Communication pipeline asset intelligent protection method and system

Abstract

The invention provides an intelligent protection method and system for communication pipeline assets. The protection method comprises the following steps: compiling a communication pipeline asset risk tag and carrying out tag configuration on a communication manhole; collecting communication pipeline asset state data; updating a communication pipeline asset risk tag based on the communication pipeline asset state data; based on the updated communication pipeline asset risk tag, calculating a communication pipeline asset risk probability by a pre-constructed communication pipeline asset risk assessment model; identifying a communication manhole identifier of which the communication pipeline asset risk probability exceeds a threshold value based on a communication pipeline asset risk probability calculation result; generating early warning information according to the communication manhole identifier exceeding the threshold value; and according to the early warning information, the maintainer checks and disposes the communication manhole exceeding the threshold value and feeds back early warning disposition result data, and the above steps are executed circularly. According to the intelligent protection method and system provided by the invention, full-period communication pipeline asset intelligent protection of pre-judgment in advance, alarm processing in the event and responsibility investigation after the event is realized on the management process level.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, in particular to underground communication pipelines, and specifically to a method and system for intelligent protection of communication pipeline assets. Background Art

[0002] With the development of the Internet and cloud computing technology, information networks have become an indispensable and important resource in people's lives, urban governance, and the development of all walks of life, and communication pipelines and optical cables have become necessary conditions for ensuring the smooth flow of information networks. At the same time, in the rapid development of urbanization, communication pipelines, as one of the core assets of operators, are often illegally infringed by theft and private penetration, causing significant asset losses to operators. There are several existing communication pipeline asset protection management modes: The first is the traditional mode, which relies entirely on regular manual inspections by pipeline patrol personnel. The second is to install a manhole cover status sensor on the manhole cover. The manhole cover status sensor integrates a gyroscope, angle, and vibration sensor, which can alarm the displacement and vibration of the manhole cover. When the state of the manhole cover changes, the state of the manhole cover is transmitted back to the management platform through the wireless network. The third is to install a manhole cover electronic lock on the manhole cover, complete the opening and closing operations of the manhole cover lock wirelessly, and transmit the change of the manhole cover state back to the management platform through the wireless network.

[0003] However, the above three solutions all have defects and are insufficient in the ability to protect communication pipeline assets, and cannot achieve effective protection of communication pipeline assets. Among them, the first solution cannot detect theft and private cable penetration in a timely and accurate manner because it relies entirely on manual inspections. The second solution cannot distinguish between normal construction and illegal operations, so there is no effective asset protection. Although the third solution can distinguish between normal construction and illegal operations through the operation authentication of the manhole cover lock, it is unable to predict and warn of illegal behaviors due to the lack of effective intelligent means. At the same time, due to the variety of illegal operation methods, the high false alarm rate of intelligent manhole covers, and the weak anti-smashing and anti-prying capabilities of equipment, the protection of communication assets is still in the post-discovery and post-processing stage, and it is impossible to achieve the full-cycle intelligent protection of communication pipeline assets from advance prediction, in-process warning, and post-event accountability at the technical level. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligent protection of communication pipeline assets, which solves the problems of various illegal operation methods in communication manholes, high false alarm rate of smart manhole covers, weak anti-smashing and anti-pry capabilities of equipment, etc., while avoiding the protection of communication assets remaining in the post-discovery and post-processing stages, and realizing full-cycle intelligent protection of communication pipeline assets including prior prediction, in-process warning and post-event accountability at the management process level.

[0005] The technical solution of the present invention is as follows:

[0006] The present invention provides a communication pipeline asset intelligent protection method, comprising the following steps:

[0007] Prepare risk labels for communication pipeline assets and configure labels for communication manholes;

[0008] Collect communication pipeline asset status data in real time;

[0009] Based on the communication pipeline asset status data, update the communication pipeline asset risk label;

[0010] Based on the updated communication pipeline asset risk label, the communication pipeline asset risk probability is calculated by the pre-built communication pipeline asset risk assessment model;

[0011] Based on the communication pipeline asset risk probability calculation results, identify the communication manhole marks where the communication pipeline asset risk probability exceeds the threshold;

[0012] Generate warning information based on communication manhole identification exceeding the threshold;

[0013] According to the early warning information, the maintenance personnel inspect and deal with the communication manholes that exceed the threshold, and feedback the early warning treatment result data, and repeat the above steps.

