dedicated line service;

[0035] The converged service coverage rate prediction unit is used to predict the converged service coverage rate of each target building by using the trained converged service coverage rate prediction model based on the current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, so as to obtain a converged service coverage rate prediction result;

[0036] The building value assessment unit is used to analyze the Internet dedicated line service type prediction result and the converged service coverage prediction result, and perform value assessment on the target building according to the analysis result to obtain the value assessment result of the target building.

[0037] Pre-selected, the first model training unit is also used to construct a data set for opened Internet dedicated line services based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprises that have opened Internet dedicated line services and the Internet dedicated line service types; construct a data set for unopened Internet dedicated line services based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprises that have not opened Internet dedicated line services and the demand for no Internet dedicated line services; and use the data set for opened Internet dedicated line services and the data set for unopened Internet dedicated line services to train the Internet dedicated line service type prediction model.

[0038] Preferably, the second model training unit is also used to use the first ratio of the wireless network signal coverage area data to the high-frequency usage area of ​​the building as the indoor signal coverage ratio; use the average of the wireless network uplink signal strength data in each area of ​​each sample building as the first indoor signal strength; use the average of the wireless network downlink signal strength data in each area of ​​each sample building as the second indoor signal strength; calculate the second ratio of the number of wireless network users of each sample building to the sum of the number of wireless network users of all the sample buildings, and the third ratio of the wireless network traffic data of each sample building to the sum of the wireless network user traffic data of all the sample buildings, and determine the wireless network demand level according to the sum of the second ratio and the third ratio; use the indoor signal coverage ratio, the first indoor signal strength, the second indoor signal strength and the wireless network demand level as input, and the integrated service coverage rate as output, to train the integrated service demand prediction model.

[0039] Preferably, the building value assessment unit is further used to obtain the number of enterprises with Internet dedicated line service demand and the number of Internet dedicated line service types in the target building based on the Internet dedicated line service type prediction result; calculate a fourth ratio of the number of enterprises to a preset enterprise quantity threshold; calculate a fifth ratio of the number of Internet dedicated line service types to the total number of Internet dedicated line service types; perform weighted fusion on the fourth ratio, the fifth ratio and the converged service coverage prediction result, and classify the target building into value grades according to the weighted fusion result to obtain a value assessment result of the target building.

[0040] Preferably, the system further comprises a model optimization unit;

[0041] The model optimization unit is used to send the value assessment results to Internet business development personnel and the cloud; obtain the accuracy evaluation of the value assessment results by the Internet business development personnel, and send the accuracy evaluation results to the cloud; based on the value assessment results and the accuracy evaluation results, regularly optimize the Internet dedicated line service type prediction model and the converged service coverage prediction model.

[0042] The present invention provides a building value assessment method and system. Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] The Internet service types with Internet dedicated line service demand in the target building are obtained, and the probability of opening integrated services in the target building is predicted, so as to conduct value assessment on each target building and improve the efficiency and accuracy of Internet service expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of steps of a building value assessment method provided by a preferred embodiment of the present invention;

[0045] Figure 2 It is a structural schematic diagram of a building value assessment system provided by a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following is a detailed explanation of the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided only for illustrative purposes and cannot be understood as limiting the present invention. The accompanying drawings are only for reference and illustration purposes and do not constitute a limitation on the scope of patent protection of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "multiple" is two or more.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0049] See also Figure 1 In an embodiment of the present invention, a building value assessment method is provided, the method comprising:

[0050] S1. Obtain the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprises in the sample buildings, as well as the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users and converged service coverage rate; wherein, the sample buildings can be understood as buildings whose building values ​​have been manually assessed by business personnel in the target area and whose Internet dedicated line service and Internet converged service coverage rates have reached the corresponding thresholds; for enterprises that have opened Internet dedicated line services, the pre-opening network format data, pre-opening network protocol data and pre-opening network performance parameter data are the network format data, network protocol data and network performance parameter data before the opening of the Internet dedicated line service, and the Internet dedicated line service type is the Internet dedicated line service type actually opened; for enterprises that have not opened Internet dedicated line services, the pre-opening network format data, pre-opening network protocol data and pre-opening network performance parameter data are the current network format data, network protocol data and network performance parameter data, and the Internet dedicated line service type is that there is no Internet dedicated line service demand.

