End-cloud cooperative rapid emergency scheme deployment method and system

Abstract

The application is suitable for the technical field of end-cloud cooperation, and particularly relates to a rapid emergency scheme deployment method and system for end-cloud cooperation. The method comprises receiving an emergency scheme uploaded by an emergency party and containing a trigger time, obtaining scene parameters of each scene data collector based on the trigger time; performing data processing on the scene parameters to obtain a feature data body of the emergency scheme; comparing the feature data bodies of different emergency schemes, clustering the emergency schemes based on the comparison result of the feature data bodies, comparing the emergency schemes in the same type of emergency schemes, and determining a mutual exclusion scheme of each emergency scheme; obtaining scene parameters uploaded by a demand party in real time, matching the emergency schemes according to the scene parameters, synchronously reading the mutual exclusion schemes of the emergency schemes, and performing replacement encryption on the emergency schemes; the new scheme is obtained through the replacement encryption mode, on one hand, the demand party can perform preliminary verification, and on the other hand, the confidentiality is very high.

Description

Technical Field

[0001] This invention relates to the field of edge-cloud collaboration technology, and in particular to a method and system for rapid emergency deployment of edge-cloud collaboration solutions. Background Technology

[0002] With the rapid development of IoT, edge computing, and cloud computing technologies, edge-cloud collaborative architecture is widely used in industrial manufacturing scenarios. In industrial manufacturing, various problems frequently arise. For a particular industrial manufacturing scenario, the probability of a problem occurring is low, making it difficult to build a problem-solution library. However, under an edge-cloud collaborative architecture, data from multiple manufacturing scenarios can be shared, enabling the construction of a problem-solution library and improving the response speed to subsequent problems. This library is typically provided by a qualified third party. Emergency plans are also confidential data in an industrial manufacturing scenario. When shared, they may only be shared with certain entities, requiring third parties to encrypt them. Existing encryption methods are too simplistic, often involving overall encryption, leaving the requesting party unable to verify whether the emergency plan matches their needs. Therefore, providing an easily verifiable encryption scheme is the technical problem this invention aims to solve. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for rapid emergency response deployment with edge-cloud collaboration, in order to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for rapid deployment of an edge-cloud collaborative emergency response solution, the method comprising:

[0006] The system receives emergency plans uploaded by emergency responders, which include trigger times, and obtains scene parameters from data collectors in each scene based on the trigger times. The time span for obtaining scene parameters is related to the scene parameters themselves.

[0007] The scene parameters are processed into data to obtain the feature data body of the emergency plan; the feature data body is an array with a preset dimension.

[0008] Compare the feature data volumes of different emergency plans, cluster the emergency plans based on the comparison results of the feature data volumes, compare the emergency plans in the same category, and determine the mutually exclusive plans for each emergency plan.

[0009] The system acquires scenario parameters uploaded by the requester in real time, matches emergency solutions based on the scenario parameters, synchronously reads the mutually exclusive solutions of the emergency solutions, and performs replacement-based encryption on the emergency solutions.

[0010] When a request to obtain data is received from a client, the replacement location and replacement source are sent to the client as the decryption key.

[0011] Furthermore, the step of receiving the emergency response plan uploaded by the emergency responder, which includes the trigger time, and obtaining the scene parameters of each scene data collector based on the trigger time includes:

[0012] Receive emergency plans uploaded by emergency responders, which include the trigger time; the data structure of the emergency plan is a graph, where node labels are used to characterize emergency measures, node size is used to characterize the emergency quantity of the applied measures, and edges are used to characterize the interval duration of the emergency measures.

[0013] For any scene data collector, the data is acquired sequentially in reverse time, with the trigger time as the last time.

[0014] The collected data is normalized, and the difference between the normalized collected data and the previous data is calculated as the reverse change rate.

[0015] When the reverse change rate is less than the preset change rate threshold, the number of stable counts is incremented. When the number of stable counts reaches the preset number threshold, the acquisition of collected data is stopped, and the acquired and normalized collected data is output as the scene parameters of the data collector for this scene.

[0016] When the reverse change rate is not less than the preset change rate threshold, the historical frequency of the collected data within the preset period is obtained. When the historical frequency reaches the preset frequency threshold, the stable count is incremented. When the stable count reaches the preset count threshold, the acquisition of collected data is stopped, and the normalized collected data is output as the scene parameters of the data collector for this scene.