[0014] In some achievable embodiments, the step of compiling a communication pipeline asset risk tag includes:

[0015] Nine risk indicators related to communication pipeline asset risks are set according to the expert decision-making method, including: regional market popularity, communication pipeline asset value, existing maintenance capabilities, manhole accessibility, on-site operation difficulty, manhole resource leasing factors, time factors, event factors and illegal occupation factors;

[0016] The nine risk indicators are quantified according to quantifiable assessment factors, and the communication pipeline asset risk label is compiled based on the quantification results.

[0017] In some achievable embodiments, the communication pipeline asset status data includes: one or more of pipeline asset illegal occupation data, pipeline resource data, pipeline lease data, and event data.

[0018] In some achievable embodiments, calculating the communication pipeline asset risk probability by using a pre-built communication pipeline asset risk assessment model includes:

[0019] According to the number of parameters contained in the risk label of the communication pipeline asset, the number of neurons in the input layer of the BP neural network model is determined;

[0020] Construct a BP neural network model according to the number of input layer neurons, the preset hidden layer function and the preset output function;

[0021] Obtain a training set, train the BP neural network model through training samples and preset training functions, and obtain a communication pipeline asset risk assessment model;

[0022] The communication pipeline asset risk label is input into the communication pipeline asset risk assessment model to evaluate the communication pipeline asset risk probability.

[0023] In some feasible embodiments, the BP neural network model includes two hidden layers; the structure of the double hidden layer BP neural network is 9-9-1-1, that is, the number of the first hidden layer is 9, and the number of the second hidden layer is 1.

[0024] In some achievable embodiments, the number of neurons in the input layer of the BP neural network model is 9, the number of neurons in the output layer is 1, and the number of neurons in the hidden layer is G; the number of neurons in any input layer is f i , i∈(1, 2…9); any hidden layer neuron is G j , j∈(1, 2…G); any output layer neuron is h k , k∈(1, 2);

[0025] The number of neurons G in the hidden layer is calculated as follows:

[0026]

[0027] Among them, f is the number of neurons in the input layer, h is the number of neurons in the output layer, and a is a constant, which is rounded between 1 and 10 and satisfies the value range of the number of neurons G in the hidden layer between 4 and 14.

[0028] In some feasible embodiments, the transfer function from the input layer to the first hidden layer adopts the Sigmoid function, the transfer function from the first hidden layer to the second hidden layer adopts the purelin function, and the transfer function from the second hidden layer to the output layer adopts the purelin function.

[0029] In some achievable embodiments, the training set includes: a certain amount of training samples and communication pipeline asset risk labels corresponding to the training samples.

[0030] In some achievable embodiments, determining the risk probability of communication pipeline assets through risk factor analysis includes:

[0031] Obtaining a risk main cause indicator, wherein the risk main cause indicator is a risk indicator whose cumulative variance contribution rate reaches more than 80% among the 9 risk indicators in the training set;

[0032] The score coefficient matrix of the risk main cause indicator is used to calculate the score of the risk main cause indicator, and the weight of the risk main cause indicator is determined by the proportion of the risk main cause indicator to the cumulative variance contribution rate;

[0033] The comprehensive score of the main risk factors is calculated based on the weights and scores of the main risk factor indicators, which is the risk probability of communication pipeline assets.