[0051] In the building value assessment method provided by the preferred embodiment of the present invention, the administrative area is taken as the target area, and the Internet business management resource library data is called to obtain the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprise, as well as the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users and converged service coverage rate of the sample buildings in the target area.

[0052] The network performance parameter data shall at least include: network quality, network demand and network security. Network quality shall at least include: network delay, network packet loss rate, network rate, channel utilization, throughput and network stability; network demand shall at least include: network broadband package charges, which are divided into high, medium and low charges; network security shall at least include: network security level.

[0053] Network security level refers to the classification of information systems or network security into different levels through security risk assessment of information systems or networks in order to meet the needs of information system security protection work, and the adoption of corresponding network security measures and management systems according to different security needs. Network security levels are divided into five levels, from level one to level five, and the protection level increases step by step.

[0054] S2. Based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type, the Internet dedicated line service type prediction model constructed by using a back propagation neural network is trained; wherein, in principle, the construction of the Internet dedicated line service type prediction model can be obtained by using an existing machine learning model construction method that can realize Internet dedicated line service prediction, but in order to ensure the efficiency, accuracy and generalization of the prediction analysis, this embodiment preferably adopts a back propagation neural network model, and constructs an activated Internet dedicated line service data set based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the Internet dedicated line service enterprises and the Internet dedicated line service type, and constructs an unactivated Internet dedicated line service data set based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the Internet dedicated line service enterprises and the demand for no Internet dedicated line service. Taking the pre-opening network format data, pre-opening network protocol data and pre-opening network performance parameter data of the opened Internet dedicated line service data set and the unopened Internet dedicated line service data set as input, and the corresponding Internet dedicated line service type or no Internet dedicated line service demand as output, the Internet dedicated line service type prediction model constructed by using the back propagation neural network is trained to obtain a trained Internet dedicated line service type prediction model.

[0055] S3. Based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the integrated service coverage rate, the integrated service coverage rate prediction model constructed by using a deep neural network is trained; wherein the wireless network signal coverage area data is the total signal coverage range of the distributed wireless network system in each sample building, such as the indoor distributed wireless network system coverage range of Building A is 800m 2 The wireless network uplink signal strength data is the wireless network uplink signal strength of the indoor wireless network system in each area of ​​the sample building, and the wireless network downlink signal strength data is the wireless network downlink signal strength of the indoor wireless network system in each area of ​​the sample building. For example, Table 1 shows the indoor distribution system signal strength of each area in Building A.

[0056] Table 1

[0057] Floor of Building A Area a Area b Area c 1st floor 4M / s 3.5M / s 10M / s 2nd Floor 1M / s 3M / s 5M / s …… …… …… …… N Building xM / s yM / s zM / s

[0058] For the number of wireless network users, the IPs that use the network for more than 4 hours per day in a building are defined as wireless network users in the building. The number of wireless network users in each building is recorded as A. The mobile traffic packages of the wireless network users in each sample building are obtained to obtain the wireless network traffic data B corresponding to each sample building. The sum of the number of wireless network users A in each sample building is the total number of wireless network users S, and the sum of the wireless network traffic data B in each sample building is the total wireless network traffic data D.

[0059] Get the area of ​​each floor of each sample building. The sum of the areas other than the elevators in the building and the restrooms on each floor is the high-frequency use area of ​​the corresponding sample building. For example, the high-frequency use area of ​​Building A is 750.

[0060] The training process of the converged service coverage prediction model built using a deep neural network includes:

[0061] Based on the wireless network signal coverage area data, high-frequency usage area, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users and wireless network traffic data of each sample building, the indoor signal coverage ratio, first room signal strength, second room signal strength and wireless network demand level of each building are obtained.

