Developing a Digital System for Disaster Prevention Planning: An Analysis Based on Local Government Policy Texts

The dramatic increase in accidents and natural disasters over the past three decades, resulting in huge economic losses and injuries, has raised concerns about emergency preparedness among the public and governments worldwide. [1] It can be seen that disasters, especially natural disasters, have accompanied the development of countries and nations around the world throughout the process and will continue to do so in the future. [2] Therefore, the prediction and prevention and control of disasters with the occurrence of disasters throughout the development history of mankind, early mankind for the worship of natural totems, reflecting the helplessness of mankind at that time for the disaster, can only hope in the so-called gods and superstitions, with the development of science and technology, the awakening of rational thinking, the development of social productive forces, the worship of natural totems to the mere observation of natural phenomena changed to the observation of natural phenomena. With the development of science and technology, the awakening of rational thinking, and the development of social productivity, the worship of natural totems changed into the observation and summary of natural phenomena, in the hope of predicting disasters, which led to the emergence of early meteorologists and observers. [3] Natural disasters affect the formulation of national major policies and the direction of policy and history, affecting the fate of the country, and at the same time, disaster governance policy as a national and local mandatory authoritative and violent means of governance, not only is the national will fully in the field of disaster governance is an important manifestation of the national will in the field of disaster governance, but also is the national and local effectively carry out the prevention and mitigation work. At the same time, disaster governance policy as a national and local coercive authoritative and violent means of governance is not only an important expression of national will in the field of disaster governance, but also an important guarantee for effective national and local disaster prevention and mitigation. [4]

In view of the ever-changing situation of the natural environment, how to scientifically and effectively predict and manage natural disasters, and to prevent and resolve major natural disasters in a timely manner is a major issue facing the sustainable development of China's society. [5] This not only requires the state to introduce relevant laws and regulations to form a more complete legal system of disaster management, but also requires local governments to formulate relevant measures according to local conditions in order to ensure the safety of people's lives and health, and the sustainable development of the economy and natural ecology. All-round, multi-level prediction and prevention system for China's disaster management business procedures, standardisation, the rule of law, and lay a solid foundation for all. Contingency planning for disasters should be a continuous process to ensure that emergency preparedness, related organisational structures, and the shortcomings arising from the process of change over time are reduced as much as possible, of which risk assessment and is the core of contingency planning, risk assessment requires the design and improvement of the relevant risk model, for different environments, the development of risk models differ. [6] Contingency plans are important to ensure that they are effective in reducing injuries and property damage in the aftermath of a disaster. However, the mere existence of a contingency plan does not necessarily guarantee a high level of resilience, as the effectiveness of resilience ultimately depends on the local government and the local context, and detailed information about the disaster can help to compensate for the shortcomings of the plan. [7] Improvements in the management of contingency plans can enhance the resilience of relevant rescue organisations, and these plans need to be regularly reviewed and updated by the relevant authorities to ensure that they remain effective and responsive to changing external threats. These issues lead to an increase in the riskiness of disaster preparedness, and the impact of these uncertainties can affect the efficiency of data systems and ultimately lead to poor performance of computational network designs. Considering the critical importance of efficient optimisation strategies for computational processes. Therefore, we consider the upgrading of the model and data system, which can be categorised into partial and full upgrading of the system. Partial upgrading of the system means that it is possible to utilise previous techniques in many cases and achieve many benefits such as power management by consolidating virtual machines into a minimum number of servers. In addition, it allows load balancing by reducing the load on overloaded servers. Dynamic management for data systems and real-time monitoring of changes in information flow and information processing capabilities facilitates the design of a more efficient and timely feedback system. Therefore, it is meaningful and necessary to observe the revision of contingency plans to improve resilience from a dynamic management perspective. The statistics on the use of the digital system by 100 randomly interviewed people of each gender, shown in Table 1, revealed that the vast majority of the interviewed people did not use the digital system, which proves that there is a long way to go for further popularisation of the digital system.

Local governments are the main administrators of a region and the main responders to disasters, so they have an unshirkable responsibility for the prediction and prevention of local disasters. In modern times, with the rapid development of digital, information technology, productivity and technology level further improved, disaster model prediction can be carried out with the help of computers, mobile platforms and other equipment, compared with the traditional prediction and management mode, digital management and prediction makes it possible to simulate the calculation of faster and more accurate, can be analysed through the analysis of nearly a hundred years of historical data, combined with the natural disasters in other parts of the world to establish a more perfect natural disaster risk model, avoiding the subjectivity of human prediction, and avoiding the subjectivity of human prediction. The natural disaster risk model can be improved by analysing nearly 100 years of historical data and combining it with natural disasters in other parts of the world, avoiding the subjectivity of human prediction. Local governments can use the digitally integrated data to guide their current disaster prevention efforts, and through big data analysis, they can develop locally specific and comprehensive disaster warning programmes. Figure 1 shows the statistical analyses of 500 randomly interviewed people on the need for a data system for local government disaster prevention policies. From Figure 1, we know that most people are eager to know the local government's laws and regulations on disaster prevention and want to participate in relevant training, which further illustrates the practical significance and application value of this work.