[0034] The present invention also provides a communication pipeline asset intelligent protection system for the above protection method, comprising:

[0035] The communication pipeline asset intelligent protection device is used to collect data on illegal occupation of communication pipeline assets and push it to the communication pipeline asset intelligent monitoring platform;

[0036] The communication pipeline asset intelligent monitoring platform is used to receive the communication pipeline asset status data reported by the communication pipeline asset intelligent protection device to update the communication pipeline asset risk label. The communication pipeline asset intelligent monitoring platform also generates early warning information based on the communication manhole identification exceeding the risk probability threshold and pushes it to the maintenance terminal;

[0037] The communication pipeline asset risk assessment platform is used to receive the updated communication pipeline asset risk tags pushed by the communication pipeline asset intelligent monitoring platform, calculate the communication pipeline asset risk probability through the communication pipeline asset risk assessment model, and push the communication manhole identification that exceeds the risk probability threshold to the communication pipeline asset intelligent monitoring platform;

[0038] Maintenance terminal, used to receive warning information pushed by the communication pipeline asset intelligent monitoring platform and display it to maintenance personnel;

[0039] A third-party system is used to push the communication pipeline asset status data to the communication pipeline asset intelligent monitoring platform according to a pre-set mode;

[0040] Maintenance personnel will inspect and handle communication manholes that exceed the risk probability threshold, and report the early warning and disposal result data to the communication pipeline asset intelligent monitoring platform through the maintenance terminal.

[0041] The beneficial effects of the present invention over the prior art are as follows: the present invention provides a method and system for intelligent protection of communication pipeline assets, which realizes full-cycle intelligent protection of communication pipeline assets, including pre-judgment, in-process warning, and post-event accountability, by coordinating the functions of communication pipeline asset risk tags, communication pipeline asset protection devices, communication pipeline asset intelligent monitoring platforms, and communication pipeline asset risk assessment platforms. The specific advantages include at least one or more of the following:

[0042] (1) The intelligent protection method for communication pipeline assets provided by the present invention proposes risk labels for communication pipeline assets and classifies and scores them. The risk assessment label classification and scoring proposes practical risk assessment quantitative data for communication manholes, which can effectively realize risk assessment and further standardize the risk level of communication pipeline assets.

[0043] (2) The intelligent protection method for communication pipeline assets provided by the present invention constructs a risk assessment model for communication pipeline assets. By utilizing the BP neural network model, quantitative prediction of the risk probability of communication pipeline assets is achieved. The result is more objective and avoids prediction errors caused by human judgment.

[0044] (3) The intelligent protection method for communication pipeline assets provided by the present invention adopts a double hidden layer BP neural network in constructing a communication pipeline asset risk assessment model, and determines the number of neurons in the input layer, output layer, and hidden layer according to the communication pipeline asset risk label, and selects a transfer function so that the model can achieve the best prediction effect.

[0045] (4) The intelligent protection method for communication pipeline assets provided by the present invention, in the model training, combines the communication pipeline asset risk labels (9) to calculate the communication pipeline asset risk probability through risk main cause analysis, and the calculation result is more practical and feasible.

[0046] (5) The intelligent protection system for communication pipeline assets constructed by the present invention realizes intelligent perception of external force damage to communication pipeline assets based on intelligent protection devices, timely processing and evaluation update of communication pipeline asset alarm information based on intelligent monitoring platforms and risk assessment platforms, and on-site dispatch and processing of alarm information based on maintenance terminals. The intelligent protection system can effectively realize dynamic evaluation and precise maintenance of risk assets in application scenarios.

[0047] It should be understood that the implementation of any embodiment of the present invention does not mean that multiple or all of the above-mentioned beneficial effects must be possessed or achieved at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0049] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment in size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0050] Figure 1 This is a schematic diagram of the overall process of a communication pipeline asset intelligent protection method according to an embodiment of the present invention;

[0051] Figure 2 A flow chart of a method for constructing a communication pipeline asset risk assessment model according to an embodiment of the present invention;

[0052] Figure 3 The present invention is a schematic diagram of an application scenario of a communication pipeline asset intelligent protection system according to an embodiment of the present invention.

[0053] Markings in the figure:

[0054] 100. Intelligent protection method for communication pipeline assets;

[0055] 300. Communication pipeline asset intelligent protection system; 310. Communication pipeline asset risk assessment platform; 320. Communication pipeline asset intelligent monitoring platform; 330. Maintenance terminal; 340. Communication pipeline asset intelligent protection device; 350. Maintenance personnel; 360. Third-party system.