[0062] Specifically, a first ratio of the wireless network signal coverage area data to the high-frequency usage area of ​​the building is used as the indoor signal coverage ratio of the building. For example, the indoor signal coverage ratio of Building A is 750 / 800=0.94.

[0063] The average value of the wireless network uplink signal strength data in each area of ​​each sample building is used as the first room signal strength of the building.

[0064] The average value of the wireless network downlink signal strength data in each area of ​​each sample building is used as the second room signal strength of the building.

[0065] Calculate the second ratio of the number of wireless network users of each sample building to the total number of wireless network users of all sample buildings, and the third ratio of the wireless network user traffic data of each sample building to the total wireless network user traffic data of all sample buildings, and calculate the sum of the second ratio and the third ratio. The calculation formula is:

[0066]

[0067] According to the size of P value, the wireless network demand of the sample building is divided into three levels: high demand, medium demand and low demand, and the wireless network demand level of the building is determined.

[0068] The coverage rate of integrated services is the ratio of the number of enterprises that have activated building integrated services to the total number of building enterprises.

[0069] The indoor signal coverage ratio, first indoor signal strength, second indoor signal strength and wireless network demand level of all sample buildings are taken as input, and the corresponding integrated service coverage rate is taken as output. The building integrated service activation prediction model constructed by deep neural network is trained to obtain the trained building integrated service activation prediction model.

[0070] S4. During the real-time evaluation process, the current network format data, current network protocol data and current network performance parameter data of each enterprise in the target building that has not yet opened the Internet dedicated line service are input into the trained Internet dedicated line service type prediction model to obtain the Internet dedicated line service type prediction result of each enterprise that has not yet opened the Internet dedicated line service; wherein, the building to be evaluated is taken as the target building, and the current network format data, current network protocol data and current network performance parameter data collected from each enterprise in the target building that has not yet opened the Internet dedicated line service are input into the trained enterprise Internet dedicated line service demand prediction model to obtain the Internet dedicated line service type prediction result of each enterprise that has not yet opened the Internet dedicated line service. The Internet dedicated line service type prediction result includes the specific type of Internet service or the absence of Internet dedicated line service demand.

[0071] S5. Based on the current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, the trained integrated service coverage prediction model is used to predict the integrated service coverage of each target building to obtain the integrated service coverage prediction result; according to the collected current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, the current indoor signal coverage ratio, the first indoor signal strength, the first indoor signal strength and the wireless network demand level of the target building are calculated, and the trained building integrated service activation prediction model is input to output the integrated service coverage prediction result of each target building.

[0072] S6. Analyze the prediction results of the Internet dedicated line service type and the prediction results of the integrated service coverage rate, and perform value assessment on the target building according to the analysis results to obtain the value assessment result of the target building; specifically, according to the prediction results of the Internet dedicated line service type, obtain the number of enterprises with Internet dedicated line service demand and the number of Internet dedicated line service types in the target building; the number of enterprises with Internet dedicated line service demand includes enterprises that have opened Internet dedicated line services and enterprises whose prediction results are specific Internet dedicated line services; calculate a fourth ratio of the number of enterprises to a preset enterprise quantity threshold; further, according to the prediction results of the Internet dedicated line service type, count the number of Internet dedicated line service types, and calculate a fifth ratio of the number of Internet dedicated line service types to the total number of Internet dedicated line service types; perform weighted fusion on the fourth ratio, the fifth ratio and the prediction results of the integrated service coverage rate, and classify the target building into value grades according to the weighted fusion results to obtain the value assessment result of the target building, and the formula used for weighted fusion is:

[0073] Q=αC1+βC2+χC3

[0074] Among them, Q represents the weighted fusion result, C1 represents the fourth ratio, C2 represents the fifth ratio, C3 represents the prediction result of the fusion service coverage rate, α, β, and χ are weighted fusion coefficients, and α+β+χ=1. The values ​​of α, β, and χ are obtained by fitting according to the relevant data of the sample buildings.