[0017] Furthermore, the step of digitizing the scene parameters to obtain the feature data body of the emergency plan includes:

[0018] Obtain the spatial location of the scene data collectors and determine the collector order based on the spatial location;

[0019] The scene parameters are read and arranged sequentially by the collector to obtain the transfer data body;

[0020] For missing data in the transit data body, query the average value of the scene parameters corresponding to the location of the missing data, and use it as supplementary data;

[0021] When each missing data point in the intermediate data body is filled in, the feature data body is obtained.

[0022] Furthermore, the steps of comparing the feature data volumes of different emergency response plans, clustering the emergency response plans based on the comparison results of the feature data volumes, comparing emergency response plans within the same category, and determining mutually exclusive plans for each emergency response plan include:

[0023] Compare the feature data volumes of different emergency response plans and calculate data similarity;

[0024] Clustering of emergency response plans based on data similarity;

[0025] For each type of emergency response plan, compare each plan in pairs and calculate the similarity between them.

[0026] For any emergency response plan, select the one with the lowest similarity among similar emergency response plans and use it as the mutually exclusive plan.

[0027] Furthermore, the calculation process for the similarity of the schemes is as follows:

[0028] Construct an arithmetic sequence with a first term of 1 and a common difference of 1; the number of elements is the number of nodes in the emergency plan with fewer nodes among the two emergency plans participating in the comparison;

[0029] The emergency plan is sequentially divided by treating each value in the arithmetic sequence as a tuple number, resulting in a tuple sequence corresponding to each value.

[0030] Compare the tuple sequences of the two emergency plans, calculate the similarity, and use it as the similarity value.

[0031] The similarity of each value is calculated to obtain the similarity between the two emergency response plans.

[0032] Furthermore, the steps of obtaining the scenario parameters uploaded by the requester in real time, matching emergency solutions based on the scenario parameters, synchronously reading the mutual exclusion schemes of the emergency solutions, and performing replacement-based encryption on the emergency solutions include:

[0033] Real-time acquisition of scene parameters uploaded by the demand side, and conversion of scene parameters into feature data body;

[0034] The feature data volume is compared with the statistically analyzed feature data volume to calculate the data similarity.

[0035] Select the statistically significant feature data volume with the highest data similarity, query its corresponding emergency plan, and use it as the target plan;

[0036] Query mutually exclusive solutions for the target solution and perform replacement encryption on the contingency solution.

[0037] The present invention also provides a rapid emergency response deployment system with edge-cloud collaboration, the system comprising:

[0038] The scene parameter acquisition module is used to receive emergency plans uploaded by emergency responders, which contain the trigger time, and to acquire scene parameters of each scene data collector based on the trigger time; the time span for acquiring scene parameters is related to the scene parameters themselves.

[0039] The scene parameter processing module is used to perform data processing on scene parameters to obtain the feature data body of the emergency plan; the feature data body is an array with preset dimensions;

[0040] The scheme analysis module is used to compare the feature data of different emergency schemes, cluster the emergency schemes based on the comparison results of the feature data, compare the emergency schemes in the same category, and determine the mutually exclusive schemes of each emergency scheme.

[0041] The replacement encryption module is used to obtain the scene parameters uploaded by the requester in real time, match the emergency plan according to the scene parameters, synchronously read the mutual exclusion scheme of the emergency plan, and perform replacement encryption on the emergency plan.

[0042] When a request to obtain data is received from a client, the replacement location and replacement source are sent to the client as the decryption key.

[0043] Furthermore, the scene parameter acquisition module includes:

[0044] An emergency plan receiving unit is used to receive emergency plans containing trigger times uploaded by emergency responders; the data structure of the emergency plan is a graph, where node labels are used to characterize emergency measures, node sizes are used to characterize the emergency quantity of the applied measures, and edges are used to characterize the interval duration of the emergency measures.

[0045] The data acquisition and reading unit is used to acquire data from any scene data acquisition device in reverse time sequence, with the trigger time as the last time.

[0046] The rate of change calculation unit is used to normalize the collected data and calculate the difference between the normalized collected data and the previous data as the reverse rate of change.

[0047] The first output unit is used to increment the number of stable times when the reverse change rate is less than the preset change rate threshold. When the number of stable times reaches the preset number threshold, it stops acquiring data and outputs the acquired and normalized data as the scene parameters of the scene data collector.