[0056] The same or corresponding symbols in the drawings indicate the same or corresponding parts. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with the embodiments and drawings. Here, the illustrative embodiments of the present invention and their description are used to explain the present invention, but are not intended to limit the present invention.

[0058] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like 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 an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] It should be understood that the terms "include / comprise", "consist of..." or any other variations are intended to cover non-exclusive inclusion, so that a product, device, process or method that includes a series of elements includes not only those elements, but also may include other elements not explicitly listed when necessary, or also includes elements inherent to such product, device, process or method. In the absence of more restrictions, the elements defined by the sentence "include / comprise...", "consist of..." do not exclude the presence of other identical elements in the product, device, process or method that includes the elements.

[0060] It is also necessary to understand that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" to indicate directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device, component or structure must have a specific direction, be constructed or operate in a specific direction, and should not be understood as a limitation on the present invention.

[0061] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0062] At present, the protection of communication manhole assets is still at the post-processing stage, lacking the prediction of illegal occupation, and unable to make advance prediction, in-process disposal and post-event accountability. In view of this, in order to achieve the purpose of full-cycle intelligent protection of communication pipeline assets with advance prediction, in-process warning and post-event accountability, the present invention provides a communication pipeline asset intelligent protection method and system to solve the existing problems and meet engineering needs.

[0063] The implementation of the present invention is described in detail below in conjunction with preferred embodiments.

[0064] See also Figure 1 As shown in the flowchart, the communication pipeline asset intelligent protection method 100 includes the following steps: S10, compile the communication pipeline asset risk label and configure the communication manhole label; S11, collect the communication pipeline asset status data; S12, based on the communication pipeline asset status data, update the communication pipeline asset risk label; S13, based on the updated communication pipeline asset risk label, calculate the communication pipeline asset risk probability by the pre-built communication pipeline asset risk assessment model; S14, based on the communication pipeline asset risk probability calculation result, identify the communication pipeline asset risk probability exceeding the threshold value of the communication manhole identification; S15, generate early warning information according to the communication manhole identification exceeding the threshold value; S16, according to the early warning information, the maintenance personnel inspect and deal with the communication manhole exceeding the threshold value, and feedback the early warning treatment result data, and repeat the step S11. The intelligent protection method provided by the present invention realizes the full-cycle communication pipeline asset intelligent protection of advance prediction, in-process warning, and post-event accountability at the management process level.

[0065] The specific implementation of the method steps of the present invention is described in detail below.

[0066] S10, compile risk labels for communication pipeline assets and configure labels for communication manholes.

[0067] It should be noted that the risk of communication pipeline assets is related to the following nine parameters: regional market heat, communication pipeline asset value, existing maintenance capabilities, manhole accessibility, difficulty of on-site operations, resource leasing factors in manholes, time factors, event factors, and illegal occupation factors.

[0068] Therefore, the preparation of risk labels for communication pipeline assets includes:

[0069] According to the expert decision-making method, nine risk indicators related to the risk of communication pipeline assets are set, including: regional market popularity, communication pipeline asset value, existing maintenance capabilities, manhole accessibility, on-site operation difficulty, manhole resource leasing factors, time factors, event factors and illegal occupation factors;

[0070] The nine risk indicators are quantified according to quantifiable assessment factors, and the communication pipeline asset risk label is compiled based on the quantification results.

[0071] When performing quantitative processing, for example, scoring can be performed based on experience. The embodiment of the present invention compiles communication pipeline asset risk label data based on the quantitative processing results. The quantification process is converted using a scoring table, which is shown in Table 1 below.

[0072] Table 1 Communication pipeline asset risk label scoring table

[0073]

[0074] For example: A community with more than 10,000 households is defined as a large community. 10,000 to 15,000 households are given 8 points, 15,000 to 20,000 households are given 9 points, and more than 20,000 households are given 10 points.

[0075] S11, collecting communication pipeline asset status data through the communication pipeline asset intelligent monitoring platform.