[0075] The target building is divided into value levels according to the weighted fusion results. The weighted fusion results are judged according to the pre-set value ranges of different value levels and the range intervals of multiple weighted fusion results to obtain the value assessment result of the target building. The setting of the value assessment results can be specifically set according to needs. In the preferred embodiment of the present application, the value assessment results include: high value, medium value and low value.

[0076] In a preferred embodiment of the present invention, after obtaining the value assessment results of the target building, the value assessment results are sent to Internet business development personnel and the cloud. Internet business development personnel give priority to promoting Internet business to high-value buildings based on the value assessment results, thereby improving the efficiency and accuracy of Internet business development.

[0077] Internet business development personnel evaluate the accuracy of the value assessment results based on the actual situation during the Internet business development process, and send the accuracy evaluation results to the cloud.

[0078] Based on the value assessment results and accuracy evaluation results, the parameters of the Internet dedicated line service type prediction model and the converged service coverage prediction model are optimized regularly to improve the accuracy of the Internet dedicated line service type prediction and the converged service coverage prediction.

[0079] In a preferred embodiment of the present invention, the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprises in the sample building, as well as the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate are obtained; based on the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type, the Internet dedicated line service type prediction model constructed by using a back propagation neural network is trained; based on the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate, the converged service coverage rate prediction model constructed by using a deep neural network is trained; In the real-time evaluation process, the current network format data, current network protocol data and current network performance parameter data of each enterprise in the target building that has not opened the Internet dedicated line service are input into the trained Internet dedicated line service type prediction model to obtain the Internet dedicated line service type prediction result of each enterprise that has not opened the Internet dedicated line service; based on the current wireless network signal coverage area data, current wireless network uplink signal strength data, current wireless network downlink signal strength data, current wireless network traffic data and current number of wireless network users of the target building, the trained converged service coverage rate prediction model is used to predict the converged service coverage rate of each target building to obtain the converged service coverage rate prediction result; the Internet dedicated line service type prediction result and the converged service coverage rate prediction result are analyzed, and the target building is valued according to the analysis result to obtain the value evaluation result of the target building. The building value evaluation method provided in the present application obtains the Internet service type with Internet dedicated line service demand in the target building, predicts the probability of the target building opening the converged service, so as to perform value evaluation on each target building and improve the efficiency and accuracy of Internet service expansion.

[0080] Accordingly, if Figure 2 As shown, based on a building value assessment method, an embodiment of the present invention further provides a building value assessment system to implement the building value assessment method disclosed in the embodiment of the present invention, the system comprising: a data acquisition unit 1, a first model training unit 2, a second model training unit 3, an Internet dedicated line service type prediction unit 4, a converged service coverage prediction unit 5 and a building value assessment unit 6;

[0081] The data collection unit 1 is used to obtain the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprises in the sample building, as well as the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate;

[0082] The first model training unit 2 is used to train the Internet dedicated line service type prediction model constructed by using a back propagation neural network based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type;

[0083] The second model training unit 3 is used to train the converged service coverage prediction model constructed by using a deep neural network based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage;

[0084] The Internet dedicated line service type prediction unit 4 is used to input the current network format data, current network protocol data and current network performance parameter data of each enterprise that has not opened the Internet dedicated line service in the target building into the trained Internet dedicated line service type prediction model during the real-time evaluation process, and obtain the Internet dedicated line service type prediction result of each enterprise that has not opened the Internet dedicated line service;

[0085] The converged service coverage rate prediction unit 5 is used to predict the converged service coverage rate of each target building based on the current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, using the trained converged service coverage rate prediction model to obtain a converged service coverage rate prediction result;

[0086] The building value assessment unit 6 is used to analyze the Internet dedicated line service type prediction result and the converged service coverage prediction result, and perform value assessment on the target building according to the analysis result to obtain the value assessment result of the target building.

[0087] Among them, the first model training unit 2 is also used to construct a data set for opened Internet dedicated line services based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprises that have opened Internet dedicated line services and the Internet dedicated line service types; to construct a data set for unopened Internet dedicated line services based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprises that have not opened Internet dedicated line services and the demand for no Internet dedicated line services; and to train the Internet dedicated line service type prediction model using the data set for opened Internet dedicated line services and the data set for unopened Internet dedicated line services.