[0048] The second output unit is used to acquire the historical frequency of the collected data within a preset period when the reverse change rate is not less than the preset change rate threshold. When the historical frequency reaches the preset frequency threshold, the unit increments the stable count. When the stable count reaches the preset count threshold, the unit stops acquiring the collected data and outputs the acquired, standardized collected data as the scene parameters of the scene data collector.

[0049] Furthermore, the scene parameter processing module includes:

[0050] The sequence determination unit is used to obtain the spatial location of the scene data collectors and determine the sequence of the collectors based on their spatial location.

[0051] The parameter arrangement unit is used to read and arrange scene parameters according to the order of the collector to obtain the transfer data body;

[0052] The supplementary data determination unit is used to query the average value of the scene parameters corresponding to the location of the missing data in the transit data body, and use it as supplementary data.

[0053] The third output unit is used to obtain the feature data body when each missing data in the intermediate data body is filled in.

[0054] Furthermore, the scheme analysis module includes:

[0055] The data volume comparison unit is used to compare the feature data volumes of different emergency plans and calculate data similarity;

[0056] The scheme clustering unit is used to cluster emergency schemes based on data similarity;

[0057] The scheme comparison unit is used to compare each type of emergency scheme with the other two schemes and calculate the scheme similarity.

[0058] The mutual exclusion scheme selection unit is used to select the emergency scheme with the lowest similarity among similar emergency schemes for any given emergency scheme, and then use it as the mutual exclusion scheme.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This invention clusters emergency response plans based on scenario data. For emergency response plans of the same type, it determines the mutually exclusive plans for each plan based on the similarity between different plans. When addressing a need, it matches the target plan according to the need, queries the mutually exclusive plans of the target plan, and performs replacement encryption on the emergency response plan to obtain a new plan. The new plan has a certain effect, but it cannot solve the technical problem itself. On the one hand, the requester can conduct preliminary verification, and on the other hand, the confidentiality is very high. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0062] Figure 1 A flowchart illustrating the rapid emergency response deployment method for edge-cloud collaboration provided in this embodiment of the invention.

[0063] Figure 2 This is a block diagram illustrating the composition of a rapid emergency response deployment system for edge-cloud collaboration provided in an embodiment of the present invention. Detailed Implementation

[0064] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0065] Figure 1 This is a flowchart illustrating a rapid emergency response deployment method for edge-cloud collaboration provided in an embodiment of the present invention. The method includes:

[0066] Step S100: Receive the emergency plan containing the trigger time uploaded by the emergency responder, and obtain the scene parameters of each scene data collector based on the trigger time; wherein, the time span for obtaining the scene parameters is related to the scene parameters themselves;

[0067] The emergency response party is the party that implements the emergency plan. For example, some workshops that have already implemented emergency measures will upload the implemented emergency measures to the implementing entity of the technical solution of this invention. It should be noted that this process is uploaded by the implementing party, and by default, they have been granted permission. Generally, the party that needs to use the implementing entity of this method will upload some emergency measures so that they can obtain some reference from the implementing entity of this method when encountering special situations. Therefore, they will be granted permission by default.

[0068] When receiving the emergency response plan uploaded by the emergency responder, it is also necessary to receive the trigger time of the emergency response plan. The trigger time is used to characterize the time when the emergency response plan starts. Taking the trigger time as the last time, the scene parameters in the scene where the emergency responder is located are read backward. The scene parameters are data collected by the scene data collector, and the amount of data collected (time span) is determined by the scene parameters themselves.

[0069] Step S200: Perform data processing on the scene parameters to obtain the feature data body of the emergency plan; the feature data body is an array of preset dimensions;

[0070] The scene data acquisition device includes sensors with data acquisition and data transmission functions. Each scene data acquisition device corresponds to a scene parameter. By statistically analyzing the scene parameters of all scene data acquisition devices, a multi-dimensional data, called the feature data volume, is obtained. Since the technical solution of this invention acquires the corresponding scene parameters once an emergency plan is received, the emergency plan and the feature data volume are in one-to-one correspondence and together constitute a sample.

[0071] Step S300: Compare the feature data volumes of different emergency plans, cluster the emergency plans based on the comparison results of the feature data volumes, compare the emergency plans in the same category, and determine the mutually exclusive plans for each emergency plan.