Figure 1.

Proportion of needs analysed by datamodelling system

Therefore, this paper is based on the digital system model of the local government in the past 10 years for disaster policy as the object of research, using a combination of quantitative and qualitative, risk model and disaster prediction model, through the data analysis and integration of overall analysis of the performance characteristics of the disaster management policy and the relevant preventive measures, to analyse and predict the tendency of the local government to pin the disaster prevention policy. Through digital analysis and statistics, the frequency of related disasters and the frequency of local government policies are analysed, the inevitable connection between the two is analysed, and the policy text is classified by the level of disaster prevention, from which the logic of disaster prevention and relief is analysed and the government experience is analysed, in order to improve the laws and regulations related to disaster prevention and relief and to provide relevant suggestions.

Local disaster governance policy is an important embodiment of the institutionalisation and standardisation of disaster governance, and with the development of economy, science and technology and the times, its form, connotation and function are gradually improved. [8] At present, not only the national level for disaster management policy, local governments based on the relevant regulations by the development of these laws and regulations and related regulations both for the enactment of different types of disaster laws and regulations, as well as These laws and regulations include both laws and regulations for different types of disasters, as well as a system of disaster policies and regulations for specific issues such as planning, emergency response, relief, supervision, insurance, information disclosure, and international co-operation in disaster management. Because local policies must be consistent with national regulations, but can be combined with local conditions to develop relevant laws and regulations, so in the selection of local policy texts, this paper first identifies the relevant laws and regulations at the national level, such as this paper's main websites and databases for the Ministry of Justice of the People's Republic of China Laws and Regulations Database, the central government network of the State Council Policy Documents Library, the Ministry of Emergency Management website, the China Policy Network, the National Reduction of Disaster Risk, and the National Disaster Reduction and Management Agency website, China Policy Network, National Disaster Reduction Network and other official institutions and departmental websites, Knowledge Network, Lawstar China Legal Search System, Wanfang Legal Database and other legal databases, with reference to the national Compilation of Disaster Management Laws and Regulations, Disaster Management Documents and Regulations, Compilation of National Public Emergency Preparedness Plans and Related Laws and Regulations-Natural Disasters, Compilation of Laws and Policies on Popularisation of Science and Technology in China, and other books. Compilation of Laws, Regulations and Policies on Science and Technology in China, etc. As for local policies, we choose to start from local government websites, government reports, natural disaster news, and local government regulations as policy texts. [9] Since natural disaster governance is a complex systematic project that requires multi-sectoral joint law enforcement and involves both governance of different types of disasters and governance norms of different sectors, levels, regions, phases, and segments, sometimes the data that are too old need to rely on consulting relevant professionals. As shown in Table 2, we list the approximate sources of data used in this model and the corresponding number of data. Therefore, there are so many relevant policy texts that it is difficult to accurately analyse and count local government texts in a short period of time without the help of relevant mobile devices and data tools. Therefore, this paper mainly selects the comprehensive disaster governance policy texts that can reflect the overall and holistic nature of disaster governance at the local government level, and in order to ensure the authoritativeness and universal applicability of the policy texts, the relevant data are called from the relevant government websites.

2.1

Underlying model logic

Standard stochastic planning methods usually do not provide control mechanisms for unfavourable outcomes, and solutions to these problems are based on the assumption that the decision maker is risk-neutral. For the prediction and treatment of such stochastic risks we need to resort to cloud computing systems for risk control, while the virtualisation of cloud computing has become an unprecedented trend. It paves the way for optimal utilisation of computing and storage resources. The process of data-based analysis of local government policies can be divided into 3 stages: type of disaster screening, class classification statistics, and pre-disaster prediction, as shown in Figure 2. [10] The matching of local government policies with disaster types and the precise deployment of countermeasures can be achieved through the rational analysis of data, which needs to rely on the Internet of Things (IoT) and big data technology to connect personnel data, natural data, economic data, policy data, and management data, and establish data-based visualisation. It needs to rely on IoT technology to connect personnel data, natural data, economic data, policy data, and management data, and establish a data-based visualisation of the government's response to disaster policy data-based correspondence model, so as to accurately predict the goal of macro-control of the government's policy. [11]

Figure 2.