[0088] The second model training unit 3 is also used to use the first ratio of the wireless network signal coverage area data to the high-frequency usage area of ​​the building as the indoor signal coverage ratio; use the average of the wireless network uplink signal strength data in each area of ​​each sample building as the first indoor signal strength; use the average of the wireless network downlink signal strength data in each area of ​​each sample building as the second indoor signal strength; calculate the second ratio of the number of wireless network users in each sample building to the sum of the number of wireless network users in all the sample buildings, and the third ratio of the wireless network user traffic data in each sample building to the sum of the wireless network user traffic data in all the sample buildings, and determine the wireless network demand level according to the sum of the second ratio and the third ratio; use the indoor signal coverage ratio, the first indoor signal strength, the second indoor signal strength and the wireless network demand level as input, and the integrated service coverage rate as output, to train the integrated service demand prediction model.

[0089] The building value assessment unit 6 is further used to obtain the number of enterprises with Internet dedicated line service demand and the number of Internet dedicated line service types in the target building according to the Internet dedicated line service type prediction result; calculate a fourth ratio of the number of enterprises to a preset enterprise quantity threshold; calculate a fifth ratio of the number of Internet dedicated line service types to the total number of Internet dedicated line service types; perform weighted fusion on the fourth ratio, the fifth ratio and the converged service coverage prediction result, and classify the target building into value grades according to the weighted fusion result to obtain a value assessment result of the target building.

[0090] The system also includes a model optimization unit;

[0091] The model optimization unit is used to send the value assessment results to Internet business development personnel and the cloud; obtain the accuracy evaluation of the value assessment results by the Internet business development personnel, and send the accuracy evaluation results to the cloud; based on the value assessment results and the accuracy evaluation results, regularly optimize the Internet dedicated line service type prediction model and the converged service coverage prediction model.

[0092] For the specific definition of a building value assessment system, please refer to the above-mentioned definition of a building value assessment method, which will not be repeated here. A person of ordinary skill in the art can appreciate that the various modules and steps described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0093] A building value assessment method and system provided in this embodiment are used to solve the technical problem of how to improve the efficiency and accuracy of building value assessment. The building value assessment method of the present application includes: obtaining the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprises in the sample building, as well as wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate; based on the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type, the Internet dedicated line service type prediction model constructed using a back propagation neural network is trained; based on the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate, the converged service coverage rate prediction model constructed using a deep neural network is trained. Practice; in the real-time evaluation process, the current network format data, current network protocol data and current network performance parameter data of each enterprise in the target building that has not opened the Internet dedicated line service are input into the trained Internet dedicated line service type prediction model to obtain the Internet dedicated line service type prediction result of each enterprise that has not opened the Internet dedicated line service; based on the current wireless network signal coverage area data, current wireless network uplink signal strength data, current wireless network downlink signal strength data, current wireless network traffic data and current number of wireless network users of the target building, the trained converged service coverage rate prediction model is used to predict the converged service coverage rate of each target building to obtain the converged service coverage rate prediction result; the Internet dedicated line service type prediction result and the converged service coverage rate prediction result are analyzed, and the target building is valued according to the analysis result to obtain the value evaluation result of the target building. The building value evaluation method provided in the present application obtains the Internet service type with Internet dedicated line service demand in the target building, predicts the probability of the target building opening the converged service, so as to perform value evaluation on each target building and improve the efficiency and accuracy of Internet service expansion.

[0094] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] The above-mentioned embodiments only express several preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principle of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the protection scope of the claims.