[0072] Each emergency plan corresponds to a feature data body. The technical solution of this invention receives emergency plans uploaded by multiple entities at different times, which are numerous and belong to the same scenario, such as production workshops producing the same product. For each emergency plan, its corresponding feature data is compared, and the emergency plans are clustered based on the comparison results of the feature data bodies to obtain a class of emergency plans corresponding to similar scenario parameters. Whether these emergency plans are similar is not fixed; they are feasible emergency plans under similar scenario parameters. In each class of emergency plans, the emergency plans are compared pairwise to determine the mutually exclusive plans for each emergency plan. The mutually exclusive plans, as the name suggests, are plans that are significantly different from the emergency plan.

[0073] Step S400: Obtain the scene parameters uploaded by the requester in real time, match the emergency plan according to the scene parameters, synchronously read the mutual exclusion scheme of the emergency plan, and perform replacement encryption on the emergency plan;

[0074] In practical application, scenario parameters uploaded by the requester are acquired in real time. These scenario parameters correspond to feature data volumes. After being converted into feature data volumes using the same method, they are compared with existing feature data volumes in the sample. The most similar feature data volume can be matched to determine which type of feature data volume the requester's scenario parameters belong to. Then, the corresponding emergency plan is read, the most matching emergency plan is selected, and the mutually exclusive plans of the emergency plan are queried. The emergency plan is then replaced with a replacement encryption. The replacement encryption process can use existing replacement processes. For example, since the emergency plan is a flowchart, a sub-flowchart is extracted from the mutually exclusive plan and replaced in the emergency plan. This is a replacement encryption process. The extraction rules can be set by the staff, such as interval extraction or random extraction, which will not be elaborated here.

[0075] When a request to obtain data is received from a client, the replacement location and replacement source are sent to the client as the decryption key.

[0076] Regarding step S100, the step of receiving the emergency plan containing the trigger time uploaded by the emergency responder, and obtaining the scene parameters of each scene data collector based on the trigger time includes:

[0077] Receive emergency plans uploaded by emergency responders, which include the trigger time; the data structure of the emergency plan is a graph, where node labels are used to characterize emergency measures, node size is used to characterize the emergency quantity of the applied measures, and edges are used to characterize the interval duration of the emergency measures.

[0078] For any scene data collector, the data is acquired sequentially in reverse time, with the trigger time as the last time.

[0079] The collected data is normalized, and the difference between the normalized collected data and the previous data is calculated as the reverse change rate.

[0080] When the reverse change rate is less than the preset change rate threshold, the number of stable counts is incremented. When the number of stable counts reaches the preset number threshold, the acquisition of collected data is stopped, and the acquired and normalized collected data is output as the scene parameters of the data collector for this scene.

[0081] When the reverse change rate is not less than the preset change rate threshold, the historical frequency of the collected data within the preset period is obtained. When the historical frequency reaches the preset frequency threshold, the stable count is incremented. When the stable count reaches the preset count threshold, the acquisition of collected data is stopped, and the normalized collected data is output as the scene parameters of the data collector for this scene.

[0082] In one example of the technical solution of this invention, the process of acquiring scene parameters is described. Since each scene data collector acquires scene parameters in real time, and the data volume is large, data from earlier periods has no analytical value in practical applications. Therefore, it is necessary to determine a time span and acquire data within that time span. The specific execution process is as follows:

[0083] For any scene data acquisition device, data is acquired sequentially in reverse chronological order, with the trigger time as the last time step. This means acquiring data from the end to the beginning. The acquired data is then normalized, and the difference between the normalized data and the previous data is calculated as the reverse chronological change rate. This indicates how much the data changes when acquiring data from the end to the beginning. The normalization process converts the data into dimensionless data with values ​​within a preset range. When the reverse chronological change rate is sufficiently small, the data is considered stable. At this point, a variable called the number of stable iterations is incremented. When the number of stable iterations is sufficiently large, data acquisition stops, and the acquired normalized data is output. The process of collecting data and using it as scene parameters for the scene data collector can be divided into two parts. First, data is acquired from the end of the sequence, with a limit set after a certain number of data points are collected. Second, when the rate of change in reverse order is high, the system determines whether the collected data is normal. This is done by acquiring historical data from the scene data collector within a preset period and querying the number of times the collected data appears in the historical data (called the historical frequency). If the historical frequency is high enough, the data is considered stable. In this case, the stable frequency is incremented. When the stable frequency is high enough, the acquisition of collected data stops, and the normalized collected data is output as the scene parameters for the scene data collector.