Patterns of local government policy responses to disasters

2.3

Classification model

The class classification of the disaster needs to be analysed according to the specific situation, if there is a government report to the government report as the exact value, if there is no need to be based on the scope of the disaster, the level of harm caused by the combination of factors such as the examination of the basic logical structure of the classification model is shown in Figure 3. Therefore, we established the following screening model based on the above logical structure: (6)E=Et+εcPc+εdPd (7)Em≤E≤Ex (8)0≤Pc≤ZcPc, max (9)0≤Pd≤ZdPd,max (10)Zc+Zd≤1 (11)Zd,Zc∈{0,1}

Figure 3.

Machine learning logic

Where E represents the level of the disaster, Et represents the level attributes of the disaster itself, the inherent level of different disasters is different, but also has nothing to do with the losses it caused, Pc, Pd represents the economic losses and population losses at that time, and ε_c and ε_d represents the proportion of weights occupied by the different losses, respectively; and E_m and E_x represents the upper and lower limits of the screening of the disaster level, Z_c and Z_d represents the economic and demographic in the end of the disaster in the end of the statistics of the state of the error factor, with P_c,max and P_d,max, represents the maximum limit of economic and demographic losses acceptable to the local government.

The above model takes into account the special case of large economic and population losses, in which the local government's functions are completely ineffective and the state intervenes to deal with the disaster. In order to avoid this situation we have established boundary conditions so that the theoretical maximum risk remains within the control of the local government.

Boundary constraints

In order to further optimise the model and make it closer to the real situation, we have designed the following boundary constraints based on the real conditions. (12)Emin≤E≤Emax (13)0≤Xi≤Xi,max (14)0≤Xj≤Xj,max

X_i,max and X_j,max is the maximum computation of parameters i and j in the normal course of operation under the control of local government policies, and E_min and E_max is the maximum risk level in the normal course of operation under the control of local government policies.

From Figure 4, we can know that in 10 years, the number of man-made disasters in M city is the most, while the number of natural disasters is the least, but also the most stable, with an average of 2-3 times per year, and the trend of unknown disasters is close to the trend of man-made disasters, which is increasing first and decreasing, which suggests that there is a certain connection between the two and they may be accompanied by the relationship. Through careful enquiry of relevant information, we found that most of the unknown disasters are man-made disasters, or secondary disasters associated with man-made disasters, which is consistent with our statistical results. When the time exceeds 7 years, we find that the number of man-made disasters and unknown disasters decreases, which may be due to the control of the local government policy, the strengthening of law enforcement to avoid the further occurrence of disasters, which is also consistent with the rapid increase in related policies in Figure 5 in the period of 6-7 years, and for natural disasters, because it is unavoidable, the vast majority of natural disasters can not be artificially prevented, and can only be achieved through legislation to reduce the damage caused by hazards. The only way to reduce the damage caused by the hazard is through legislation. This also shows that man-made disasters are still the main cause of all disasters, and that the nature of human society is still a human society, and the purpose of governance, education, and legislation is for the reasonable and legitimate rights and interests of the majority of people. Governance, education, and legislation are aimed at the reasonable and legitimate rights and interests of the majority of the people. Local government policies should also be formulated with the governance of people as the main focus.

Figure 4.

Occurrence of different disasters in the last 10 years

Figure 5.

Trends in the number of policies formulated by the local government in the past 10 years

From Figure 5, we can know that in the past 10 years, the local government of M city has formulated the most laws and regulations for natural disasters, followed by man-made disasters, and finally other disasters. Among them, the natural disasters related laws and regulations developed by the growth rate is relatively average, which indicates that the number of natural disasters in the past 10 years has not increased significantly, the average annual increase of 1-2 related laws and regulations. Considering the unavoidable nature of natural disasters, it is only possible to avoid greater losses caused by natural disasters through the enactment of relevant laws and regulations, which is also consistent with the results in Figure 4, proving our prediction of natural disaster policymaking and illustrating the accuracy of the data system analysis model. The formulation of policies related to man-made and unknown disasters increases slowly but rapidly in the 6th-7th year, and then slows down in the 8th year, which may be due to the lack of understanding of man-made and unknown disasters in the early period, the number of legislation is relatively small, leading to a rapid increase in the number of disasters, and the formulation of laws and regulations has a lag, and by the time in the 6th-7th year, the relevant laws and regulations are formulated in mature By the 6th-7th year, the relevant laws and regulations have matured, resulting in a rapid decrease in the number of man-made and unknown disasters, which leads to a decrease in the rate of legislation, which is consistent with the change in the number of man-made and unknown disasters in Figure 4. Changes in local government policies can indicate changes in the focus of the government's work, in response to the government's lack of attention to man-made and unknown disasters in the previous period, leading to a rapid increase in the number of disasters, while the prevention and control of natural disasters has always been the focus of the local government. This is also due to the nature of natural disasters.