[0076] It should be noted that the communication pipeline asset status data includes: illegal occupation of pipeline assets data, communication pipeline resource data, pipeline leasing data, special event data, etc.

[0077] In this step, the communication pipeline asset intelligent protection device is installed on the communication manhole to collect the data of illegal occupation of communication pipeline assets in real time; the communication pipeline asset illegal occupation data in the communication manhole without intelligent protection device is collected through manual inspection. By synchronizing with the third-party system, the communication pipeline resource data, pipeline leasing data, special event data, etc. are obtained.

[0078] S12, after the communication pipeline asset intelligent monitoring platform receives the communication pipeline asset status data, it updates the communication pipeline asset risk label and pushes it to the communication pipeline asset risk assessment platform.

[0079] S13, the communication pipeline asset risk assessment platform has pre-built a communication pipeline asset risk assessment model, and calculates the communication pipeline asset risk probability based on the communication pipeline asset risk label.

[0080] In this step, a BP neural network model is constructed. BP (back propagation) neural network is a multi-layer feedforward neural network trained according to the error back propagation algorithm. It is widely used in artificial intelligence network models. Its basic idea is the gradient descent method, which uses gradient search technology to minimize the mean square error between the actual output value and the expected output value of the network. The BP neural network model includes an input layer, a hidden layer, and an output layer, where the hidden layer can include multiple sublayers.

[0081] A neuron is a simple abstraction of a biological neuron, with multiple inputs, each of which has a corresponding weight. The neuron multiplies the input by the weight and sums the result, and then processes it through an activation function to get the output. This output can be used as the input of other neurons, thus forming a neural network.

[0082] In some embodiments, Figure 2 As shown in the figure, a communication pipeline asset risk assessment model based on the BP neural network model is constructed, which can be specifically carried out in the following manner:

[0083] S130, develop risk labels for communication pipeline assets;

[0084] The communication pipeline asset risk assessment model constructed by the present invention adopts a BP neural network model, and the model is closely related to the communication pipeline asset risk label. Therefore, it is easy to understand that the communication pipeline asset risk label is formulated here. This is the same as the aforementioned S10. The label has been formulated before and there is no need to formulate it again. It can be used directly according to the label.

[0085] S131, determining the number of neurons in the input layer of the BP neural network model according to the number of parameters contained in the communication pipeline asset risk label;

[0086] In this embodiment, the communication pipeline asset risk label includes 9 parameters, including regional market heat, communication pipeline asset value, existing maintenance capabilities, manhole accessibility, difficulty of on-site operations, resource leasing factors in manholes, time factors, event factors, and illegal occupation factors. Therefore, the number of neurons in the input layer of the BP neural network model to be established is 9.

[0087] S132, constructing a BP neural network model according to the number of input layer neurons, a preset hidden layer function and a preset output function;

[0088] In this embodiment, the BP neural network model can only receive input data with the same number as the input nodes, and calculates the mathematical model of the output data through the preset hidden layer function and the preset output function.

[0089] Considering that the number of hidden layers has a great influence on the recognition performance of the BP neural network model, if the number of hidden layers is too small, the recognition accuracy of the neural network will be too low. Generally speaking, a neural network containing two hidden layers can represent any nonlinear decision planning. However, too many hidden layers will increase the complexity of training and also lead to overfitting. Therefore, in this embodiment, the number of hidden layers selected is 2.

[0090] In this embodiment, the number of neurons in the input layer of the BP neural network model is 9, the number of neurons in the output layer is 1, and the number of neurons in the hidden layer is G; the number of neurons in any input layer is f i , i∈(1, 2…9); any hidden layer neuron is G j , j∈(1, 2…G); any output layer neuron is h k , k∈(1, 2);

[0091] The number of neurons G in the hidden layer is calculated as follows:

[0092]

[0093] Among them, f is the number of neurons in the input layer, h is the number of neurons in the output layer, and a is a constant, which is rounded between 1 and 10 and satisfies the value range of the number of neurons G in the hidden layer between 4 and 14.