[0084] Regarding step S200, the step of performing data processing on the scene parameters to obtain the feature data body of the emergency plan includes:

[0085] Obtain the spatial location of the scene data collectors and determine the collector order based on the spatial location;

[0086] The scene parameters are read and arranged sequentially by the collector to obtain the transfer data body;

[0087] For missing data in the transit data body, query the average value of the scene parameters corresponding to the location of the missing data, and use it as supplementary data;

[0088] When each missing data point in the intermediate data body is filled in, the feature data body is obtained.

[0089] After the scene parameters are acquired, the spatial location of the scene data collector is obtained, and the collector order is determined based on the spatial location. In fact, this order can also be directly input by the administrator. The scene parameters are read and arranged according to the collector order. Since the time span corresponding to each scene parameter is different, the number of elements of each scene parameter is actually inconsistent. After time-domain alignment, the scene parameters obtained are essentially a matrix with a large amount of iron deficiency data, called the transit data volume. For the missing data in the transit data volume, the mean of the scene parameters corresponding to the position of the missing data is queried as supplementary data. When every missing data in the transit data volume is supplemented, the feature data volume is obtained.

[0090] Regarding step S300, the steps of comparing the feature data volumes of different emergency plans, clustering the emergency plans based on the comparison results of the feature data volumes, comparing emergency plans within the same category, and determining mutually exclusive plans for each emergency plan include:

[0091] Compare the feature data volumes of different emergency response plans and calculate data similarity;

[0092] Clustering of emergency response plans based on data similarity;

[0093] For each type of emergency response plan, compare each plan in pairs and calculate the similarity between them.

[0094] For any emergency response plan, select the one with the lowest similarity among similar emergency response plans and use it as the mutually exclusive plan.

[0095] In one example of the technical solution of this invention, the process of determining mutually exclusive schemes is described. The feature data volumes of different emergency schemes are compared, the data similarity is calculated, and the emergency schemes are clustered based on the data similarity. The clustering process can use existing clustering schemes, such as K-means clustering, etc., to obtain various emergency schemes. For each category of emergency schemes, the emergency schemes are compared pairwise, the scheme similarity is calculated, and for any emergency scheme, the emergency scheme with the smallest scheme similarity among the emergency schemes of the same category is selected as the mutually exclusive scheme.

[0096] The above content involves the comparison process of feature data volumes and the comparison process of emergency plans. For the feature data volume comparison process, the calculation of data similarity is actually a comparison process between matrices of different sizes, which can be done in many ways, such as:

[0097] 1. First, interpolate the matrix to standardize its size, and then compare the results.

[0098] 2. Use a preset matrix feature extraction algorithm to obtain feature values, and then compare the feature values.

[0099] 3. A sliding window matching algorithm is adopted.

[0100] The above comparison method is common in existing technology and will not be elaborated here.

[0101] For the comparison process of emergency plans, the present invention provides a unique comparison scheme, the basic conditions of which are: the data structure of the emergency plan is a graph, the node label is used to characterize the emergency measures, the node size is used to characterize the emergency quantity of the applied measures, and the edge is used to characterize the interval duration of the emergency measures; wherein, the node label is the name of the emergency measure, the node size is used to characterize the amount of resources invested in the applied measures, and the edge is used to characterize the interval duration of the emergency measures, the larger the interval duration, the longer the edge.

[0102] The calculation process for the similarity of the schemes is as follows:

[0103] Construct an arithmetic sequence with a first term of 1 and a common difference of 1; the number of elements is the number of nodes in the emergency plan with fewer nodes among the two emergency plans participating in the comparison;

[0104] The emergency plan is sequentially divided by treating each value in the arithmetic sequence as a tuple number, resulting in a tuple sequence corresponding to each value.

[0105] Compare the tuple sequences of the two emergency plans, calculate the similarity, and use it as the similarity value.

[0106] The similarity of each value is calculated to obtain the similarity between the two emergency response plans.

[0107] For two emergency plans (Figure), they are divided into different numbers of tuples. For example, suppose one of the emergency plans is ABCDE, and the number of divisions is two, resulting in tuples AB, BC, CD, and DE. During comparison, these tuples are compared correspondingly (by their corresponding numbers) to obtain the similarity between the tuples. The mean of the similarity between the tuples is calculated as the similarity of that number. Finally, the similarity of each value is calculated to determine the similarity between the two emergency plans. This process can also calculate the mean, or select the maximum or minimum value, which is determined in advance by the management personnel.