Claims

1. A building value assessment method, characterized in that: The method comprises: Obtain the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service types of enterprises in sample buildings, as well as wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate; Based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type, training an Internet dedicated line service type prediction model constructed using a back propagation neural network; Based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage rate, training a converged service coverage rate prediction model constructed using a deep neural network; In the real-time evaluation process, the current network format data, current network protocol data and current network performance parameter data of each enterprise in the target building that has not yet opened an Internet dedicated line service are input into the trained Internet dedicated line service type prediction model to obtain the Internet dedicated line service type prediction result of each of the enterprises that have not yet opened an Internet dedicated line service; Based on the current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, the trained converged service coverage prediction model is used to predict the converged service coverage of each target building to obtain a converged service coverage prediction result; The Internet dedicated line service type prediction result and the converged service coverage prediction result are analyzed, and the value of the target building is evaluated according to the analysis results to obtain the value evaluation result of the target building.

2. The building value assessment method according to claim 1, characterized in that: The training of the Internet dedicated line service type prediction model constructed by using a back propagation neural network based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type comprises: Constructing a dataset of Internet dedicated line services that have been opened according to the mapping relationship between the pre-opening network format data, the pre-opening network protocol data, and the pre-opening network performance parameter data of the enterprise that has opened the Internet dedicated line service and the Internet dedicated line service type; According to the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprise that has not opened the Internet dedicated line service and the demand for no Internet dedicated line service, a data set for the non-opening Internet dedicated line service is constructed; The Internet dedicated line service type prediction model is trained using the activated Internet dedicated line service data set and the unactivated Internet dedicated line service data set.

3. The building value assessment method according to claim 1, characterized in that: The training of the converged service demand prediction model constructed by using a deep neural network based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage rate includes: Using a first ratio of the wireless network signal coverage area data to the high-frequency usage area of ​​the building as the indoor signal coverage ratio; Taking the average value of the wireless network uplink signal strength data in each area of ​​each sample building as the first room-based signal strength; Taking the average value of the wireless network downlink signal strength data in each area of ​​each sample building as the second room signal strength; Calculating a second ratio of the number of wireless network users of each of the sample buildings to the total number of wireless network users of all the sample buildings, and a third ratio of the wireless network traffic data of each of the sample buildings to the total traffic data of the wireless network users of all the sample buildings, and determining a wireless network demand level according to the sum of the second ratio and the third ratio; The indoor signal coverage ratio, the first indoor signal strength, the second indoor signal strength and the wireless network demand level are taken as inputs, and the integrated service coverage rate is taken as output to train the integrated service demand prediction model.

4. The building value assessment method according to claim 1, characterized in that: The analyzing the Internet dedicated line service type prediction result and the converged service coverage prediction result, and performing value assessment on the target building according to the analysis result to obtain the value assessment result of the target building includes: According to the Internet dedicated line service type prediction result, the number of enterprises with Internet dedicated line service demand and the number of Internet dedicated line service types in the target building are obtained; Calculating a fourth ratio of the number of enterprises to a preset threshold value of the number of enterprises; Calculating a fifth ratio of the number of Internet dedicated line service types to the total number of Internet dedicated line service types; The fourth ratio, the fifth ratio and the prediction result of the integrated service coverage are weightedly fused, and the target building is classified into value grades according to the weighted fusion result to obtain a value assessment result of the target building.

5. The building value assessment method according to claim 1, characterized in that: The method further comprises: Sending the value assessment results to Internet business development personnel and the cloud; Obtaining the accuracy evaluation of the value assessment result by the Internet business development personnel, and sending the accuracy evaluation result to the cloud; Based on the value assessment result and the accuracy evaluation result, the Internet dedicated line service type prediction model and the converged service coverage prediction model are regularly optimized.