[0094] According to multiple experiments, the best prediction results are obtained when the number of the first hidden layer is 9 and the number of the second hidden layer is 1. Therefore, it can be determined that the structure of the double hidden layer BP neural network is 9-9-1-1, that is, 9 input layer neurons, 9 first hidden layer neurons, 1 second hidden layer neuron, and 1 output result data, that is, the risk probability of communication pipeline assets.

[0095] In this embodiment, the transfer function from the input layer to the first hidden layer adopts a nonlinear transformation function, namely, a Sigmoid function. The transfer function from the first hidden layer to the second hidden layer is a purelin function. The transfer function from the second hidden layer to the output layer is a purelin function. At this time, the BP neural network model achieves the best prediction effect.

[0096] In this embodiment, the BP neural network model obtains an output data according to a preset output function, that is, the risk probability of the communication pipeline asset.

[0097] S133, obtaining a training set, training the BP neural network model through training samples and a preset training function, and obtaining a communication pipeline asset risk assessment model;

[0098] In this embodiment, the training set includes: a certain number of training samples and communication pipeline asset risk labels corresponding to the training samples. The BP neural network model is trained through the training samples and the preset training function to obtain a communication pipeline asset risk assessment model.

[0099] In this embodiment, the BP neural network model is iteratively trained through training samples, and finally the optimal values ​​of the unknown mathematical parameters in the BP neural network model are obtained.

[0100] S134, inputting the communication pipeline asset risk tag into the communication pipeline asset risk assessment model, and evaluating to obtain the communication pipeline asset risk probability.

[0101] In this embodiment, the sample risk probability is determined by risk factor analysis, including the following steps:

[0102] (1) Obtaining the main risk factor indicator, which is the risk indicator with a cumulative variance contribution rate of more than 80% among the 9 risk indicators in the training set;

[0103] (2) Calculate the score of the risk main cause indicator using the score coefficient matrix of the risk main cause indicator, and determine the weight of the risk main cause indicator by the proportion of the risk main cause indicator to the cumulative variance contribution rate;

[0104] (3) Calculate the comprehensive score of the main risk factors based on the weights and scores of the main risk factor indicators, which is the risk probability of the communication pipeline assets.

[0105] By inputting the acquired communication pipeline asset risk tags into the established communication pipeline asset risk assessment model, the communication pipeline asset risk probability can be obtained through risk main cause analysis.

[0106] S135, determining whether the communication pipeline asset risk probability exceeds a threshold.

[0107] S14, based on the calculation result of the communication pipeline asset risk probability, identifying the communication manhole identifier whose communication pipeline asset risk probability exceeds a threshold.

[0108] During the entire modeling process, the communication pipeline asset risk assessment platform constructs a communication pipeline asset risk assessment model through a communication pipeline asset risk assessment algorithm based on the BP neural network algorithm, dynamically calculates the communication pipeline asset risk assessment values ​​(risk probability) of all manholes, and pushes the communication manhole identifiers whose asset risk assessment values ​​(risk probability) exceed the threshold to the communication pipeline asset intelligent monitoring platform.

[0109] It should be noted that the threshold value is the risk probability limit value, and in the embodiment of the present invention, it is set to 0.7.

[0110] S15, generating warning information according to the communication manhole identification exceeding the threshold.

[0111] The communication pipeline asset risk assessment platform pushes the communication manhole identification of the communication pipeline asset risk probability exceeding the threshold to the communication pipeline asset intelligent monitoring platform. The communication pipeline asset intelligent monitoring platform generates early warning information based on the received communication manhole identification, pushes it to the maintenance terminal and displays it.

[0112] It should be noted that the warning information includes: communication manhole identification, warning time, etc.

[0113] The maintenance terminal is the terminal equipment of pipeline maintenance personnel, who can check the early warning information in time and perform fixed-point maintenance based on the early warning information.

[0114] S16, according to the early warning information, the maintenance personnel inspect and handle the communication manholes that exceed the threshold, and feedback the early warning handling result data, and repeat step S11.