[0108] Regarding step S400, the steps of obtaining the scenario parameters uploaded by the requester in real time, matching emergency solutions based on the scenario parameters, synchronously reading the mutual exclusion schemes of the emergency solutions, and performing replacement-based encryption on the emergency solutions include:

[0109] Real-time acquisition of scene parameters uploaded by the demand side, and conversion of scene parameters into feature data body;

[0110] The feature data volume is compared with the statistically analyzed feature data volume to calculate the data similarity.

[0111] Select the statistically significant feature data volume with the highest data similarity, query its corresponding emergency plan, and use it as the target plan;

[0112] Query mutually exclusive solutions for the target solution and perform replacement encryption on the contingency solution.

[0113] The system acquires scenario parameters uploaded by the requester in real time, converts these parameters into feature data in the same way as described above, compares them with the statistically analyzed feature data in the same way as described above, calculates the data similarity, selects the statistically analyzed feature data with the highest data similarity, queries its corresponding emergency solution as the target solution, queries the mutually exclusive solutions of the target solution, and performs replacement encryption on the emergency solution.

[0114] The substitution encryption process is a simple graph data replacement process that requires recording the replacement location and replacement source. In practical applications, when a request for explicit access is received from the requesting party, the replacement location and replacement source are sent to the requesting party, which is equivalent to the decryption key. At this point, the requesting party can obtain the original contingency plan. The advantage of this process is that since the contingency plan and the mutually exclusive plan can independently handle the current situation, but they are combined to form a new plan, this plan cannot solve the current situation. However, if it is forcibly applied, some measures will be correct. When the contingency plan is obtained later, it is equivalent to having undergone a "preprocessing" process. In summary, the technical solution of this invention is equivalent to constructing a relatively flexible encryption scheme.

[0115] Figure 2 This is a structural block diagram of an edge-cloud collaborative rapid emergency response deployment system provided in an embodiment of the present invention. The system 10 comprises:

[0116] The scene parameter acquisition module 11 is used to receive the emergency plan containing the trigger time uploaded by the emergency response party, and to acquire the scene parameters of each scene data collector based on the trigger time; wherein, the time span of the scene parameter acquisition is related to the scene parameter itself.

[0117] The scene parameter processing module 12 is used to perform data processing on the scene parameters to obtain the feature data body of the emergency plan; the feature data body is an array with preset dimensions;

[0118] The scheme analysis module 13 is used to compare the feature data of different emergency schemes, cluster the emergency schemes based on the comparison results of the feature data, compare the emergency schemes in the same category, and determine the mutually exclusive schemes of each emergency scheme.

[0119] The replacement encryption module 14 is used to obtain the scene parameters uploaded by the requester in real time, match the emergency plan according to the scene parameters, synchronously read the mutual exclusion scheme of the emergency plan, and perform replacement encryption on the emergency plan.

[0120] When a request to obtain data is received from a client, the replacement location and replacement source are sent to the client as the decryption key.

[0121] Furthermore, the scene parameter acquisition module 11 includes:

[0122] An emergency plan receiving unit is used to receive emergency plans containing trigger times uploaded by emergency responders; the data structure of the emergency plan is a graph, where node labels are used to characterize emergency measures, node sizes are used to characterize the emergency quantity of the applied measures, and edges are used to characterize the interval duration of the emergency measures.

[0123] The data acquisition and reading unit is used to acquire data from any scene data acquisition device in reverse time sequence, with the trigger time as the last time.

[0124] The rate of change calculation unit is used to normalize the collected data and calculate the difference between the normalized collected data and the previous data as the reverse rate of change.

[0125] The first output unit is used to increment the number of stable times when the reverse change rate is less than the preset change rate threshold. When the number of stable times reaches the preset number threshold, it stops acquiring data and outputs the acquired and normalized data as the scene parameters of the scene data collector.

[0126] The second output unit is used to acquire the historical frequency of the collected data within a preset period when the reverse change rate is not less than the preset change rate threshold. When the historical frequency reaches the preset frequency threshold, the unit increments the stable count. When the stable count reaches the preset count threshold, the unit stops acquiring the collected data and outputs the acquired, standardized collected data as the scene parameters of the scene data collector.

[0127] Specifically, the scene parameter processing module 12 includes:

[0128] The sequence determination unit is used to obtain the spatial location of the scene data collectors and determine the sequence of the collectors based on their spatial location.

[0129] The parameter arrangement unit is used to read and arrange scene parameters according to the order of the collector to obtain the transfer data body;

[0130] The supplementary data determination unit is used to query the average value of the scene parameters corresponding to the location of the missing data in the transit data body, and use it as supplementary data.