6. A building value assessment system, implementing the building value assessment method according to any one of claims 1 to 5, characterized in that: The system comprises: a data collection unit, a first model training unit, a second model training unit, an Internet dedicated line service type prediction unit, a converged service coverage prediction unit and a building value assessment unit; The data collection unit is used to obtain the pre-opening network format data, pre-opening network protocol data, pre-opening network performance parameter data and Internet dedicated line service type of the enterprises in the sample buildings, as well as the wireless network signal coverage area data, wireless network uplink signal strength data, wireless network downlink signal strength data, number of wireless network users, wireless network traffic data and converged service coverage rate; The first model training unit is used to train the Internet dedicated line service type prediction model constructed by using a back propagation neural network based on the pre-opening network format data, the pre-opening network protocol data, the pre-opening network performance parameter data and the Internet dedicated line service type; The second model training unit is used to train the converged service coverage prediction model constructed by using a deep neural network based on the wireless network signal coverage area data, the wireless network uplink signal strength data, the wireless network downlink signal strength data, the number of wireless network users, the wireless network traffic data and the converged service coverage; The Internet dedicated line service type prediction unit is used to input the current network format data, current network protocol data and current network performance parameter data of each enterprise that has not opened the Internet dedicated line service in the target building into the trained Internet dedicated line service type prediction model during the real-time evaluation process, and obtain the Internet dedicated line service type prediction result of each of the enterprises that have not opened the Internet dedicated line service; The converged service coverage rate prediction unit is used to predict the converged service coverage rate of each target building by using the trained converged service coverage rate prediction model based on the current wireless network signal coverage area data, the current wireless network uplink signal strength data, the current wireless network downlink signal strength data, the current wireless network traffic data and the current number of wireless network users of the target building, so as to obtain a converged service coverage rate prediction result; The building value assessment unit is used to analyze the Internet dedicated line service type prediction result and the converged service coverage prediction result, and perform value assessment on the target building according to the analysis result to obtain the value assessment result of the target building.

7. The building value assessment system according to claim 6, characterized in that: The first model training unit is further used to construct a data set for opened Internet dedicated line services based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprises that have opened Internet dedicated line services and the Internet dedicated line service types; to construct a data set for unopened Internet dedicated line services based on the mapping relationship between the pre-opening network format data, the pre-opening network protocol data and the pre-opening network performance parameter data of the enterprises that have not opened Internet dedicated line services and the demand for no Internet dedicated line services; and to train the Internet dedicated line service type prediction model using the data set for opened Internet dedicated line services and the data set for unopened Internet dedicated line services.

8. The building value assessment system according to claim 6, characterized in that: The second model training unit is further used to use the first ratio of the wireless network signal coverage area data to the high-frequency use area of ​​the building as the indoor signal coverage ratio; use the average value of the wireless network uplink signal strength data in each area of ​​each sample building as the first indoor signal strength; use the average value of the wireless network downlink signal strength data in each area of ​​each sample building as the second indoor signal strength; Calculating a second ratio of the number of wireless network users of each of the sample buildings to the total number of wireless network users of all the sample buildings, and a third ratio of the wireless network traffic data of each of the sample buildings to the total traffic data of the wireless network users of all the sample buildings, and determining a wireless network demand level according to the sum of the second ratio and the third ratio; The indoor signal coverage ratio, the first indoor signal strength, the second indoor signal strength and the wireless network demand level are taken as inputs, and the integrated service coverage rate is taken as output to train the integrated service demand prediction model.

9. The building value assessment system according to claim 6, characterized in that: The building value assessment unit is further used to obtain the number of enterprises with Internet dedicated line service demand and the number of Internet dedicated line service types in the target building according to the Internet dedicated line service type prediction result; calculate a fourth ratio of the number of enterprises to a preset enterprise quantity threshold; calculate a fifth ratio of the number of Internet dedicated line service types to the total number of Internet dedicated line service types; The fourth ratio, the fifth ratio and the prediction result of the integrated service coverage are weightedly fused, and the target building is classified into value grades according to the weighted fusion result to obtain a value assessment result of the target building.

10. The building value assessment system according to claim 6, characterized in that: The system also includes a model optimization unit; The model optimization unit is used to send the value assessment result to the Internet business development personnel and the cloud; obtain the accuracy evaluation of the value assessment result by the Internet business development personnel, and send the accuracy evaluation result to the cloud; Based on the value assessment result and the accuracy evaluation result, the Internet dedicated line service type prediction model and the converged service coverage prediction model are regularly optimized.

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