In order to further consider the relationship between the occurrence of disasters and the number of policies formulated by the local government, we introduce an unknown number k, which represents the number of relevant policies corresponding to each disaster, and theoretically, a larger value means that the local government pays more attention to the disaster. (19)k= The number of policies of the year The number of disasters

k1,k2,k3 denote the ratio with natural disaster policy, man-made disaster policy, and no explicit disaster policy respectively.

As shown in Figure 6, we can find that the natural disaster has the largest k value, and the distribution is uneven, generally high and low staggered rows, which indicates that the natural disaster has been by the local government's attention, but due to the occurrence of natural disasters is not controllable, resulting in government regulations can only be summed up the experience of the disaster after the occurrence of the disaster, the formulation of new laws and regulations, which is to present in the value of k value after the very small value of k value of a rapid increase, and show the trend of instability and uncertainty. On the contrary, the rapid increase of k value in the 6th-7th year of man-made and unknown disasters may be due to the fact that the government has not introduced perfect laws and regulations in the early period, the government does not understand the man-made hazards and unknown disasters, and the number of legislation is relatively small, which leads to a rapid increase in the number of disasters, which also reflects the lag in the formulation of laws and regulations.

Figure 6.

Trend of k-value in the last 10 years

From Figure 7 we succeeded in the consistency of the local government's policy making in the coming years through the above data system with the machine learning model. In the first year, the error between expectation and actual is only 1, and in the second year, it can be accurately predicted due to the machine learning model's characteristics of continuous optimisation and self-updating. The successful establishment of the machine learning model provides suggestions for accurately judging the local government policy adjustments in the future, and at the same time, machine learning can improve the applicability and calculation speed of the machine model through self-optimisation and through the continuity of a large amount of real data.

Figure 7.

Predicted 3-year results vs. actual policy increases

Finally, we randomly surveyed 1,000 passers-by about their satisfaction with the data system's prediction experiment and found that nearly 90% of the interviewees were very satisfied with the data system's data integration and analysis of the government's disaster preparedness, which further illustrates the practical value and significance of our model and methodology to the general public, and at the same time contributes to the further promotion of a digital society. The low level of dissatisfaction with the use of the data system is due to the fact that the interviewees have not yet gained a deeper understanding of the practical implications of the model. In addition, the model is in its infancy and needs to be further optimised and adapted.

Figure 8.

Satisfaction survey on the use of data systems

This paper takes the background of informatisation and data in the data era, the customisation of the local government's disaster prevention policy and related measures as the research object, and uses the data system to carry out modelling analysis by collecting relevant government reports, data, laws and regulations, and other relevant information. In the modelling stage, economic and demographic factors are added, taking into account the characteristics of natural disasters and other disasters. Certain stochastic risk variables are also considered, and the use of stochastic programming models allows supply chain decisions to be evaluated in a space of uncertain parameters before final decisions are made. Next, boundary conditions are also introduced to ensure that the research object is reasonable and to avoid excessive errors that lead to detachment from reality. Meanwhile, the random consecutive 10-year data were used as an example to compare the number of different disasters, the number of related laws and regulations enacted, and the intrinsic connection between the number of laws and the number of disasters over the 10-year period. As for natural disasters, because they are unavoidable, the vast majority of natural disasters cannot be stopped by human beings, but only through legislation to reduce the losses caused by the hazards. The number of policies related to man-made and unknown disasters increases slowly in the first year, but increases rapidly in the 6th-7th year, and then slows down in the 8th year, which may be due to the lack of understanding of man-made and unknown disasters in the early period, the number of legislations is relatively small, which leads to a rapid increase in the number of disasters, and the enactment of laws and regulations has a lagging effect. In addition, the statistical system is versatile and can be applied to any large-scale statistical analysis problem, thus reducing the difficulty of calculating the actual large-scale problem. Meanwhile, risk management should be comprehensively carried out for local governments in policy formulation, and the basic concept of disaster risk management is to incorporate trade-offs between risk and performance measures in the decision-making process. Our proposed planning method can classify and count a large amount of complex and messy data through a data analysis system and predict the future trend according to the corresponding machine learning model. Therefore, this data analysis system model is better than the traditional data analysis method in terms of applicability, convenience, accuracy and other indicators, which is conducive to improving the efficiency of the government's work and the protection of people's lives and health safety. [14]