[0131] The third output unit is used to obtain the feature data body when each missing data in the intermediate data body is filled in.

[0132] Furthermore, the scheme analysis module 13 includes:

[0133] The data volume comparison unit is used to compare the feature data volumes of different emergency plans and calculate data similarity;

[0134] The scheme clustering unit is used to cluster emergency schemes based on data similarity;

[0135] The scheme comparison unit is used to compare each type of emergency scheme with the other two schemes and calculate the scheme similarity.

[0136] The mutual exclusion scheme selection unit is used to select the emergency scheme with the lowest similarity among similar emergency schemes for any given emergency scheme, and then use it as the mutual exclusion scheme.

[0137] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for rapid emergency response deployment through edge-cloud collaboration, characterized in that, The method includes: The system receives emergency plans uploaded by emergency responders, which include trigger times, and obtains scene parameters from data collectors in each scene based on the trigger times. The time span for obtaining scene parameters is related to the scene parameters themselves. The scene parameters are processed into data to obtain the feature data body of the emergency plan; the feature data body is an array with a preset dimension. Compare the feature data volumes of different emergency plans, cluster the emergency plans based on the comparison results of the feature data volumes, compare the emergency plans in the same category, and determine the mutually exclusive plans for each emergency plan. The system acquires scenario parameters uploaded by the requester in real time, matches emergency solutions based on the scenario parameters, synchronously reads the mutually exclusive solutions of the emergency solutions, and performs replacement-based encryption on the emergency solutions. When a request for access is received from the requester, the replacement location and replacement source will be sent to the requester as a decryption key. The steps of receiving the emergency response plan containing the trigger time uploaded by the emergency responder and obtaining the scene parameters of each scene data collector based on the trigger time include: Receive emergency plans uploaded by emergency responders, which include the trigger time; the data structure of the emergency plan is a graph, where node labels are used to characterize emergency measures, node size is used to characterize the emergency quantity of the applied measures, and edges are used to characterize the interval duration of the emergency measures. For any scene data collector, the data is acquired sequentially in reverse time, with the trigger time as the last time. The collected data is normalized, and the difference between the normalized collected data and the previous data is calculated as the reverse change rate. When the reverse change rate is less than the preset change rate threshold, the number of stable counts is incremented. When the number of stable counts reaches the preset number threshold, the acquisition of collected data is stopped, and the acquired and normalized collected data is output as the scene parameters of the scene data collector. When the reverse change rate is not less than the preset change rate threshold, the historical frequency of the collected data within the preset period is obtained. When the historical frequency reaches the preset frequency threshold, the stable count is incremented. When the stable count reaches the preset count threshold, the acquisition of collected data is stopped, and the normalized collected data is output as the scene parameters of the data collector for this scene. The step of digitizing the scene parameters to obtain the feature data body of the emergency plan includes: Obtain the spatial location of the scene data collectors and determine the collector order based on the spatial location; The scene parameters are read and arranged sequentially by the collector to obtain the transfer data body; For missing data in the transit data body, query the average value of the scene parameters corresponding to the location of the missing data, and use it as supplementary data; When every missing data point in the intermediate data body is filled in, the feature data body is obtained; The steps of acquiring scene parameters uploaded by the demander in real time, matching emergency solutions based on the scene parameters, synchronously reading the mutual exclusion schemes of the emergency solutions, and performing replacement-based encryption on the emergency solutions include: Real-time acquisition of scene parameters uploaded by the demand side, and conversion of scene parameters into feature data body; The feature data volume is compared with the statistically analyzed feature data volume to calculate the data similarity. Select the statistically significant feature data volume with the highest data similarity, query its corresponding emergency plan, and use it as the target plan; Query mutually exclusive solutions for the target solution and perform replacement encryption on the contingency solution.

2. The rapid emergency response deployment method for edge-cloud collaboration according to claim 1, characterized in that, The steps of comparing feature data volumes of different emergency response plans, clustering emergency response plans based on the comparison results of the feature data volumes, comparing emergency response plans within the same category, and determining mutually exclusive plans for each emergency response plan include: Compare the feature data volumes of different emergency response plans and calculate data similarity; Clustering of emergency response plans based on data similarity; For each type of emergency response plan, compare each plan in pairs and calculate the similarity between them. For any emergency response plan, select the one with the lowest similarity among similar emergency response plans and use it as the mutually exclusive plan.