[0115] According to the warning information received from the maintenance terminal, the maintenance personnel inspect and handle the abnormal communication manhole, and report the warning handling result data to the communication pipeline asset intelligent monitoring platform, and repeat step S11.

[0116] It should be noted that the early warning and disposal results include: whether the communication pipeline assets have been encroached upon; if so, the name of the encroaching party, etc.

[0117] Pipeline maintenance personnel inspect and handle abnormal communication manholes according to the risk characteristics in the early warning information, and report the early warning disposal results to the communication pipeline asset intelligent monitoring platform through the maintenance terminal. The communication pipeline asset intelligent monitoring platform pushes the received early warning disposal results to the communication pipeline asset risk assessment platform. The risk assessment platform inputs the early warning disposal result data into the neural network for model training, and improves the prediction accuracy through the model's self-learning mechanism.

[0118] The present invention also provides a communication pipeline asset intelligent protection system, such as Figure 3 As shown, the communication pipeline asset intelligent protection system 300 includes:

[0119] The communication pipeline asset intelligent monitoring platform 320 is used to receive the communication pipeline asset status data reported by the communication pipeline asset intelligent protection device 340, update the communication pipeline asset risk label, and generate warning information for the communication manhole identification exceeding the risk probability threshold and push it to the maintenance terminal 330.

[0120] The communication pipeline asset risk assessment platform 310 is used to receive the communication pipeline asset status data pushed by the communication pipeline asset intelligent monitoring platform 320, and calculate the communication pipeline asset risk probability through the communication pipeline asset risk assessment model, and push the communication manhole identification exceeding the risk probability threshold to the communication pipeline asset intelligent monitoring platform 320. The communication pipeline asset intelligent monitoring platform 320 generates warning information for the communication manhole identification exceeding the risk probability threshold and pushes it to the maintenance terminal 330.

[0121] The communication pipeline asset intelligent protection device 340 is used for protecting assets in the communication manhole. When facing external force impact, external force prying, and cutting, it generates an alarm and collects real-time status data of the communication pipeline assets and pushes it to the communication pipeline asset intelligent monitoring platform 320.

[0122] The maintenance terminal 330 is used to receive the warning information pushed by the communication pipeline asset intelligent monitoring platform 320 and display it to the maintenance personnel 350.

[0123] The third-party system 360 is used to push the communication pipeline asset status data to the communication pipeline asset intelligent monitoring platform 320 according to a preset mode.

[0124] The maintenance personnel 350 inspect and handle the communication manholes that exceed the risk probability threshold, and report the warning handling results to the communication pipeline asset intelligent monitoring platform 320 via the maintenance terminal 330.

[0125] It is easy for those skilled in the art to understand that, under the premise of no conflict, the above-mentioned preferred solutions can be freely combined and superimposed.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A communication pipeline asset intelligent protection method, characterized in that: The steps include: Prepare risk labels for communication pipeline assets and configure labels for communication manholes; Collect communication pipeline asset status data in real time; Based on the communication pipeline asset status data, update the communication pipeline asset risk label; Based on the updated communication pipeline asset risk label, the communication pipeline asset risk probability is calculated by the pre-built communication pipeline asset risk assessment model; Based on the calculation results of the risk probability of communication pipeline assets, identify the communication manhole marks where the risk probability of communication pipeline assets exceeds the threshold; Generate warning information based on communication manhole identification exceeding the threshold; According to the early warning information, the maintenance personnel inspect and deal with the communication manholes that exceed the threshold, and feedback the early warning treatment result data, and repeat the above steps.

2. The protection method according to claim 1, characterized in that: The preparation of communication pipeline asset risk labels includes: Nine risk indicators related to communication pipeline asset risks are set according to the expert decision-making method, including: regional market popularity, communication pipeline asset value, existing maintenance capabilities, manhole accessibility, on-site operation difficulty, manhole resource leasing factors, time factors, event factors and illegal occupation factors; The nine risk indicators are quantified according to quantifiable assessment factors, and the communication pipeline asset risk label is compiled based on the quantification results.