3. The rapid emergency response deployment method for edge-cloud collaboration according to claim 2, characterized in that, The calculation process for the similarity of the schemes is as follows: Construct an arithmetic sequence with a first term of 1 and a common difference of 1; the number of elements is the number of nodes in the emergency plan with fewer nodes among the two emergency plans participating in the comparison; The emergency plan is sequentially divided by treating each value in the arithmetic sequence as a tuple number, resulting in a tuple sequence corresponding to each value. Compare the tuple sequences of the two emergency plans, calculate the similarity, and use it as the similarity value. The similarity of each value is calculated to obtain the similarity between the two emergency response plans.

4. A rapid emergency response deployment system with edge-cloud collaboration, characterized in that, The system includes: The scene parameter acquisition module is used to receive emergency plans uploaded by emergency responders, which contain the trigger time, and to acquire scene parameters of each scene data collector based on the trigger time; the time span for acquiring scene parameters is related to the scene parameters themselves. The scene parameter processing module is used to perform data processing on scene parameters to obtain the feature data body of the emergency plan; the feature data body is an array with preset dimensions; The scheme analysis module is used to compare the feature data of different emergency schemes, cluster the emergency schemes based on the comparison results of the feature data, compare the emergency schemes in the same category, and determine the mutually exclusive schemes of each emergency scheme. The replacement encryption module is used to obtain the scene parameters uploaded by the requester in real time, match the emergency plan according to the scene parameters, synchronously read the mutual exclusion scheme of the emergency plan, and perform replacement encryption on the emergency plan. When a request for access is received from the requester, the replacement location and replacement source will be sent to the requester as a decryption key. The scene parameter acquisition module includes: An emergency plan receiving unit is used to receive emergency plans containing trigger times uploaded by emergency responders; the data structure of the emergency plan is a graph, where node labels are used to characterize emergency measures, node sizes are used to characterize the emergency quantity of the applied measures, and edges are used to characterize the interval duration of the emergency measures. The data acquisition and reading unit is used to acquire data from any scene data acquisition device in reverse time sequence, with the trigger time as the last time. The rate of change calculation unit is used to normalize the collected data and calculate the difference between the normalized collected data and the previous data as the reverse rate of change. The first output unit is used to increment the number of stable times when the reverse change rate is less than the preset change rate threshold. When the number of stable times reaches the preset number threshold, it stops acquiring data and outputs the acquired and normalized data as the scene parameters of the scene data collector. The second output unit is used to acquire the historical frequency of the collected data within a preset period when the reverse change rate is not less than the preset change rate threshold. When the historical frequency reaches the preset frequency threshold, the stable count is incremented automatically. When the stable count reaches the preset count threshold, the acquisition of collected data is stopped, and the acquired and normalized collected data is output as the scene parameters of the scene data collector. The scene parameter processing module includes: The sequence determination unit is used to obtain the spatial location of the scene data collectors and determine the sequence of the collectors based on their spatial location. The parameter arrangement unit is used to read and arrange scene parameters according to the order of the collector to obtain the transfer data body; The supplementary data determination unit is used to query the average value of the scene parameters corresponding to the location of the missing data in the transit data body, and use it as supplementary data. The third output unit is used to obtain the feature data body when each missing data in the intermediate data body is filled in; The steps of acquiring scene parameters uploaded by the demander in real time, matching emergency solutions based on the scene parameters, synchronously reading the mutual exclusion schemes of the emergency solutions, and performing replacement-based encryption on the emergency solutions include: Real-time acquisition of scene parameters uploaded by the demand side, and conversion of scene parameters into feature data body; The feature data volume is compared with the statistically analyzed feature data volume to calculate the data similarity. Select the statistically significant feature data volume with the highest data similarity, query its corresponding emergency plan, and use it as the target plan; Query mutually exclusive solutions for the target solution and perform replacement encryption on the contingency solution.

5. The rapid emergency response deployment system for edge-cloud collaboration according to claim 4, characterized in that, The scheme analysis module includes: The data volume comparison unit is used to compare the feature data volumes of different emergency plans and calculate data similarity; The scheme clustering unit is used to cluster emergency schemes based on data similarity; The scheme comparison unit is used to compare each type of emergency scheme with the other two schemes and calculate the scheme similarity. The mutual exclusion scheme selection unit is used to select the emergency scheme with the lowest similarity among similar emergency schemes for any given emergency scheme, and then use it as the mutual exclusion scheme.

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