3. The protection method according to claim 1, characterized in that: The communication pipeline asset status data includes: one or more of pipeline asset illegal occupation data, pipeline resource data, pipeline leasing data, and event data.

4. The protection method according to claim 1, characterized in that: The calculation of the communication pipeline asset risk probability by the pre-built communication pipeline asset risk assessment model includes: According to the number of parameters contained in the risk label of the communication pipeline asset, the number of neurons in the input layer of the BP neural network model is determined; Construct a BP neural network model according to the number of input layer neurons, the preset hidden layer function and the preset output function; Obtain a training set, train the BP neural network model through training samples and preset training functions, and obtain a communication pipeline asset risk assessment model; The communication pipeline asset risk label is input into the communication pipeline asset risk assessment model to evaluate the communication pipeline asset risk probability.

5. The protection method according to claim 4, characterized in that: The BP neural network model includes two hidden layers; the structure of the double hidden layer BP neural network is 9-9-1-1, that is, the number of the first hidden layer is 9, and the number of the second hidden layer is 1.

6. The protection method according to claim 5, characterized in that: The number of neurons in the input layer of the BP neural network model is 9, the number of neurons in the output layer is 1, and the number of neurons in the hidden layer is G; the number of neurons in any input layer is f i , i∈(1, 2…9); any hidden layer neuron is G j , j∈(1, 2…G); any output layer neuron is h k , k∈(1, 2); The number of neurons G in the hidden layer is calculated as follows: ； Among them, f is the number of neurons in the input layer, h is the number of neurons in the output layer, and a is a constant, which is rounded between 1 and 10 and satisfies that the value range of the number of neurons G in the hidden layer is between 4 and 14.

7. The protection method according to claim 6, characterized in that: The transfer function from the input layer to the first hidden layer adopts the Sigmoid function, the transfer function from the first hidden layer to the second hidden layer adopts the purelin function, and the transfer function from the second hidden layer to the output layer adopts the purelin function.

8. The protection method according to claim 4, characterized in that: The training set includes: a certain amount of training samples and communication pipeline asset risk labels corresponding to the training samples.

9. The protection method according to claim 4, characterized in that: Determine the risk probability of communication pipeline assets through risk factor analysis, including: Obtaining a risk main cause indicator, wherein the risk main cause indicator is a risk indicator whose cumulative variance contribution rate reaches more than 80% among the 9 risk indicators in the training set; The score coefficient matrix of the risk main cause indicator is used to calculate the score of the risk main cause indicator, and the weight of the risk main cause indicator is determined by the proportion of the risk main cause indicator to the cumulative variance contribution rate; The comprehensive score of the main risk factors is calculated based on the weights and scores of the main risk factor indicators, which is the risk probability of communication pipeline assets.

10. A communication pipeline asset intelligent protection system for the protection method according to any one of claims 1 to 9, characterized in that: include: The communication pipeline asset intelligent protection device is used to collect data on illegal occupation of communication pipeline assets and push it to the communication pipeline asset intelligent monitoring platform; The communication pipeline asset intelligent monitoring platform is used to receive the communication pipeline asset status data reported by the communication pipeline asset intelligent protection device to update the communication pipeline asset risk label. The communication pipeline asset intelligent monitoring platform also generates early warning information based on the communication manhole identification exceeding the risk probability threshold and pushes it to the maintenance terminal; The communication pipeline asset risk assessment platform is used to receive the updated communication pipeline asset risk tags pushed by the communication pipeline asset intelligent monitoring platform, calculate the communication pipeline asset risk probability through the communication pipeline asset risk assessment model, and push the communication manhole identification that exceeds the risk probability threshold to the communication pipeline asset intelligent monitoring platform; Maintenance terminal, used to receive warning information pushed by the communication pipeline asset intelligent monitoring platform and display it to maintenance personnel; A third-party system is used to push the communication pipeline asset status data to the communication pipeline asset intelligent monitoring platform according to a pre-set mode; Maintenance personnel will inspect and handle communication manholes that exceed the risk probability threshold, and report the early warning and disposal result data to the communication pipeline asset intelligent monitoring platform through the maintenance terminal.

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